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Effect Financial Technology Credit, Credit Sharing and Bank
Regulation on the Performance of Microfinance Institutions in
Kisumu City
Agom David Otieno., Dr. Peter Ndichu., Mark Opondo
Department of Accounting and Finance Maseno University, Kenya
DOI: https://doi.org/10.51244/IJRSI.2025.120800218
Received: 18 Aug 2025; Accepted: 25 Aug 2025; Published: 23 September 2025
ABSTRACT
In Kisumu City, Kenya, microfinance institutions (MFIs) are key in the provision of financial services to low-
income earners. Notwithstanding the evolution of financial technologies (Fintech), with its potential to provide
innovative solutions that can improve financial inclusion, there are a number of obstacles to fully exploit these
advancements. The practical problem which this study seeks to address is the knowledge gap as to how Fintech
credit and credit sharing, and banking regulations influence the performance of MFIs in Kisumu City. The
critical knowledge void is an understanding of how Fintech credit, credit sharing and banking regulations
specifically influence the financial performance of MFIs. The aim is to develop a robust understanding of how
each factor affects the financial health of MFIs so as to contribute to data-based improvement and targeted
interventions. The study is based on financial intermediary theory and asymmetric information theory that have
theoretical implications for understanding how financial technologies and regulatory frameworks may
influence financial performance and the management of risk. Correlational survey as a research design is used
to obtain data from 60 respondents, branch managers, credit officer and operations manager working in all the
twelve MFIs in Kisumu City. The survey instruments were tested for reliability using the Cronbach's alpha
coefficient (total scale, 0.916), verifying consistency of the data. Descriptive and inferential statistics were
utilized to analyze the data. The findings reveal that Fintech credit is a significant driver of improved financial
performance among MFIs, indicating its crucial role in enhancing service delivery and operational efficiency.
Credit sharing and banking regulations also positively affect financial performance, though their impact is less
pronounced compared to Fintech credit. In conclusion, while Fintech credit significantly enhances MFI
performance, credit sharing and regulatory compliance also contribute positively but to a lesser extent. The
study underscores the need for further research and the implementation of data-driven strategies and supportive
regulatory frameworks to fully harness the benefits of Fintech innovations for the economically vulnerable
populations in Kisumu City.
INTRODUCTION
Background of the study
Microfinance institutions (MFIs) are entities that offer a range of financial services, including loans, savings
accounts, insurance, and money transfers, primarily to individuals and small businesses that generally do not
have access to traditional banking options. They play a significant role in helping communities improve their
financial well-being and are especially important in efforts to reduce poverty and promote financial inclusion
across the globe. (Omondi, 2018) Microfinance institutions follow an eligibility criterion for the customers,
such as an age limit (for Kenya the limit is at least 18 years), ability to pay the loan (this may be determined by
a person’s income or savings), or gender since some MFIs maybe restricted to one gender an example being
the Kenya women microfinance bank, as well as other requirements which may vary according to the region.
Microfinance institutions started gaining ground across the globe as a way to help the poor and marginalized
populations. The idea gained significant momentum following the establishment of the Grameen Bank in
Bangladesh by Nobel Peace Prize laureate Muhammad Yunus who showed just how impactful small loans
could be. Over time, this approach spread far and wide, with microfinance becoming a key part of financial
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
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systems in both wealthy and poorer nations. Back in 2016, India led the way, with around 47 million people
borrowing a total of roughly 15 billion US dollars through microfinance programs.
In Africa, where a large proportion of the population lacks access to formal financial services, microfinance
institutions have become crucial drivers of economic development. Many African countries have seen the
emergence of MFIs that cater for the financial needs of the underserved population, empowering them with
opportunities to engage in income generating activities and improve their livelihoods, or rather to cater for
their unsatisfied need for financial services (Etzensperger, 2013).
In Kenya, microfinance institutions have been instrumental in extending financial services to people who
traditionally lacked access to banks. Over the years, the sector has seen major changes, especially with the rise
of financial technology (Fintech). These innovations have made it easier for MFIs to reach more people, even
in remote or previously underserved parts of the country. The micro-finance act became active in the year 2008
and the growth of MFI’s started escalating. By the year 2010 we had over 1.5 billion active customers of MFIs
in Kenya being served approximately USD 1.5 billion in loans by the over 20 micro-finance institutions that
had already grown in the country. (Guti´errez-Nieto, 2009) Different MFIs kept growing in the country with
equity bank Kenya being the leading with share of loans, accounting for 73.5% of the market, translating to
over 100,000 clients. Followed by Kenya women microfinance bank taking 12.06% of the market.
Kisumu, positioned in western Kenya, lies along a bay on the eastern edge of Lake Victoria at an elevation of
1,131 meters (3,711 feet). It serves as the administrative center of Kisumu County and ranks as the second-
largest city in the Lake Victoria basin, following Kampala.
Kisumu is renowned for its vibrant economy which is mostly driven by small businesses and the informal
sector. Microfinance institutions have plays a key role in empowering local entrepreneurs by offering them
access to credit which has helped spur economic growth and reduce poverty in the region. Today, more than
twelve (12) officially licensed MFIs operate in Kisumu City. These include, but are not limited to, Momentum
Kenya, ECLOF Kenya, Musoni Microfinance, Faulu, Kenya Women Microfinance Bank, SMEP, Rafiki,
Uwezo, Vision Fund, Sumac, Rafode, Letshego, and Caritas (CBK, 2008).
Over many years, microfinance institutions have grown and adapted to better meet the financial needs of
underserved communities, offering a variety of services beyond just small loans. But with the rapid rise of
financial technology, the industry has seen major shifts. New, tech-driven credit products and innovative ways
of sharing credit have started to reshape how financial services are delivered and accessed. The Central Bank
of Kenya (CBK) Innovation Survey 2022 revealed that out of the 14 microfinance banks surveyed, 93%
identified credit, deposit, and capital-raising services as key areas for innovation in their short to medium term
strategies. Additionally, 96% of these institutions had implemented mobile banking solutions to support
banking operations and enhance customer relationship services.
KPMG (2017), provides a definition of fintech companies as those that leverage technology to their advantage,
causing significant disruptions within the financial services industry. They stand out from rivals by their
single-minded pursuit of excellence in service quality for customers. Moreover, they employ digital technology
in every conceivable area of financial life with great emphasis on education and entertainment apps. The use of
fintech has lowered intermediation costs for these firms and increased financial access. This has encouraged
financial inclusion. The power of fintech lies in that it can attack the traditional bank's Achilles heel of
information gaps that have plagued these institutions for many years. What ‘s more, fintech companies are not
shackled by Old systems. This gives them greater freedom to innovate and adapt quickly compared with their
counterparts in the financial services industry.
With peer-to-peer (P2P) lending platforms, individuals and businesses can access loans without having to go
through traditional banks. By making themselves the middle men between borrowers and lenders, these
platforms give users a larger say in matters and some are even able to let lenders pick who they lend money to
or select certain types of loans. In some cases, peer-to-peer lending is conducted through online auctions,
adding an extra degree of flexibility to the process and making it smoother (Financial Stability Board, 2017).
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In Kenya, the fintech space has seen remarkable growth, with numerous companies entering the market and
offering diverse financial services that continue to attract a growing number of users. As reported by the
Communications Authority of Kenya (2018), a number of firms including Telkom Kenya Ltd, Mobile Pay Ltd,
Airtel Networks Ltd, Finserve Africa Ltd, Safaricom Plc and Sema Mobile Services were officially registered
to deliver mobile money services.
Additionally, there are companies like Cellulant, Jambopay and Pesapal which are not formally regulated but
provides a widely used payment solutions. On the lending side, firms such as Branch, Tala, Zenka and
Micromobile mainly target individual borrowers through mobile platforms, while others like Musoni, Saidia,
and Umati Capital focuses more on financing needs for businesses. There are also companies specializing in
P2P lending services, such as Odyssey Capital and PesaZetu. According to (Ngumo, 2020), Fintech credits
often use non-traditional data sources and innovative algorithms to assess creditworthiness, extending credit to
previously underserved populations.
Tchamyou and Asongu (2017) define credit data sharing as the routine exchange of information concerning the
creditworthiness of borrowers. Bos, De Haas, and Millone (2016) explain credit information sharing as the
exchange of details about a borrower's financial history and behavior that is an essential factor in the effective
functioning of credit markets.
According to Tchamyou (2019), lending data movements between financial institutions are designed to shield
creditors from the risks of over-lending large sums. It also protects lenders against potential defaults.
Supporting a lender to share information about a borrower’s credit history means that it will be much easier to
collect any defaulted loans. This benefit gives banks more freedom to follow the credit background of all
applicants in the past, reducing the cost of gathering credit details directly from borrowers and even helps
make borrowers more aware of their borrowing habits. Moreover, it serves as a deterrent against borrowing
excessively from multiple institutions (Tchamyou & Asongu, 2017). Yet, Tchamyou (2019) has pointed out
that the subject of credit information sharing is fraught with various theoretical issues. For one thing, he
observed that only local credit information is available, making it difficult to reduce the default rates among
non-local borrowers.
According to the evidence from his research, on the other hand, despite limiting loans to those deemed risky
borrowers taking steps for promoting responsible lending might not produce a total return equal to what is lost.
What 's more, at some stage sharing credit information may damage the trust between banks and customers
that often has been built up over years. It also gives banks superior information that could change the power
balance, perhaps leading at some future date to tension or even dislocation in the way they lend money.
Credit information sharing is a gateway for those who, to use one expression, once had "No record of a past
period limit." This is particularly invaluable to people in the country on the fringe (Cull, Demirgüç-Kunt, &
Morduch, 2009). Regulation of banking influences how microlenders (MFIs) perform and what they are able
to do. These guidelines are meant to ensure financial stability, protect consumers and promote fair and
responsible lending practices across the sector. However, their impact on MFIs isn’t always clear-cut.
Depending on the regulatory framework, how effectively an institution is managed and the broader economic
conditions, regulations can have both positive and negative effects. While well-crafted rules can strengthen
stability and safeguard borrowers, they may also pose operational challenges that MFIs need to carefully
manage in order to maintain solid financial performance (Hermes & Lensink, 2011).
In addition to the specific regulations that pertain to their sector, Fintech companies are also obliged to ensure
compliance with broader areas of law. Some of these include:
Data Protection and Privacy: Every individual's right to privacy, encompassing the protection of personal and
private information, is safeguarded under Article 31 of the 2010 Kenyan Constitution. The Data Protection Act
(DPA) of 2019 was introduced to enforce the constitutional right to privacy by regulating how personal data is
handled. It clearly states the rights of individuals (data subjects) and the responsibilities of those who collect
and manage data, known as data controllers and processors. This law holds particular importance for fintech
companies, as their operations often depend on gathering and handling sensitive customer information, like
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Know Your Customer (KYC) details and records of financial transactions. As a matter of fact, under the E-
commerce Law of Thailand, the definition of personal data is loosely based on a catch-all phrase. It's intended
to cover literally any information relating directly or indirectly to an identifiable individual. These regulations
are designed to guarantee that all personal data is processed and utilized in compliance with the law, both
diligently and responsibly. For example, the Consumer Protection Act of 2012 is the principal legislation in
place today to defend consumer rights and curb practices by businesses which are unfair. It should be noted
that many industry specific regulations also imply requirements on protection for consumers.
Accordingly, the CBK (Digital Credit Providers) Regulations 2022 contain a section on consumer protection.
Such digital credit providers are required under these rules to issue a transaction receipt, establish customer
grievances handling system, maintain enterprise continuity and comply with such definitions for limits on
access and gathering information on customers as are laid down by the regulations. They must also provide
their terms and conditions in the form prescribed by the regulations themselves, not to engage in any false
advertising and obtain written prior consent from the Central Bank of Kenya (CBK) before bigger credit terms
are changed. And also the National Payment System Regulations 2014 lay down specific regulations on
consumer protection. They deal with disclosure, customer service, dealing with complaints, service agreements
and the confidentiality of information received from members of public who hold accounts in a participant
bank's data networks system.
The Money Remittance Regulations also address consumer protection. Anti-Money Laundering (AML)
obligation falls under the jurisdiction of the Proceeds of Crime and Anti-Money Laundering Act (POCAMLA)
2009. According to Section 2, "Financial institution" is anything or entity that engages in providing financial
services, which would include processing payments using instruments such as credit or debit cards, cheques,
money orders or electronic transmissions. Such institutions are legally obliged to be "reporting entities" and
also to register with the Financial Reporting Bureau (FRC). In addition, they must set up systems to detect and
prevent money laundering.
As detailed in PART IV of the Act, these duties include verifying the identities of their customers, keeping
comprehensive records, establishing internal reporting systems and furnishing reports annually to FRC.
Compliance with these legal requirements is indispensable for Fintech companies operating in Kenya. Kenya's
Fintech ecosystem is characterized both by its dynamism and diversity. It cuts across many industries and
hence involves multiple regulatory complexities. So a Fintech company may well find itself in a position
where, for the various services it provides, it has to seek multiple permits. For example, a Fintech firm that was
operating a mobile money product would have to obtain licenses both from the Central Bank of Kenya and
from Communications Authority Kenya. Moreover, it must comply with laws governing data protection,
consumer protection and antimony laundering.
According to Al-Matari, Al-Swidi, and Fadzil (2014), financial performance is defined as a company's ability
to meet specific financial objectives, especially in terms of profitability. It acts as an indicator of how
effectively an organization achieves or exceeds its financial aims. Baba and Nasieku (2016) emphasize that
financial performance indicates how efficiently a firm utilizes its assets to generate revenue, providing
essential insights for stakeholders. Nzuve (2016) points out that the stability of the banking sector is largely
influenced by the performance of individual banks, as it reflects their operational strengths and weaknesses.
Additionally, both governmental and regulatory entities closely monitor financial performance for oversight.
The analysis of financial performance investigates the elements that directly affect a company's financial
statements and reports (Omondi & Muturi, 2013). This analysis is the primary method for external
stakeholders to evaluate a firm's performance (Demertzis, M., Merler, S., and Wolff, G., 2017), which is why it
is frequently utilized as a benchmark. A firm's performance is determined by how well it meets its internal and
external goals (Lin, 2008). The concept of performance is linked to various aspects, including growth,
competitiveness, and survival (Nyamita, 2014).
Financial performance can be measured through several ratios, such as Return on Assets (ROA) and Net
Interest Margin (NIM). ROA evaluates a bank's effectiveness in utilizing its assets to generate profits (Hoenig
& Morris, 2012). This ratio is calculated by dividing operating profit by total assets, indicating earnings
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derived from all of the company's financial resources. In contrast, NIM measures the difference between the
interest paid to the bank's creditors (liabilities) and the interest income earned by the bank relative to its assets.
The NIM is expressed as net interest income divided by total earning assets (Gul et al., 2011).
Fintech companies play a crucial role in improving financial performance by making use of digital financial
platforms. By harnessing advanced technologies, these firms are able to compete with traditional banks and act
as important players in delivering financial services more efficiently and accessibly. The Kenyan market
possesses all the conducive conditions for the growth of fintech companies and the advancement of financial
systems (Klingebiel, 2000). Fintech companies operate efficiently and maintain a competitive advantage, as
they are subject to fewer regulatory constraints compared to traditional banks, which translates into an
enhancement of the financial performance within the banking sector.
Mutua (2013) states rapid technological progress in the payments space has greatly enhanced financial
inclusion and transforms how traditional banking systems operate. Although these advances have been made,
most of Kenya's financial market are still unpenetrated territory, and there is plenty of room for fintech
companies to enter the crowd. However, the combined tide of globalization, changing customer needs, and
severe competition in the financial sector altogether serve to make the struggle for market shares even more
brutal.
The purpose of this study is to deeply investigate the impact of fintech credit, credit information sharing and
banking regulation on the financial performance of MFIs in Kisumu. By combining the best bits of both
contemporary and traditional financial practice, the study hopes to cast light not only on opportunities but also
on challenges arising in these changing environments. It should therefore provide a useful, evidence-based
guidebook for policymakers, microfinance managers and other stakeholders to make sensible and practical
decisions. It also examines how regulatory frameworks shape the relationship between fintech and
microfinance as highlighted by Adams (2020). A deeper understanding of Kisumu’s regulatory environment
will help uncover the key factors that either support or hinder fintech adoption, contributing to the
development of a more enabling and innovation-friendly regulatory framework.
To stay competitive and boost their financial performance, Kenyan banks now find it useful to partner with
fintech firms. There are a number of studies that have been done on digital credit and Fintech and its impact on
financial inclusions. A related study by Wambua (2022) concentrated on youth in Nairobi County, which
highlights a gap in the literaturesince the use of fintech credit and credit information sharing among MFIs
isn't limited to any one region. In fact, Doe et al. (2022) underscore the increasing importance of fintech in
Kenya’s financial landscape, pointing out its potential to extend financial services to populations that have
historically been underserved. As the saying goes, you can have too much of a good thing. The impact of the
increased use of Fintech credit on lending portfolios, risk management and competitive advantage in Kisumu is
an area crying out for research. Ochieng et al. (2021) show how Credit Information Sharing has evolved in
Kenya. But there is still a need to look at present practices, what effect sharing credit has on microfinance
institutions in Kisumu as well as regulatory implications and yesterday’s technology in today's digital, data-
driven era. Good banking regulation and supervision particularly for the micro-finance sector ensure financial
stability and protect consumers.
With the rapid development of digital financial services, there have been many changes in policy and
regulation in Kenya. Reports from the Central Bank of Kenya (CBK) tell us that regulators are increasingly
recognizing the need for a regulatory system that is both flexible and adaptable in the face of these changes.
However, evaluating the adequacy or effectiveness of these regulatory measures and the impact on financial
performance of MFI's in Kisumu is an important challenge and an area for future research.
To date, very little research has been done regarding Financial Technology, and the cumulative impact of
Financial Technology together with Credit Sharing and Bank Regulation on the financial performance of
microfinance institutions in Kenya, particularly in Kisumu remains unexplored. This study therefore aims to
give a comprehensive analysis of the impact of financial technology, credit sharing and banking regulation on
microfinance institutions' financial performance in Kisumu, and to propose effective measures for removing
this stifled state. Recognizing a complex interplay between those many factors is essential in working out
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appropriate strategies for the sustainable development of microfinance institutions and higher quality financial
services to underprivileged populations throughout the district of our study.
This study explored the intricacies of Financial Technology (Fintech), credit sharing practices, bank
regulations and their synergistic effect form together on the performance of Micro-Finance Institutions (MFIs)
located in Kisumu City. In conclusion, this research aimed to fill a critical gap in the understanding of how
Financial Technology, Credit Sharing and Bank Regulations affect Financial Performance in Kisumu City's
microfinance institutions.
Statement of the problem
In Kisumu, an important town in Kenya, there are a number of microfinance institutions (MFIs). They play an
instrumental role in the aim to achieve financial integration across the region and promote development.
However, no research has yet been done on the impact of this financial dynamism on performance from
microfinance institutions in Kisumu City. This includes the general increase of Financial Technology
(FinTech), credit archives measures for loan repayment and fulfilment, and conforming with banking
regulations. To overcome these challenges, further research and targeted interventions are needed in order to
gauge the impact of FinTech, credit archives and banking regulations on both the performance of microfinance
institutions as well as financial integration in Kisumu City. There was a need for data-driven tactics and
supportive regulatory environments that enhanced the benefits of Fintech innovations for the poor and
vulnerable. As microfinance institutions increasingly opt for FinTech services to improve efficiency by
offering more services, it is necessary to study exactly which FinTech tools and strategies they are employing,
and the effect of such practices on the public obligations, working efficiency and accessibility of their services.
Implementing sharing of credit information is thought necessary as one of the main bases for the evaluation
their credit standing of borrowing individuals. Many of these sharing platforms have myriad consequences for
MFI risk management policies, loan quality and overall financial solvency of MFIs. This analysis investigates
how banking regulations affect the operational conditions of MFIs. By assessing the impact of banking
regulations on the legality structure, expenses needed in complying with them and terms of overall
performance for MFI we will be better positioned to understand regulatory systems dynamics as well as their
implications into microfinance realms. Keeping an eye on the three-way relationship between the deployment
of FinTech, credit information sharing practices and banking rules gave a comprehensive picture of the great
variety of trials and opportunities that microfinance face in today's financial milieu. As this work addresses
these aspects in a comprehensive manner, it provides a unique resource for policy makers, regulators,
practitioners and researchers. It can help them make informed decisions and plan strategically in a landscape
where the terrain is microfinance sector. Despite the transformative potential of Fintech and the adoption of
innovative financial services, there remains a significant gap in fully realizing the benefits for the economically
vulnerable population in Kisumu City, Kenya.
Objectives of the study
The main objective of this study is to analyze the effects of financial technology, credit sharing and banking
regulations on financial performance of MFI’s in Kisumu City.
Specific Objectives
The specific objectives are;
1. To evaluate the influence of Fintech credits on the financial performance of microfinance institutions in
Kisumu City.
2. To analyze the impact of credit sharing on the financial performance of microfinance institutions in Kisumu
City.
3. To examine the correlation between banking regulatory compliance and the financial performance of
microfinance institutions in Kisumu City.
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Test Hypothesis
The study seeks to test the following hypotheses;
𝐻
𝑂1
: The adoption of Fintech credit has no significant effect on the financial performance of microfinance
institutions in Kisumu.
𝐻
𝑂2
: The adoption of credit information sharing has no significant effect on the financial performance of
microfinance institutions in Kisumu.
𝐻
𝑂3
: The banking regulatory compliance has no significant effect on the performance of microfinance
institutions in Kisumu.
Scope of the study.
In this study, we focus on a detailed analysis of financial technology adoption, credit information sharing and
banking regulations and their impacts on the microfinance sector in Kisumu City, Kenya. This study attempts
to ascertain whether there is a statistically significant connection between the adoption of Fintech, sharing
credit information, banking regulations and how microfinance institutions fare financially. The independent
variable in our study is divided between fintech, credit information sharing and banking regulations; while the
dependent variable for this study is the financial performance of microfinance institutions Research is
conducted to permit us to have an in-depth and thorough understanding of Fintech’s dynamics, its relationships
with credit information sharing practices and banking regulations, and how these factors impact on
microfinance institutions performance within the confines of Kisumu City. In the Kisumu's area there are
unique economic and financial conditions.Fintech adoption, credit sharing, banking regulation and
microfinance performance, in this context, make for a potentially fertile field of inquiry. The study will use
data covering a period of 3 years (2021 to 2023) to gather comprehensive data regarding the adoption of
fintech credits and credit sharing by microfinance institutions, as well as the subsequent effect on their
financial performance.
Justification of the study
We hope that this study provides practical advice to microfinance institutions that they can understand and
accept, which will help them further share information with their customers about all stages in the process.
This study can also serve as an essential resource for other researchers and scholars interested in similar
research areas. Additionally, people moving into academia and students can accept the findings of this study
into their own subject system as a foundation. The government will analyze its national goals and what steps to
take in order to achieve them in order to develop a national strategy that is neither exclusive nor limited. For
such goals are the economic future of all mankind and they belong equally to all people.
Conceptual framework
Drawing from the reviewed literature, a conceptual framework was developed as illustrated in Figure 1 below.
In this study, the independent variables include financial technology with indicators such as digital payment
systems, online lending platforms, Insurtech and Regitech. Another independent variable is credit information
sharing, represented by credit reporting agencies, credit scores/reports and credit information exchange
platforms. Also, Indicators like data privacy laws, consumer protection regulations and anti-money laundering
policies are used to assess banking regulation. The performance of microfinance institutions is the dependent
variable. It is measured by indicators like return on assets, liquidity levels and capital adequacy.
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Figure 1: Conceptual Framework
Source: Adopted from Betker (1997)
LITERATURE REVIEW
Theoretical framework
In this section, we discuss several theories under which this particular research is anchored. The theories help
to build this study as well as paint a practical picture of what the study entails. These theories include;
Financial intermediation theory
Franklin Allen and Anthony M. Santomero wrote the financial intermediation theories aiming at reducing
transaction costs and breaking information asymmetry. Banks and insurers themselves participate in all parts
of financial intermediary massages: first mobilizing deposits or issuing insurance policies and then using the
proceeds to finance business activities. Through this theory, we can see how intermediaries act in the economy.
It emphasizes their indispensable importance as intermediaries from lender to borrower, also bridging the gap
to ensure that capital is allocated efficiently and in addition oiling wheels for smooth financial markets.
Gurley and Shaw (1960) further argued that these intermediaries contribute significantly to liquidity provision
by converting short-term deposits into long-term credit. They also underscored that banks and similar
institutions are not merely conduits but are actively involved in the creation of credit and money.
According to (Berger & Molyneux, 2019), financial intermediaries act as a bridge between those who have
excess funds (savers or investors) and those who need funds (borrowers or entrepreneurs). They gather funds
from savers and then lend these funds to borrowers hence facilitating investments, economic growth and the
development of financial markets.
This theory serves as a fundamental concept in the field of economics and finance as it helps to explain the role
of financial intermediaries in financial operations. Financial intermediaries act as mediators between savers
and borrowers. They help in facilitating the flow of funds from those with excess funds (savers) to those who
need the funds(borrowers).
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On the transformation of risks, the key functions of financial intermediaries are to transform risks. They take
on the risk associated with lending to borrowers and diversify it across their portfolios, reducing risk exposure.
This risk transformation allows them to provide a relatively lower-risk investment option to savers (Boot,
2019).
The financial intermediation theory makes various assumptions/considerations which may not all be relevant
to our study. For that reason, we take into consideration two key parts of the theory that will help us build on
part of our study variables.
The theory assumes that financial operations are often characterized by information asymmetry. This means
that savers may need more complete information about potential borrowers, therefore making it more
expensive and time-consuming to assess the creditworthiness of the borrowers.
The theory also explains that financial intermediaries use their expertise to evaluate the creditworthiness of
borrowers, assess risk, and make investment decisions.
This theory helps us build on the credit information-sharing factor and the impact it brings on the financial
performance of MFIs. CIS reduces information asymmetry in the microfinance sector and, most importantly,
assesses the creditworthiness of borrowers, thus helping mitigate risk by reducing the number of loan default
cases.
Financial intermediaries have knowledge and skills that can help fix the problems that come up when
borrowers and lenders don't have the same information. They conduct due diligence, assess creditworthiness
and provide valuable information to lenders, reducing adverse selection and moral hazard issues (Houston &
Lin, 2019). Financial intermediaries also perform liquidity transformation by offering a range of deposit and
savings products to savers while providing loans with different maturities terms to borrowers. This change
helps both depositors and borrowers achieve an agreement on how much liquidity they want (Cai & Song,
2020). Financial Intermediation Theory keeps changing with changes in the structure of financial markets,
advances in technology and amendments to regulatory frameworks. Both researchers and policy makers are
busy exploring its applications and consequences in contemporary financial systems.
Asymmetric information theory
Asymmetric Information, the idea of how differences in information between buyer and sellers can lead to
market failure was first developed in George A. Akerlof’s The Market for Lemons: Quality Uncertainty and
the Market Mechanismpublished in 1970. This theory of economics explains situations where one participant
has greater or better information than the other, resulting in problems such as adverse selection and moral
hazard. Such anomalies may lead to decision making errors and undermine the efficiency of markets (Akerlof,
1970).
Adverse selection, for instance, happens when when one party in a transaction has better informations about
the qualities or characteristics of a product or service than the other party. This leads to a situation where
lower-quality products or riskier ventures are more likely to be selected (Akerlof, G. A.,1970). This theory was
developed to explain market failures (market inefficiency and adverse selection). An imbalance of information
between buyers and sellers can lead to market failure, which means that there might be quality uncertainty in
the market. It majors on situations where one party in a transaction has more information than the other party
and, therefore, affects their decision-making.
The theory identifies the challenges accompanied by determining the behaviors of borrowers in the financial
sector, leading to market failure. In the case of microfinance institutions, information asymmetry occurs when
the institution needs more background information about the borrower to determine their ability to repay the
loan. This leads to adverse selection since the institution has to make a decision based on the already
inadequate information they can access at the time. This is where the credit information-sharing bodies, such
as the CRB, come in to bridge the gap.
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Asymmetric information often leads to moral hazard problems, where one party takes risks because it doesn't
bear the full consequences of those risks. Recent research by Leland et al. (2021) examines moral hazard in
financial markets, emphasizing its role in the 2008 financial crisis. The moral hazard theory addresses how
people alter their behavior when they know that an insurer or other protective measures will mitigate the
consequences of their actions. This leads to increased risk-taking because of individuals’ irresponsibility. In
this case, if borrowers do not have adequate information about the consequences of loan defaulting, they may
decide to default on their loans deliberately. This eventually makes it difficult for the lenders to determine their
profitability due to loan default.
Credit information-sharing bodies step in to regulate this natural habit by humans to relent by setting policies
that make it less likely to default on loans. They rate the creditworthiness of borrowers based on their past
behaviors. This makes them only eligible for a certain amount of loan, which they are capable of paying.
CIS regulatory bodies are an important basis for our study since they seek to regulate the effects explained by
the moral hazard theory. This reduces bias in terms of risk-taking by the two parties: the lender and the
borrower. This could affect the overall performance of financial institutions.
Asymmetric information plays a significant role in credit markets and banking. Recent research by Spagnolo
(2019) investigates the impact of information asymmetry on bank lending and the potential consequences for
credit availability. Asymmetric Information Theory remains a fundamental concept in economics and finance,
and recent research continues to explore its implications in various sectors, from financial markets to
healthcare and Fintech. Understanding and addressing information disparities is essential for market efficiency
and effective policy design.
This theory has implications for the lending and credit business. It plays a vital role in the formation of
regulatory bodies and the formulation of policies that regulate financial transactions in an economy. Among
them are credit information sharing facilitators, activities targeting the information asymmetries that lenders
face with borrowers. This is the type of data that gives credit institutions the ability to make good, unbiased
decisions. Theoretical reviews in this study, we intend to examine the influence of sharing of credit
information on the performance of microfinance institutions.
Technology Acceptance Theory.
The Technology Acceptance Theory, introduced by Davis, Bagozzi and Warshaw in 1989, provides a
framework for understanding why individuals chooses to adopt or reject new technologies or information
systems. The theory revolves around two central beliefs: how beneficial a person thinks the technology will be
(perceived usefulness) how simple they find it to understand and apply (perceived ease of use). Perceived
usefulness refers to the belief or expectation that the technology will improve task performance while ease of
use refers to the degree of effort the individual anticipates needing to interact with the system (Baker et al.,
2015).
It also takes into account the role of external influences such as the user's environment or organizational
context, which can shape their perceptions and ultimately affect their willingness to adopt the technology.
Notably, the model suggests that when a system is easy to use, users are more likely to see it as useful,
reinforcing their intention to use its.
Over the years, researchers have adapted and extended TAM to fit a wide range of fields. For example, Liu and
Arnett (2000) applied the theory to identifies essential components of effective website design. Luarn and Lin
(2003) expanded the model by incorporating trust, offering a better understanding of consumer behavior in
digital transactions. Pavlou (2003) built a model tailored for e-commerce, using experimental data to test user
acceptance, while Horst, Kuttschreuter, and Gutteling (2007) investigated public readiness to use digital
government services in the Netherlands, concluding that trust in the system and user experience played a major
role in adoption.
Within the scope of this study, Technology Acceptance Theory serves as a major theoretical lens to examine
how the adoption of fintech affects the performance of listed banks in Kenya. The effectiveness of new
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banking technologies hinges not only on their availability but also on the willingness of users to adopt them.
As such, fintech innovations are more likely to succeed when users view them as both useful and easy to
engage with (Baker et al., 2015).
Concept of FinTEch, Credit sharing and Banking Regulations
Financial technology (Fintech), is the application of technical ideas to provide commercial organizations with
financial solutions (Arner, Barberis, & Buckley, 2015). Hwang and Tellez (2016) claim that the definition of
fintech is based on loan eligibility, which is made possible by a number of widely accessible digital platforms
that use digital data from their clients to assess a customer's eligibility. The creation of internet apps that let
customers execute loans using their phones can lead to an automated loan decision. According to Leeladhar
(2005), financial technology is the means by which banking services is delivered.
According to Coad and Rao (2008), fintech is growing quickly which benefits both the companies and their
clients. For the simple reason that the benefits outweigh the obstacles, this necessitates more integrated
approaches to addressing the challenges. More credit access channels have been made available to customers
by technology which has occasionally increased consumer debt (Ryan, Trumbull & Tufano, 2010). In 2017,
Demertzis, Merler and Wolff proposed that "customers have rapidly become accustomed to ordering and
paying for products with a touch of their finger wherever they may be getting personalized recommendations,
selecting products that suit their needs and having almost anything delivered straight to their front door."
Customers can now get financial services straight from their phones thanks to fintech. In addition, fintechs
have made significant strides in the areas of lending, financial advice, insurance and payment systems through
their innovative use of digital technology (Vives, 2017).
Fintech companies of all kinds are operating in the Kenyan market and are providing a wide range of services.
A report by the Communication Authority of Kenya (2018), lists the following registered companies provided
mobile money services; Mobile Pay Ltd., Safaricom Plc., Airtel Networks Ltd., Finserve Africa Ltd., Sema
Mobile Services and Telkom Kenya Ltd. Other businesses that process payments but aren't registered with
regulators include Pesapal, Jambopay, Cellulant, and others. While businesses receive loans from
organizations like Musoni, Saidia, and Umati Capital, individuals can obtain mobile financing services from
companies like Branch, Micromobile, Tala, and others. P2P financing services are provided by companies like
Odyssey Capital and PesaZetu (C A K, 2018)
Credit information sharing (CIS), is the process by which credit bureaus and financial institutions share data on
an applicant's credit status. Financial organizations that primarily collect data on borrowers may conduct
confidential credit information sharing based on the specifics sought. Information, however, is one option in
these situations as several institutions can decline to provide information due to client confidentiality.
However, sharing information among institutions becomes mandatory when mandated by the government
(Jappelli & Margo, 2005).
In Kenya, the exchange of credit information was gazetted in July 2008 and became operational in February
2009. The creation of the laws offers a means of controlling Credit Reference Bureaus (CRBs), which are
permitted to gather data and exchange it with organizations that are licensed under the Banking Act. Kenyans
now have easier access to affordable credit thanks to the sharing of credit information, which reduces
information asymmetry and, consequently, credit access costs. In the past, borrowers paid a risk premium to
cover the expense of information access (Central Bank of Kenya, 2013).
Information technology, which makes data transfer between the institutions easier is used to share credit
information. Effective July 2010, the Central Bank of Kenya mandated that all non-performing loans be turned
over to authorized CRBs at the end of each month, along with a monthly report that included additional data.
The CBK may take corrective measures if the listed institutions fail to furnish this information (CIS Kenya,
2018).
Credit reference bureaus (CRBs) only disseminated negative information about loans that were provided to
borrowers in the early years after the act was passed but as operations have developed, the organizations have
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begun to disseminate both positive and negative information. For as long as it takes to estimate a borrower's
creditworthiness, CRBs are allowed to keep both positive and negative information on file for five years. The
CBK has also observed in recent years that while CRBs have been quick to list people those with excellent
credit records have not benefited from lower credit costs (CIS Kenya, 2018).
Financial institutions are required to operate within a framework of rules and standards designed to ensure
proper oversight and accountability in the financial sector (Goodhart, Dimitrios & Shubik, 2013). These
regulations are essential for maintaining the stability and trustworthiness of the financial system.
Governmental or non-governmental entities guarantee the implementation of financial regulations. These rules
have an impact on the banking industry's structure, which benefits customers.
Some of the recent regulations include and are not limited to; Data Protection and Privacy: Every individual's
right to privacy, encompassing the protection of personal and private information, is safeguarded under Article
31 of the 2010 Kenyan Constitution. The Data Protection Act (DPA) of 2019, which was enacted to uphold
this constitutional provision, governs the handling of personal data. It outlines the rights of data subjects, as
well as the responsibilities of data controllers and processors. For Fintech firms, this is particularly relevant
due to the necessity of obtaining and processing customer Know Your Customer (KYC) information and
transactional data. The Act defines personal data as "any information relating to an identified or identifiable
natural person" and establishes principles and obligations for the lawful collection and processing of such data.
Consumer Protection: The Consumer Protection Act of 2012 serves as the primary legislation for safeguarding
consumers and preventing unfair trade practices. It is important to note that many sector-specific regulations
also include provisions related to consumer protection. For example, the CBK (Digital Credit Providers)
Regulations of 2022 encompass consumer protection under Part VII. To be in compliance, digital credit
institutions need to put forth transaction vouchers; they are also being instructed to do customer redress,
audience access and constraint on gathering customer information. Moreover, providers have agreed not to run
false ads per national regulations released thus far as well respecting all provisions of the law book there
imposed by the Money Remittance Regulations.
The National Payment System Regulations of 2014 are very clear about consumer protection regulations in
terms of disclosure, customer care, complaint resolution, service agreements and confidentiality. The Money
Remittance Regulations also deal with consumer protection laws in the (Consumer Protection Act of 2012).
Formative education. Under Section 2 of the proceeds and Anti-money Laundering Act 2009, 'regime' means
any institution engaged in financial services such as (a) managing payment instruments; that is to say creating
issuing and collecting credit cards, bearer checks or money substitute, drafts of electricity but in form the one
example electronic money; (b) providing a gateway for which payor and payee may complete transactions
between them Financial institutions fall into this category and are therefore classified as reporting entities by
the Act. Reporting entities are required to register with Financial Reporting Centre (FRC) and shall also be
responsible for developing effective policies against money laundering Therefore, according to Chapter IV of
the Act, their duties include identity verification; keeping detailed records on customers; conducting regular
registration; furnishing monthly Activity Reports to FRC; putting in place internal reporting systems. In other
words, it is through legislative conduct that these legal obligations are necessary for Fintech firms if they
decide to live in Kenya like persons with natural rights.
Empirical Literature Review
Financial Technologies
Over the years, microfinance institutions (MFIs) have evolved to address the financial needs of underserved
communities, offering a range of financial services. However, the financial landscape has witnessed significant
disruption with the rise of Financial Technology (Fintech), leading to the emergence of innovative credit
products and credit-sharing mechanisms.
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Different scholars have published studies about the operation of MFIs, which align with this study's objectives.
However much the studies achieve their objectives, they need to be improved in some ways, which we seek to
achieve in this research.
In their 2021 research, Ong’era and Omagwa examined the effect of mobile banking on the financial
performance of select commercial banks in Kenya. The primary objective was to evaluate how the adoption of
mobile banking affects the financial results of these institutions.
The study concentrated on four commercial banks and employed a descriptive research design, utilizing
purposive sampling to choose participants. Data were gathered from both primary and secondary sources, with
questionnaires facilitating the collection of primary data, while financial statements from the years 2011 to
2015 served as the secondary data source.
The analysis incorporated both inferential and descriptive statistical techniques. The results revealed that
mobile banking positively influences the financial performance of commercial banks in Kenya. The
researchers suggested that policymakers should encourage the growth of mobile banking services and lessen
dependency on traditional branch-based systems to enhance profitability.
Harelimana (2018) conducted research on how mobile banking affected Unguka Microfinance Bank Limited's
financial performance in Rwanda from 2012 to 2016. The objective was to evaluate how Rwandan
microfinance institutions' financial performance was affected by the volume of transactions and goods offered
by mobile banking services. Primary data were gathered for the study using both qualitative and quantitative
methods, including questionnaires and interviews. Fifty Unguka Microfinance staff were the target population.
Return on equity (ROE) and return on asset (ROA) were used to gauge financial success. According to the
research, there is a direct link between Unguka Microfinance Institution's financial success and mobile
banking. According to the study, the firm should reduce transaction costs in order to increase the revenues
The first study by (Omondi, 2018) focused on MFI's and SME's financial performance in Kisumu. The study's
sample population, however, focused explicitly on youths across the seven sub-counties in the county who
owned enterprises. The second scholar (Ngumo, 2020) published similar research on the determinants of the
financial performance of MFI banks in Kenya. Both studies achieved different conclusions concerning the
factors under consideration for each particular study. Omondi's research found that factors such as access to
credit, savings mobilization, financial skills training and role modeling had a significant impact on the
financial performance of SMEs. In contrast, Ngumo's study established a direct link between operational
efficiency, capital adequacy, firm size and the financial performance of microfinance banks in Kenya.
The studies mentioned above show that a vast range of factors, which may not fit in a single study, may
influence the performance of MFIs. None of the scholars considered the effect of fintech adoption, credit
information-sharing nor the impact of banking regulations. In addition, the sample populations used for both
studies may not be appropriate if one needs to make conclusions about Kisumu County in specific. The first
study is limited to youths, while the second one covers the entire country.
In order to major in the above research achievements and cover up for some of their limitations, this study
aims to narrow down to two significant factors: fintech credits and credit information sharing. This study also
targets a sample population of MFIs across Kisumu County.
Credit Information Sharing
Turner and Varghese (2007) carried out a study in Brazil that examined how credit bureaus can reduce
uncertainties in lending. Their findings showed that when lenders lack reliable borrower information, they
often misjudge risk therefore mistaking low-risk clients for high-risk ones and vice versa. This mismatch can
lead to unfair interest rates, where cautious borrowers are overcharged and riskier clients benefit from
unintended advantages. As a result, more high-risk borrowers are attracted to the credit market, which pushes
up average interest rates and contributes to rising default levels. The lenders, in turn, could tighten up access to
credit even for those with legitimate needs. with similar risk levels. In the end, the research found that credit
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bureaus contribute to correcting this flies in the ointment by introducingbetter risk evaluation and reducing the
default rate. While their study was concentrated in Brazil, our study turns the focus on Kisumu City, Kenya, to
see if loan allocation behaviours carried across to being seen in what they termed credit-sharing arrangements.
Some specific studies have been done on credit information sharing in the Kenyan context; for example,
Mugwe and Oliweney (2015) have examined the way in which the credit information sharing has influenced
the commercial banks. Their study focused on the changes in financial performance indicators like return on
equity, return on assets and net interest margins, pre andpost the development of licensed Credit Reference
Bureaus (CRBs). Based on 43 banks during 2005-2014, the study had non-performing loans, total assets, and
interest income as dependent variables, and it also utilized a correlational research design. Their results
revealed a significant recovery of profitability after the implementation of CRBs in 2010. This study also
found that non performing loans reduced to below 5% whereas net interest margins remain robust at over 6%.
to this, regression results indicate that approximately 68% of bank variation Moreover, that system wide bank
liquidity risk exposure is due to sen- sitivity to bank liquidity risk exposure was explained.addicted to this risk
factor. profitability might be related to the sharing of credit information.
Kipyego and Wandera (2013) also studied the effects of credit information sharing concentrating on Kenya.
Commercial Bank (KCB). They also looked at how loan performance had evolved before and after CRBs were
established, targets of bad loans identified and levels of risk exposure established across the varies nurses.’’.
sectors. The research was descriptive case study in design and used stratified random sampling utilizing both
primary and secondary sources of data comprising financial reports of the KCB parties. between 2007 and
2012. Their findings indicated high level of association between credit information sharing and a decline in
non-performing loans. The sharing of borrower data, the researchers emphasized, enhanced transparency,
better credit decisions, lower risk, resiliency, and stimulates responsible behaviour and reducing the total cost
of loans.
Then, MFIs have become more interested in credit information sharing as a. tool to assess borrowers and to
reduce the risk in lending. By allowing financial In contrast, conditional on interbank institutions sharing
information on borrowers’ credit histories, this mechanism is conducive to more productive. reliable,
comprehensive evaluations of a client’s credit health. Song (2017) studied microcredit cooperatives and
concluded that information-sharing policies resulted in lower loan defaults and more robust loan portfolios.
Alongside this development, PPC lending platforms empowered by fintech were developed as a novel kind of
collaborative lending landing platform. alternatives to traditional credit systems.
Lenders and borrowers are also linked directly by these platforms. with the addition of an enhanced trading
platform, bypassing current financial intermediaries and resulting in a more transparent and efficient
borrowing process. And yet, despite this progress, the particular impact of credit sharing and fintech-on-banks‟
likelihood of extending loans and their risk assessment abilities remains an open question, lending models on
the financial performance of MFIs in Kisumu has not been done in depth explored by identifying an important
avenue of future research.
Banking Regulations
The relationship between microfinance institutions and Fintech is greatly influenced by the regulatory
environment. Good regulations should both encourage technological innovation and maintain consumer
protection, but also financial stability. Rahman (2022) pointed out that in favour of responsible development of
the FinTech, regulatory environment is crucial. Simultaneously we must appropriately manage risks.
Over time, the regulatory framework for microfinance institutions has adapted to reflect the increasing
relevance of the sector and integration of Fintech solutions. Governments and regulatory authorities have been
working on how to do that for several years. In Kenya, the 2006 microfinance act provides for licensing,
regulation and supervision of microfinance institutions by defined bodies. The CBK plays a significant role in
regulating and supervising the microfinance sector. The CBK’s Microfinance Act outlines the legal
framework for the establishment, licensing, and operations of microfinance institutions. It also sets out the
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prudential requirements, governance standards, and reporting guidelines for MFIs so as to maintain their
financial health (and protect borrowers).
It also prescribes how MFI’s must be run in order to have deposit-taking functions, location of their business,
appropriate levels of capitalization with provisions for increasing or decreasing as necessary over time in all
areas; liquidity risk management plans; rules governing governance structure; internal control systems for
preventing any unauthorized transactions from taking place on its books while maintaining strict
confidentiality about customers who have placed money at an MFI and have not reported themselves to be
involved in any businesses at all; external control mechanisms such as audits carried out by external auditors
hired with prior MOU’s signed due before commencing work so they know what kind of results are expected
from them related timescales for when results will be available after each audit; reporting financial
statements so that people understand what is happening within a company (or any other type of entity).
In the latest banking regulations that were released in Kenya, emphasis was placed upon responsible lending
practices and effective risk management. As a result, MFIs must adhere strictly to the guidelines on loan
classification and provisioning in order ensure a high standard of quality in their loan portfolios while
safeguarding against potential credit risks. Njeri (2013) conducted research into the impact of liquidity on the
financial performance of deposit-taking microfinance institutions in Kenya. The study found that for these
institutions, liquidity greatly influences financial performance. The findings indicated that there was a positive
correlation between liquidity and microfinance institution performance.
The study also proposed strategies to encourage microfinance institutions to improve their financial
performance, with an emphasis on greater industry efficiency. In addition, it found that there was a positive
relationship between asset growth at microfinance institutions and their financial performance. The study
posited that obtaining additional loans from banks was a strategy likely to improve financial performance and
promote growth. Therefore, the study highlighted how significant the impact of asset growth on financial
performance within this sector is. Furthermore, operational efficiency was found to have a positive and
significant impact on profitability within this industry because efficient firms could carry out many
transactions in a short amount of time.
This high degree of efficiency positively influenced customer satisfaction and customer faith in the company.
Thus, over time the increase in transaction volumes was the major driver behind financial growth and overall
performance of the institution. As a result, a statistically significant positive correlation was found for
operating efficiency and the financial performance of MFIs.
Determinants of Performance in Micro Finance Institutions
The performance of microfinance institutions is evaluated through various financial indicators, this includes
size of the firm, liquidity and capital adequacy.
Size of the Firm.
Schmalensee (2001) defined size in relation to total assets and used profit margin and return on assets as two
of the accounting performance metrics. The number of people, sales, assets, and value contributed are among
the often-used metrics to determine the size of the company (Pandy, 2005). According to Lee (2008), there is a
positive correlation between a company's size and financial success because operating costs might increase
while cutting back on certain expenses. Large companies can reduce their operating risks by diversifying the
assets they choose (Liargovas, 2008). According to Liargovas and Skandalis (2008), large companies
outperform small ones overall because they can take advantage of economies of scale and have the resources to
retain and grow their management capacity.
Liquidity
Financial institutions are evaluated based on their capacity to pay off debt and manage their cash flow without
going bankrupt or suffering losses. It indicates that the financial organization has the capacity to finance an
expansion in assets so they can pay their debts when they become due (Kumar & Yadav, 2013). Liquidity is
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traditionally understood to mean an organization's capacity to pay its debts as they become due. It might also
imply that a financial institution can look to the market for funding in the event that they are required to pay
back debt. Thus, the capacity to comprehend the financing's cost-benefit analysis is linked to liquidity
management.
According to Kumar and Yadav (2013), organizations must strategically invest their assets to generate
sufficient returns, enabling them to repay borrowed funds while also earning a profit.
Capital Adequacy
Capital adequacy simply refers to whether a microfinance institution (MFI) has enough financial strength to
absorb losses without collapsing. It’s a key measure of how prepared an institution is to handle different types
of risks like loan defaults, operational setbacks or market shocks (Karlyn, 1984). A commonly used tool for
assessing this is the Capital Adequacy Ratio (CAR) which compares an institution’s capital to its risk-weighted
assets. This ratio acts as a financial safety net, protecting depositors, investors and the institution itself from
potential insolvency.
For MFIs, having enough capital isn’t just a legal requirementit’s also what gives clients and stakeholders
confidence. So even in want of money, an institutional completes all its obligations and ensures to be those
overseeing clients can continue saving funds will come. Especially in times of financial trouble when people
panic out of fear or fear turns to greed and there are sudden withdrawals, that kind (style) of stability becomes
especially critical.
This introduces a possible avenue for indirect financial support because if an institution is allowed to collapse
its capital is generally used once and this does not matter, Rehema (2013). Nevertheless, in the process timely
intervention can save otherwise irreversible losses such as those mentioned in the chapter. Therefore,
institutions with negative parent products typically have no option but to dissolve themselves and either close
shop altogether or merge with some other bank.
There is also empirical evidence that strength in capital adequacy often opens the door to vigorous financial
performance. For instance, Kariuki and Wafula (2017) found that MFIs with relatively healthy capital
positions tend to rely less on outside lending and are more profitable. Similarly, Osoro and Muturi (2015)
discovered that financial institutions with excellent levels of capital adequacy usually have stronger asset
returns, which suggests in turn that a powerful balance sheet is essential for long-term financial health.
RESEARCH METHODOLOGY
Introduction
This section details the research approach employed in the survey and explains the design of this study. It also
lists the target population and the sampling techniques used in studies. In addition, it delineates the instruments
utilized for data collection, the procedures implemented to ensure reliability and validity of research findings
as well as the data analysis methods applied. The chapter contains guidelines on how researchers should
conduct research ethically in all spheres.
Research Design
This study utilized a correlational research plan that is suitable for studying the interrelationship
between measurement variables. Using this method, the research investigates relationship between
financial technology (Fintech), credit information sharing and Banking regulations on banking with
the performance of microfinance institutions.
As Saunders et al. (2007) explains that a research design provides the overall plan for how the research
questions will be addressed. In this case, using a correlational design made it possible to apply statistical
methods to assess the relationships between the selected variables using numerical data. Creswell (2003)
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supports this approach noting that correlational designs are part of the broader category of quantitative research
methods where data is collected and analyzed to establish patterns or associations among variables.
Study Area
The study area was micro-finance institutions in Kisumu City which contribute immensely in various sectors
in the country to bring change and boost the economy.
Target Population
The study target population comprised of all employees with knowledge of the institution's financial
performance and regulatory compliance that is; Credit Managers and the branch managers, operation managers
and Credit officers within microfinance institutions in Kisumu City. According to Oso and Onen (2009), the
term population refers to the entire group of individuals or elements that belong to a clearly defined category
and are of interest in a specific study. Our research is targeting a total population of 60 which consist of Credit
Managers and the branch managers, operation managers and Credit officers within microfinance institutions in
Kisumu (CBK, 2022).
Sample Size and Sampling Procedures
The census method of data collection was used in collecting information from credit managers, branch
managers, operation managers and credit officers within microfinance institutions to obtain a complete and
comprehensive dataset for analysis. An association between the variables was also assessed after collecting
quantitative data from all 60 key employees with knowledge of the institution's financial performance.
Research Instruments
The collect primary data for the study, self administered questionnaires were used. This tool was selected
because it is both efficient and cost-effective, especially when working with a large sample. Moreover, it
allows for the standardized data collection that makes it easier to analyze and compare responses easily
(Creswell, 2003).
The questionnaire was structured to generate mainly quantitative data, but it also provided room for deeper
insights. It featured a combination of structured and unstructured questions. The structured items offered fixed
response options, which simplified the coding process and facilitated statistical analysis. Meanwhile, the
unstructured questions gave respondents the freedom to elaborate on their views by providing more detailed
feedback and richer perspectives on the core variables.
Validity of Research Instrument
How well a research instrument captures or measures the particular concept it is meant to investigate has been
called "validity." According to Bryman (2012) it describes extent that the outcomes of the data accurately view
what is being studied. To improve content validity, the researchers consult with experts including professors
and practitioners with finance as well as micro-finance background; this will provide tangible feedback on
their data collection methodology.
In addition, proper construct validity requires that we carefully examine and revise the questionnaire so as not
to include any ambiguous or misleading questions. In its process of this kind, all items on the final draft were
tested for understandability among respondents; in order words, they needed to make sense after being checked
by testers whose language was more common than that used by researchers.
Reliability of Research Instrument
Reliability refers to whether a research instrument shows the same result under constant conditions when
repeated as well. Administered improperly, it can reduce the validity of your data. In relation to how accurately
the data reflects what you are measuring
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According to Mugenda and Mugenda (2003), reliability refers to how well the data collected actually reflects
what was being studied by it. In this research, the method of test-retest was used to measure the reliability of
the questionnaire adopted. It did so by posing the same questions in a set set on two occasions to a group of
participants, and comparing for consistency between their responses each time.
To further verify the reliability of the instrument, Cronbach’s Alpha coefficient was calculated. As suggested
by Sekaran (2001), a Cronbach’s Alpha value of 0.7 or above is generally considered acceptable, indicating
good internal consistency among the items.
The reliability analysis was conducted using SPSS version 22, which helped determine how well the items
within each section of the questionnaire were related to one another.
Table 1: Reliability Results
Nos. of Item
Coefficient
Verdict
Fintech credit and financial performance
8
0.958
Reliable
Credit sharing and financial performance
8
0.927
Reliable
Banking Regulation and financial performance
8
0.891
Reliable
Financial performance
6
0.889
Reliable
Overall
30
0.916
Reliable
Data Collection Procedure
The researcher presented the introduction letter showing the intent to carry out the research to the various
Micro-Finance institution in Kisumu City so as to seek permission to collect data, once permission was granted
the researcher issued the questionnaires to the respondents. The respondents were then given time to fill the
questionnaires and then submit to the researcher.
Data Analysis and Presentation
This study explored both the strength and nature of relationships between the study variables, including
potential cause-effect dynamics. Data analysis involved the use of both inferential and descriptive statistical
methods. After collection, responses were first coded and reviewed to detect and correct any inconsistencies or
missing information. The analysis was conducted using SPSS version 22.
A Likert scale was utilized to quantify responses for descriptive statistics. This approach helped reduces
respondent bias and allowed for inferential analysis to be performed. The Likert scale values were arranged to
reflects the extent to which a given attribute was present or absent, as guided by Mugenda and Mugenda
(2003).
Descriptive Statistics
Data was collected and descriptive statistics were employed to assess its normality The method summarized
and presented key features of a data set, offering insights into patterns, trends and relationships relevant for
study. This is the basic purpose for descriptive statistics.
The primary purpose of descriptive statistics was to provide basic summaries of data without inference-to lay
out information in such a way that it could be tested. In the study, descriptive analysis was carried out with
respect to both demographic characteristics and main research variables, namely Fintech Credit, Credit
Information Sharing, and Banking Regulations. This involved calculating frequencies, means, and percentages
to present participants' responses clearly as required by the researcher.
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Inferential Statistics
To make a clear and precise interpretation of data, inferential statistics were used. And depending on the
sample results, conclusions about the entire population could then will be inferred. In order to achieve the
objectives of the study, this research used the Pearson′s Correlation Coefficient technique for examining the
strengths and directions of interrelationships between variables under analysis.
With the Pearson′s method, the author could see whether connections between variables were positive or
negative, and if they were weak, moderate or strong. As noted in the formula proposed by Karl Pearson
(Bagchi & Khamrui, 2012), the coefficient was calculated dividing the covariance of two variables by their
standard deviations.
r = Karl Pearson’s correlation formula;
To assess the statistical significance of these relationships, hypothesis testing was performed. If the observed
coefficients were meaningful and not the result of random chance a significance level of 5% (α=0.05) was used
to determine both these Resulted.
The study also used multiple regression analysis in order to assess how variables Fintech Credit, Credit
Information Product and Banking Regulations were affecting the dependent variable --financial performance
of microfinance institutions. This allowed us to estimate how much deviation from average performance could
be explained by our predictors. As Lucey (1996) pointed out, multiple regression is appropriate when the
situation involves more than one independent variable and a continuous explanatory variable. To check that
assumptions (particularly those for regression models) do not weaken our results, the present study subjected
the regression model to a series of tests for the robustness of its model. We conducted diagnostic tests to
confirm that these assumptions held, which is essential for the validity of results from regression analysis.
Then, using the regression model as our instrument, direction and strength of relationships between variables
were assessed, as represented in the regression equation (Equation 3.2).
Model
This equation (3.2) examines the main effects of financial technology credit, credit sharing, and banking
regulation on performance.
Y
i
= β
0
+ β
1
F+ β
2
S + β
3
R+Ɛ
i
…………………………………………. (3.2)
Where: Y
i
: Performance of Micro-Finance institutions
β
0
: is the y intercept
β1 and β
2
are the regression (beta) coefficients
F: Variable of Fintech Credit
S: Variable of Credit sharing
R: Variable of Banking regulations
Ɛ
i
: Error term - the residual or unexplained variation. (A lower value of the standard error implies that the
regression model provides a close fit to the actual data.)
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Ethical Consideration
The research observed the principle of this study with a high regard for ethical standards. A specific purpose
was this research; participants in the whole study were clearly informed as to its objectives so that none should
feel burdened by self- consciousness about their involvement. Participants were also assured that all aspects of
the study would be held in strict confidence. Participation was entirely voluntary and no reward or incentive
offered themselves in reply to questions.
As Soomer and Sommer (2004) emphasize, ethics in social research means particularly upholding such basic
norms as confidentiality, anonymity and transparency. These principles, then, were scrupulously observed
throughout the study in order to protect the rights and dignity of all interviewees. Nonetheless it did not
compromise the actual research process.
RESULTS AND FINDINGS
Introduction
By investigating the performance of microfinance institutions in Kisumu city as influenced by FDCs, Sharing
Credit and bank Regulation, this chapter presents the outcomes and conclusions of that study. This segment is
structured based on the research objectives. The descriptive and inferential analysis on the effect of Fintech
credits on the financial performance of microfinance institutions is covered in the first section of this chapter.
The second section gives descriptive and inferential analyses of the impact of credit sharing on financial
performance at microfinance institutions. The third section provides descriptive and inferential analyses of the
correlation between bank regulatory compliance on their financial result for MFIs in Kisumu City.
Response Rate and Demographic Information
In this study section, the response rate and demographic information findings are reported. It presents the
analyzed data results for the gender outcome, the highest level of education the respondents had attained, their
age group, their position in the MFI and their work experience (years worked with the MFI).
Response Rate
Figure 1 presents the response rate of the study which was 83%, indicating that from the 60 study
questionnaires that were distributed to the identified respondents, 50 were duly filled and used in the study.
Hazzi and Maldaon (2015) state that any study that obtains a response rate that is over and above 50% is
alleged to be valid and the outcome may be relied upon. The figure shows that the study had surpassed this
threshold.
Figure 1: Response rate of the study
Obtained
Responses, 83%
Unobtained
Responses, 17%
Response rate of the study
Obtained Responses Unobtained Responses
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Gender Outcome
Participants were asked to indicate their gender category. According to the findings, as shown in figure 2, 36%
of respondents were female and 64% were male. This indicates that the study had a fair representation of the
gender, and that the management of Kenyan MFI’s had a good gender balance.
Figure 2: Gender of the Respondents
Highest Level of Education
The responses for the participant’s highest level of education are shown in figure 3. It shows that 12% of the
managers had attained a post graduate degree, 26% had a bachelor’s degree, and 54% had diplomas and only
8% had certificate. This shows that the management of Kenyan MFI’s had a high educational background,
allowing them to understand the study questions and submit valid responses.
Figure 3: Education level
Fintech Credits
Figure 4 gives the percentage summary and bar graphs of respondents' opinions on various aspects of
integrating Fintech credit services. On the positive Impact on Serving More Borrowers, a significant portion
44% of the respondents strongly agree that integrating Fintech credit services has positively impacted their
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institution's ability to serve more borrowers. Combined with the agree responses of 28%, this suggests that the
majority of respondent’s view Fintech integration favorably in terms of expanding the borrower’s base.
Based on the contribution to Loan Portfolio, a substantial proportion of 38% strongly agree and 36% agree that
Fintech credit has contributed to an increase in their institution's loan portfolio. This indicates that many
respondents perceive Fintech credit as beneficial for growing their loan portfolios. Similarly, a noteworthy
finding is that a significant percentage of 82% of respondents collectively agree that Fintech credit has
improved their institution's overall financial performance, while 78% agree it has enhanced the efficiency of
their lending process. These findings highlight the perceived positive impacts of Fintech integration on both
financial performance and operational efficiency.
On the client outreach and accessibility, while fewer respondents strongly agree that Fintech credit has
increased client outreach and accessibility by 30%, a larger proportion of 40% agree with this statement. This
suggests that while there is consensus on the positive impact, it might not be as strong as in other areas. The
responses regarding the positive impact of Fintech credit on the repayment rate of loans are less unanimous,
only 20% strongly agree, and 33% agree, indicating that a considerable portion of respondents are neutral or
skeptical about this aspect.
Considering the adoption of Fintech by MFI’s, majority of respondents (38% strongly agree and 38% agree)
believe that their MFI has fully adopted Fintech solutions in its operation, suggesting a widespread acceptance
and implementation of Fintech within the industry. Finally, there is a considerable consensus (40% strongly
agree and 36% agree) among respondents that Fintech adoption has resulted in improvements in customer
satisfaction and engagement. This indicates that Fintech integration is seen as positively impacting the overall
customer experience.
Figure 4: Summary Statistics on the effects of Fintech Credit on Financial Performance
The results suggest a generally positive perception of Fintech integration among respondents, particularly
regarding its impact on serving more borrowers, contributing to loan portfolios, improving financial
0
5
10
15
20
25
30
35
40
45
50
Integrating
Fintech
credit
services
has
positively
impacted
our
institution
s ability to
serve…
Fintech
credit has
contribute
d to an
increase in
our
institution'
s loan
portfolio
The use of
Fintech
credit has
improved
the use of
our
institution'
s overall
financial
performan
ce
Fintech
credit has
improved
the
efficiency
of our
lending
process
Fintech
credit has
increased
our client
outreach
and
accessibilit
y
Fintech
credit has
positively
impacted
the
repayment
rate of
loans.
Your MFI
has fully
adopted
fintech
solutions
in its
operation
Fintech
adoption
has
resulted to
improvem
ent in
customer
satisfactio
n and
engageme
nt
Strongly Agree
44 38 36 38 30 20 38 40
Agree
28 36 46 40 40 33 38 36
Neutral
20 14 10 12 14 22 10 16
Disagree
8 8 4 2 6 10 4 4
Strongly Disagree
0 4 4 8 8 14 10 4
EFFECTS OF FINTECH CREDIT
Strongly Agree Agree Neutral Disagree Strongly Disagree
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performance and efficiency, and enhancing customer satisfaction and engagement. However, there are areas,
such as the repayment rate of loans, where opinions are more divided or uncertain.
Table 2 presents descriptive statistics summarizing the perceptions of respondents regarding the impact of
Fintech credit services on various aspects of their microfinance institution (MFI). The statistics include the
mean, standard deviation, variance, and skewness of responses to several statements about Fintech's influence
on the institution's ability to serve borrowers, increase the loan portfolio, improve financial performance,
enhance the lending process, and impact customer satisfaction. The data, gathered from 50 respondents (except
for one item with 49 respondents), provide insights into the overall agreement levels and variability in opinions
concerning the integration and adoption of Fintech solutions within the MFI.
Table 2: Descriptive statistics
Descriptive Statistics
N
Mean
Std. Deviation
Variance
Skewness
Statistic
Statistic
Statistic
Statistic
Statistic
Std. Error
Integrating Fintech credit
services has positively impacted
our institutions ability to serve
more borrowers
50
3.92
1.397
1.953
-1.722
.337
Fintech credit has contributed to
an increase in our institution's
loan portfolio
50
3.80
1.471
2.163
-1.563
.337
The use of Fintech credit has
improved the use of our
institution's overaLl financial
performance
50
3.94
1.331
1.772
-2.049
.337
Fintech credit has improved the
efficiency of our lending
process
50
3.90
1.374
1.888
-1.829
.337
Fintech credit has increased our
client outreach and accessibility
50
3.66
1.437
2.066
-1.388
.337
Fintech credit has positively
impacted the repayment rate of
loans.
50
3.27
1.483
2.199
-.839
.340
Your MFI has fully adopted
fintech solutions in its operation
50
3.82
1.453
2.110
-1.588
.337
Fintech adoption has resulted to
improvement in customer
satisfaction and engagement
50
3.92
1.368
1.871
-1.844
.337
Valid N (listwise)
50
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Across the statements, most means are close to 4, indicating overall agreement with the positive impacts of
Fintech. The lower mean (3.27) for the impact on loan repayment rates suggests this area is viewed less
favorably or is more uncertain among respondents.
The standard deviations, ranging from 1.331 to 1.483, indicate moderate variability in responses. This suggests
that while many respondents agree, there is a notable range of opinions.
The negative skewness across all items indicates that the responses are generally concentrated towards
agreement. The higher skewness values (e.g., -2.049 for overall financial performance) show a strong
consensus towards positive impacts, while lower skewness values (e.g., -0.839 for loan repayment rates)
suggest more mixed feelings or uncertainty.
Respondents generally perceive Fintech adoption as having a positive impact on various aspects of their
institution's operations, particularly in serving more borrowers, increasing the loan portfolio, improving overall
financial performance, and enhancing customer satisfaction. However, there is less certainty or positivity
regarding the impact on loan repayment rates. The negative skewness in most items indicates a strong tendency
towards agreement, although the moderate standard deviations highlight the presence of diverse opinions.
Credit sharing
Table 3 provides descriptive statistics on respondents' perceptions of the impact of credit-sharing practices
within their microfinance institution (MFI). The data include responses from 50 participants, summarized
using key statistical measures such as mean, standard deviation, variance, and skewness.
Table 3: Descriptive statistics on effects of credit sharing
Descriptive Statistics
N
Mean
Std. Deviation
Variance
Skewness
Statistic
Statistic
Statistic
Statistic
Statistic
Std. Error
Credit sharing has
improved our Institutions
ability to assess credit risk
50
3.96
1.160
1.345
-1.064
.337
Credit sharing has
increased our institution's
efficiency in making
lending decisions
50
4.16
1.037
1.076
-1.476
.337
Credit sharing has reduced
the rate of non-performing
loans in our institution.
50
4.00
1.010
1.020
-1.237
.337
There is a significant
change in your MFI's
financial performance since
engaging in credit sharing.
50
4.16
.710
.504
-.241
.337
Credit sharing has
improved the accuracy of
credit risk assessment and
decision making.
50
3.96
.989
.978
-.973
.337
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Credit sharing has
positively impacted the
repayment rates of loans
50
3.56
.929
.864
-.262
.337
Credit sharing adoption has
resulted to improvement in
customer satisfaction and
engagement
50
4.10
.931
.867
-1.469
.337
Credit sharing has
enhanced the efficiency of
our loan approval process
50
4.04
1.106
1.223
-1.308
.337
Valid N (listwise)
50
Most items have mean values around 4, indicating a strong agreement among respondents that credit sharing
has positively impacted various aspects of their MFI’s operations. The lowest mean (3.56) suggests more
neutral perceptions regarding the impact on loan repayment rates.
All items have negative skewness, meaning responses generally lean towards agreement, with the strongest
skews observed in efficiency in lending decisions and customer satisfaction.
Figure 5: Effects of Credit sharing on Financial Performance
Analyzing the percentage summary of respondents' opinions on credit sharing from figure 5, based on the
improved ability to assess Credit Risks, a significant portion of 42% strongly agree that credit sharing has
improved their institution's ability to assess credit risk. Combined with the agree responses of 28%, this
0 20 40 60
Credit sharing has improved our Institutions ability…
Credit sharing has reduced the rate of non-…
Credit sharing has improved the accuracy of credit…
Credit sharing adoption has resulted to…
Credit
sharing has
improved
our
Institutions
ability to
assess credit
risk
Credit
sharing has
increased
our
institution's
efficiency in
makin
lending
decisions
Credit
sharing has
reduced the
rate of non-
performing
loans in our
institution.
There is a
significant
change in
your MFI's
financial
performance
since
engaging in
credit
sharing.
Credit
sharing has
improved
the accuracy
of credit risk
assessment
and decision
making.
Credit
sharing has
positively
impacted the
repayment
rates of
loans
Credit
sharing
adoption has
resulted to
improvemen
t in customer
satisfaction
and
engagement
Credit
sharing has
enhanced
the
effficiency of
our loan
approval
process
Strongly Disagree
6 4 4 0 4 2 4 6
Disagree
4 4 4 0 0 8 0 2
Neutral
20 10 14 18 26 38 14 16
Agree
28 36 44 48 36 36 46 34
Strongly Agree
42 46 34 34 34 16 36 42
EFFECTS OF CREDIT SHARING
Strongly Disagree Disagree Neutral Agree Strongly Agree
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indicates a widespread belief among respondents in the positive impact of credit sharing on credit risk
assessment.
Similarly, a substantial majority of 46% strongly agree and 36% agree that credit sharing has increased their
institution's efficiency in making lending decisions. This suggests that credit sharing is perceived as beneficial
for streamlining lending processes. A considerable proportion of 34% strongly agree and 44% agree that credit
sharing has reduced the rate of non-performing loans in their institution. This indicates that many respondents
believe credit sharing has a positive impact on loan performance.
Based on the accuracy of Credit Risk Assessment, while there is agreement of 34% that credit sharing has
improved the accuracy of credit risk assessment and decision making, there is a substantial proportion of 26%
who are neutral on this aspect. This suggests that there may be some uncertainty or variation in perception
regarding the impact of credit sharing on the accuracy of risk assessment. On the other hand, the responses
regarding the positive impact of credit sharing on the repayment rates of loans are mixed since 36% agree and
16% strongly agree, 8% disagree and 2% strongly disagree. This indicates that opinions are somewhat divided
on whether credit sharing has a significant effect on loan repayment rates.
A majority (46%) strongly agree and 36% agree that credit sharing adoption has resulted in improvements in
customer satisfaction and engagement. This suggests that credit sharing is perceived as positively impacting
the overall customer experience. On the efficiency of Loan Approval Process, A significant portion of 42%
strongly agree and 34% agree that credit sharing has enhanced the efficiency of their loan approval process.
This indicates that credit sharing is viewed favorably in terms of improving operational efficiency.
It is clear that a noteworthy finding is that a significant percentage (48%) of respondents agree there is a
significant change in their MFI's financial performance since engaging in credit sharing. This suggests that
credit sharing may have a notable effect on the overall financial performance of MFIs.
Banking Regulations
Examining the percentage summary of respondents' opinions on banking regulations provided in figure 6, a
notable percentage of respondents (28% strongly agree and 38% agree) believe that recent banking regulations
have improved their MFI's risk management practices, a considerable portion (26%) are neutral. This suggests
a mixed perception regarding the impact of regulations on risk management effectiveness.
A significant proportion of respondents (32% strongly agree and 40% agree) believe that regulatory
compliance has increased operational costs for their MFI. This indicates a widespread perception that
compliance with banking regulations imposes financial burdens on institutions. On the improved stability,
while a notable percentage of respondents (24% strongly agree and 36% agree) believe that banking
regulations have improved the stability of their MFI, a significant portion (24%) are neutral. This suggests
some uncertainty or variability in perceptions regarding the impact of regulations on institutional stability.
Based on the level of compliance with Regulations, majority of respondents (50%) believe that their MFI is
fully compliant with Banking Regulations of Kenya. However, a notable percentage (24%) disagree, indicating
potential compliance challenges within the industry. A significant proportion of respondents (36% strongly
agree and 32% agree) also believes that banking regulations have increased compliance costs for their MFI.
This underscores the perceived financial impact of regulatory compliance requirements.
Assessing the impact of Banking Regulations on confidence of investors and clients, a notable percentage of
respondents (38% strongly agree and 32% agree) believe that banking regulations have positively impacted the
confidence of investors and clients, a considerable portion (18%) are neutral. This suggests some variability in
perceptions regarding the influence of regulations on stakeholder confidence. A significant number of
respondents (52%) indicate that their MFI undergoes regulatory audits or assessments. This highlights the
prevalence of regulatory oversight within the industry.
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Finally, on the influence on Pricing Strategies, responses regarding the influence of banking regulations on
MFIs' ability to set interest rates or pricing strategies are varied. A significant percentage (48%) agree that
regulations influence pricing strategies while a notable portion (28%) disagree.
Table 4 below presents descriptive statistics summarizing the perceptions of respondents regarding the impact
of recent banking regulations on various aspects of their microfinance institution (MFI). The data, collected
from 50 respondents, includes key measures such as the mean, standard deviation, variance, and skewness,
offering insights into how these regulations are perceived in terms of risk management, operational costs,
stability, compliance, investor confidence, and regulatory audits.
Table 4: Descriptive Statistics of Banking Regulations
Descriptive Statistics
N
Mean
Std. Deviation
Variance
Skewness
Statistic
Statistic
Statistic
Statistic
Statistic
Std. Error
Recent banking regulations
have improved our risk
management practices
50
3.82
1.024
1.049
-.811
.337
Regulatory compliance has
increased operational costs
50
3.88
1.081
1.169
-.964
.337
28
32
24
50
36
38
52
28
38
40
36
24
32
32
36
48
26
16
24
12
20
18
8
14
4
8
12
10
4
4
0
0
4
4
4
4
8
8
4
10
0 20 40 60 80 100 120
Recent banking regulations have improved our risk
management practices
Regulatory compliance has increased operational costs for
our MFI
Banking regulations has improved the stability of our MFI
Our MFI is fully compliant with Banking Regulations of
Kenya
Banking regulations has increased our compliance costs
Banking Regulations has positively impacted the confidence
of investors and clients
Our MFI undergo regulatory audits or assessment
Banking regulations influenced your MFI's ability to set
interest rates or pricing strategies for financial products
EFFECTS OF BANKING REGULATIONS
Strongly Agree Agree Neutral Disagree Strongly Disagree
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for our MFI
Banking regulations has
improved the stability of our
MFI
50
3.64
1.102
1.215
-.562
.337
Our MFI is fully compliant
with Banking Regulations
of Kenya
50
4.06
1.185
1.404
-1.116
.337
Banking regulations has
increased our compliance
costs
50
3.84
1.201
1.443
-1.004
.337
Banking Regulations has
positively impacted the
confidence of investors and
clients
50
3.88
1.206
1.455
-1.069
.337
Our MFI undergo regulatory
audits or assessment
50
4.32
.935
.875
-1.943
.337
Banking regulations
influenced your MFI's
ability to set interest rates or
pricing strategies for
financial products
50
3.84
1.149
1.321
-1.355
.337
Valid N (listwise)
50
The mean values range from 3.64 to 4.32, indicating overall agreement with the statements. The highest mean
(4.32) suggests strong consensus that MFIs undergo regulatory audits, while the lower mean (3.64) shows a
more neutral stance on the improvement of MFI stability.
The standard deviations range from 0.935 to 1.206, reflecting varying degrees of consensus, with the most
consensus on the occurrence of regulatory audits and the least on the impact of regulations on operational costs
and investor confidence.
All items show negative skewness, indicating that the distribution of responses generally leans towards
agreement. The most skewed item (regulatory audits) suggests a strong consensus in favor of agreement.
Generally, table 4 indicates that respondents perceive recent banking regulations as generally beneficial,
particularly in improving compliance, investor confidence, and regulatory oversight, though they also
acknowledge increased operational and compliance costs.
Correlation Analysis between Fintech Credit, Credit sharing and Banking Regulations on Financial
Performance
Table 5 presents the results of the correlation that provide insights into the relationships between regulatory
compliance, credit sharing, Fintech credit, and the overall financial performance of MFIs (Microfinance
Institutions)
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Table 5: Correlation Coefficients
Correlations
Regulatory
compliance
Credit
sharing
Fintech
credit
Financial
Performance
Regulatory
compliance
Pearson Correlation
1
.571
**
.312
*
0.528
Sig. (2-tailed)
.000
.027
.046
N
50
50
50
50
Credit sharing
Pearson Correlation
.571
**
1
.488
**
0.405
Sig. (2-tailed)
.000
.000
.008
N
50
50
50
50
Fintech credit
Pearson Correlation
.312
*
.488
**
1
.703
**
Sig. (2-tailed)
.027
.000
.004
N
50
50
50
50
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
There is a moderate positive correlation (Pearson's r = 0.528, p < 0.01) between regulatory compliance and the
overall financial performance of MFIs. This suggests that MFIs that exhibit moderate higher levels of
regulatory compliance tend to have better financial performance.
This correlation underscores the importance of adhering to regulatory requirements for achieving financial
success in the microfinance sector.
Based on the credit sharing and financial performance, there is a weak positive correlation (Pearson's r = 0.405,
p < 0.01) between the impact of credit sharing on the financial performance of MFIs. This indicates that MFIs
experiencing a slight positive impacts from credit sharing initiatives tend to exhibit better financial
performance. Credit sharing practices seem to contribute positively to enhancing the financial performance of
MFIs.
Very similar to this, there is a strong positive correlation (Pearson's r = 0.703, p < 0.01) between the
impact of Fintech credit and financial performance in MFIs. The implication is that MFIs who have
implemented Fintech credit solutions boast significantly better financial performance. Fintech credit adoption
appears to bring tangible financial benefits for MFIs. These results highlight the interdependence- even
interlinkage -among such aspects as regulatory compliance, credit sharing, Fintech credit adoption and
financial performance in microfinance institutions.
While regulatory compliance serves as a basic system, both credit sharing and Fintech credit adoption play key
roles in propelling MI to achieve better financial results. The positive correlations suggest that regulatory
compliancy, credit sharing, and FinTech credit adoption have important roles in determining the overall
financial health of MFIs. Institutions which are able to meet regulatory requirements and at the same time
harness innovative practices such as regulatory compliancy along with financial integration will perform better
when it comes to finance. MFIs should make regulatory compliance as a basic aspect of their operations. That
way, they can ensure that they operate legally and avoid any potential regulatory risks. Investing in credit
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sharing activities means that MFIs get substantial returns from their financial performance. This way, MFIs
can expand their reach, enhance operating efficiency, and do a better job for clients.
Government departments and industry organizations need to understand that an enabling regulatory
environment promoting innovation is essential for consumer protection and stable finances in the field of
microfinancing. To sum up, it can be seen from the correlation results that regulatory compliance, credit
sharing, and Fintech credit adoption are key factors in shaping the financial performance of MFIs. These
results offer valuable enlightenment for MFIs and policymakers who are concerned with making the
microfinance sector a more enduring, tougher adversary on the international stage.
In conclusion, the correlation results underscore the significance of regulatory compliance, credit sharing, and
Fintech credit adoption in shaping the financial performance of MFIs. These findings provide valuable insights
for MFIs and policymakers seeking to enhance the sustainability and resilience of the microfinance sector.
Linear Regression Analysis
In this section, we presented the results of a multiple regression for regulatory compliance, credit
sharing Fintech credit and overall financial performance of MFIs (Microfinance Institutions). The
regression coefficients for this multiple regression model provide coefficients of each independent
variable (Impact of Fintech credit, Impact of credit sharing, Regulatory compliance) as well as
ANOVA results.
Regression Coefficients between regulatory compliance, credit sharing, Fintech credit and the overall
financial performance of MFIs
Table 6:Regression coefficients
Coefficients
a
Model
Unstandardized Coefficients
Standardized Coefficients
t
Sig.
B
Std. Error
Beta
1
(Constant)
.716
.539
1.328
.041
Fintech credit
.562
.072
.545
7.806
.001
Credit sharing
.151
.152
.055
.993
.049
Regulatory compliance
.185
.110
.319
1.681
.042
a. Dependent Variable: Overall financial performance of your MFI
The regression coefficients provide insights into how each independent variable (Impact of Fintech credit,
Impact of credit sharing, Regulatory compliance) influences the dependent variable (Overall financial
performance of MFIs)
The constant term in the model represents the expected value of the dependent variable when all of its
independent variables are set to zero. In our case, this is 0.716, representing the baseline financial performance
of microfinance institutions (MFIs) in the absence of Fintech credit, credit sharing, and regulatory compliance
influences.
The coefficient for Fintech credit is 0.562, implying that, with all other factors held constant, a one-unit
increase in Fintech credit leads to a 0.562-unit rise in the financial performance of MFIs. This relationship is
statistically significant at the 0.001 level (p = 0.001), indicating a strong and positive association between
Fintech credit and MFI financial performance.
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Regarding credit information sharing, the coefficient is 0.151, which is statistically significant at the 0.05 level
(p = 0.049). This suggests a positive effect of credit sharing on financial performance. However, the smaller
coefficient compared to Fintech credit implies that while credit sharing positively influences performance, its
contribution is relatively modest.
The coefficient for regulatory compliance influencing the financial performance of MFIs is 0.185. This
coefficient is statistically significant at the 0.05 level (p = 0.042), indicating that regulatory compliance has a
positive effect on financial performance. However, similar to credit sharing, the effect size (0.185) suggests
that while regulatory compliance contributes positively, its impact may be smaller compared to Fintech credit.
The t-value for Fintech credit is large (7.806), and the p-value is highly significant (p < 0.05), meaning that
Fintech credit is a highly significant predictor of overall financial performance.
The t-value is small (0.993) for Credit sharing, but the p-value is just under 0.05, indicating that Credit sharing
is a statistically significant, albeit weaker, predictor of overall financial performance. The t-value is moderate
(1.681) for Regulatory compliance, and the p-value is significant (p < 0.05), meaning that Regulatory
compliance is a statistically significant predictor of overall financial performance.
The regression model highlights the significant positive influence of Fintech credit on the financial
performance of MFIs. Both credit sharing and regulatory compliance also have positive effects on financial
performance, although their impacts are relatively smaller compared to Fintech credit.
Based on the findings, MFIs should prioritize the adoption and integration of Fintech credit solutions to
enhance their financial performance.
Credit sharing and regulatory compliance are also important factors contributing to financial performance,
although their impacts may be less pronounced compared to Fintech credit.
Finally, the coefficients provide valuable insights into the relative importance of different factors influencing
the financial performance of MFIs. Fintech credit emerges as a significant driver of financial performance,
while credit sharing and regulatory compliance also play positive roles, though to a lesser extent. (This model
suggests that all three factorsFintech credit, Credit sharing, and Regulatory complianceare important
predictors of financial performance, with Fintech credit having the most substantial effect.)
The results for the multiple regression model is given by;
Y= 0.716 + 0.562F+ 0.151S + 0.185R
Where: Y
i
: Performance of Micro-Finance institutions
β
0
: is the y intercept
β1 and β
2
are the regression (beta) coefficients
F: Variable of Fintech Credit
S: Variable of Credit sharing
R: Variable of Banking regulations
ANOVA between Fintech Credit, Credit sharing, Banking Regulations and Financial Performance
The ANOVA table 2 provides information about the overall fit of the regression model and whether the
independent variables collectively significantly predict the dependent variable (Financial performance).
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Table 7: ANOVA results
ANOVA
a
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
4.531
3
1.510
4.468
.008
b
Residual
15.549
46
.338
Total
20.080
49
a. Dependent Variable: Financial performance
b. Predictors: (Constant), Banking regulations, Fintech credit, credit sharing.
The F-statistic tests the overall significance of the regression model by comparing the amount of variation
accounted for by the model (the regression sum of squares) with the amount of variation that remains
unaccounted for (the residual sum of squares).
In the case our findings, the F-value is 4.468, which indicates, a very significant correlation between those two
variables. The associated p-value is 0.008 which is less than the conventional level of significance of 0.05,
suggesting the model to be significant. This represents that the model appropriately explains the variation of
the response.
According to the results of the ANOVA, the regression model with the constant, banking regulations, Fintech
credit, and credit sharing, was a significant predictor of the financial performance of microfinance institutions
of (MFIs). This would imply that at least one of the independent variables has a significant e circumcision on
the financial performance of MFIs.
SUMMARY, CONCLUSION AND RECOMMENDATIONS
Introduction
This chapter concludes the study with the presentation of the summary of findings, study conclusions, and
recommendations that were derived from the study results and findings.
Summary
The study's first objective was to determine the effect of Fintech credit on the financial Upper performance of
MFIs in Kisumu City. From research performed in the study, results suggest that in general Fintech executives
enjoy favorable support from respondents; specifically regarding its impact on putting more money into the
hands of borrowers, such contributions to loan portfolios, improving financial performance and efficiency, and
enhancing customer satisfaction and engagement. However, there are areas, such as the repayment rate of
loans, where opinions are more divided or uncertain.
Other findings from fieldwork Focusing on the impact of credit sharing on financial performance, it is revealed
that its effect here is generally mixed among respondents, particularly regarding its impact on credit risk
assessment, lending efficiency, reduction of non-performing loans, financial performance, customer
satisfaction, and loan approval process efficiency. However, there are areas, such as the accuracy of credit risk
assessment and the impact on loan repayment rates, where opinions are more varied.
The findings suggest a mixed perception of the impact of banking regulations on MFIs, depending on how
much and in which way we can look within different layers to balance these effects. While there were most
certainly perceived benefits such as improved risk management practices and more stakeholder confidence
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among small business owners thus shares in their company being held by family members either directly or
through an intermediary.
On the other hand, there are also concerns regarding increased operational and compliance costs and their
impact on liquidity for MFI’s. Within the MFI’s, there are multiple compliance challenges and variable
perceptions of stability and pricing strategy to be noted. The four correlations presented in Table 15 highlight
the interconnectedness between regulatory compliance, credit sharing, Fintech credit adoption, and financial
performance in the MFI. While regulatory compliance sets the basic direction for a blueprint, both credit
sharing and Fintech credit adoption are important drivers of good consecutive years for MFIs.
The positive correlations implied that regulatory compliance, credit sharing, and Fintech credit adoption are
factors that contribute significantly to the overall financial health of MFIs. Institutions that effectively navigate
regulatory requirements while leveraging innovative practices such as credit sharing and Fintech integration
are likely to achieve superior financial performance.
MFIs should prioritize regulatory compliance as a fundamental aspect of their operations to ensure legal
adherence and mitigate regulatory risks. Investing in credit sharing initiatives and adopting Fintech credit
solutions can yield substantial benefits for MFIs in terms of enhancing financial performance. These strategies
enable MFIs to expand their reach, improve operational efficiency, and better serve their clients.
Policymakers and the microfinance industry needed to understand the need for creating an enabling regulatory
environment for innovation in microfinance, while not incorporating innovation that put consumers' welfare
and financial stability of the system at risk.
Regression analysis reveals a significantly positive impact of Fintech credit on the financial performance of
microfinance institutions (MFIs). Credit sharing and compliance work positively on financial performance but
marginal as compared to Fintech credit.
Based on these insights, MFIs will need to seek inclusion and integration of Fintech-based credit tools to
streamline their financial performance. Sharing someone else's credit and compliance with regulations are still
quite material even if they have become less so as compared with Fintech credit.
The interesting results of ANOVA show that interaction between banking regulation, Fintech credit and credit
sharing has a significant impact on the financial performance of MFIs. This underscores the need for a
multidimensional assessment comprising regulatory compliance and technological developments like Fintech,
in assessing and enhancing the financial capability of MFIs.
In conclusion, the ANOVA analysis confirms the overall significance of the regression model in predicting the
financial performance of MFIs based on the included predictors.
Conclusions
After doing a comprehensive analysis on effects of financial technology (Fintech) credit, credit sharing and
bank regulation on the performance of microfinance institutions (MFIs), a final report found several key
conclusions.
First of all: Fintech credit strongly affects the financial performance of MFIs. And secondly, though credit
sharing and bank regulation are also beneficial to MFI performance, their contribution is relatively small
compared to Fintech credit.
The correlation results indicate that compliance with regulation rules, credit sharing and Fintech credit
adoption are deemed noteworthy determinants of MFI performance differences among MFIs. This finding can
supply useful suggestions for MFIs and macro-economic policy designers who try to make the mini-credit
sector more sound resilient. The regression coefficients deliver valuable insight into the relative importance of
different factors affecting the financial performance of MFIs. Fintech credit plays a key role in finance,
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emerging as a significant driver of financial performance while credit sharing and regulatory compliance also
contribute positively.
Recommendations
MFIs should give priority to pursue Fintech credit solutions to enhance their performance. On the other hand,
efficiency of credit sharing and observance regulatory compliance is also important to financial performance,
but in comparison with Fintech credit, this scale may be smaller.
Further research and analysis are required in order to uncover how credit sharing, regulatory compliance and
financial performance are influenced by each other in the microfinance industry.
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Declaration
This research thesis is my original work and has never been presented for a degree award in any other
institution.
David Otieno Agom
MSC/BE/00016/2014
SIGNATURE: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. DATE. . . . . . . . . . . . . . . . . . . . ....
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This thesis report has been submitted for examination with my approval as a University Supervisor.
Dr. Peter Ndichu
Department Of Accounting And Finance Maseno University.
SIGNATURE. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...DATE: . . . . . . . . . . . . . . . . . . . . ..
List Of Abbreviations
MFI’s - Micro-finance institutions.
Fintech - Financial technology.
NBFC - Non-bank financial companies.
MSEs - Micro and small enterprises
CIS Credit Information Sharing.
OSS- Operational Self Sufficiency Ratio
ROA- Return on Asset
KWFT- Kenya Women Finance Trust
SMEP- Small and Micro Enterprises
P2P- Peer to Peer
TAT-Technology Acceptance theory