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Empirical Analysis on the Impact of Financial Technology at the

Financial System, Bank Age and Size, GDP and Inflation towards the

Performance of Commercial Banks in Malaysia, From 2004 to 2023

Liew Li Ling

, Caroline Geetha

Faculty of Business, Economics and Accountancy, University Malaysia Sabah

*Corresponding Author

DOI:

https://dx.doi.org/10.47772/IJRISS.2025.915EC00767

Received: 03 November 2025; Accepted: 08 November 2025; Published: 23 November 2025

ABSTRACT

This study examined the impact of financial technology (fintech) on the performance of commercial banks in

Malaysia. It evaluated the profitability of commercial banks, specifically looking at return on equity (ROE), after

integrating fintech into daily banking operations. The research framework included ROE as the dependent

variable, with fintech application, bank size, and bank age as independent variables, while Gross Domestic

Product (GDP) and inflation were controlled variables. To conduct the analysis, quantitative methods were

employed using secondary data from the financial reports of individual commercial banks between 2004 and

2023. A sample of eight local banks was selected, and the data were gathered from their financial statements

over this period. Statistical software such as Stata was utilized to analyse the data. A key focus of this study was

the application of financial technology, which was assessed using the Digital Innovation Index. To assess the

effects of fintech application, bank size and age, GDP and inflation towards the bank performance, panel data

analysis was applied. The analysis process began with unit root tests, followed by the diagnostic tests, including

checks for autocorrelation, normality, stability, heteroskedasticity, and multicollinearity to ensure the reliability

and validity of the model. The results indicated that financial technology, bank size, and GDP positively and

significantly influenced the performance of commercial banks, while bank age and inflation were found to have

no significant impact.

Keywords: financial technology, digital innovation index, performance, panel data

INTRODUCTION

Financial Technology (Fintech) is well-known as an application of technology within the financial industry that

covers a wide range of activities such as contactless payment, collecting financial data, performing financial

analysis, implementing financial software, practising digitized processes and electronic payment platforms

(Arner, Barberis & Buckley, 2015; Chong et al., 2019). These activities aren’t just being widely applied by

endusers for daily transactions, but also executed by financial institutions for daily operations as well.

The modern financial business began to take shape globally with the introduction of the first Automated Teller

Machine (ATM) in 1967, after the invention of key technologies such as telegraphs, railroads, canals, and

steamships that designed to accelerate financial transactions and facilitate the rapid transmission of financial

information (Arner et al., 2015). It marked the start of Fintech 2.0, a period from 1967 to 1987, during which the

financial services industry shifted from analogue to digital operations. Togetherness, this transformation

accelerated with the advent of mobile phones and the expansion of internet access that catalysed the digital

revolution (Mickalowski et al., 2008; Capatina & Draghescu, 2016), robo-advisory financial services such as

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Wealthfront that offers automated investment (Otinga, 2023), blockchain technology such as Bitcoin that runs

on decentralized ledger system, digital payment platform such as Square that provides mobile payment solutions

(Imam, 2019; James, 2018) that acted as the main categories of fintech application in financial system during

Fintech 3.0.

Moreover, Fintech developments were even more significant when entering the era of Fintech 3.5. M-Pesa was

introduced in Kenya, Alibaba from China began offering loans to small and medium-sized enterprises (SMEs)

on its e-commerce platform, Lufax as a peer-to-peer lending and wealth management platform was launched

(Arner, Barberis & Buckley, 2015). According to reports from KPMG (2017) and Leong & Sung (2018),

investments in FinTech companies reached $24.7 billion across 1,076 deals in 2016. The global cryptocurrency

market capitalization also tripled since 2016, reaching nearly $25 billion by March 2017 (Hileman & Rauchs,

2017; Leong & Sung, 2018). Additionally, the global crowdfunding industry reached $34 billion in 2015,

equivalent to £27 billion, representing a 2.1-fold growth compared to 2014 (Hobey, 2015; CrowdfundingHub,

2016; Leong & Sung, 2018). This highlights how deeply fintech has permeated the financial business landscape.

In Malaysia, the first ATM was launched by Malayan Banking Berhad in 1981. This instalment has made a

change in banking activities, from physical dealings to semi-automated functions until the digitalized

transactions. Along the transition of aging technology to innovative products, every commercial bank in

Malaysia is facing different level of obstacles in absorbing the fintech application because the age of commercial

banks is a crucial factor influencing their growth, as older institutions often face challenges such as the liability

of newness, difficulties in adapting to changing business strategies, and increased inertia and organizational

rigidities (Ikechukwu & Boniface, 2016; Coad et al., 2014). Referring to the present condition, the COVID-19

pandemic acted as a catalyst for the digitalization of Malaysia’s financial system, bridging the gap across

different age groups. The government-imposed lockdown further accelerated the significant rise in mobile app

downloads and usage across many commercial banks in Malaysia as they were eager to embrace new

technologies in order to sustain, enhance, or even boost their business and performance.

As Malaysia moves towards globalization, digital innovation has become a key factor in driving economic

growth because it helps the financial sector remain competitive (Konasilan & Murugiah, 2020; Bakar & Nordin,

2020; Khin & Ho, 2018; Tan et al., 2016). Digitalization reduces the operational workload and minimizes the

number of employees required in commercial banks, leading to cost savings on space and rent that would

otherwise be spent on expanding physical branches, which directly leaving an impact on internal economies of

scale (Fitriani & Renny, 2019). On the other hand, external economies of scale occur when commercial banks

expand their networks internationally, positioning themselves as multinational corporations seeking profit. By

establishing subsidiaries, these banks can tap into cheaper labour and grow their market share in host countries

(Mas, 2009). Thus, the size of commercial banks reflects their market power, allowing them to influence market

conditions, such as setting interest rates, dominating certain financial services, and leveraging economies of

scale.

Problem Statement

The rise in financial technology has led to the modernization of various financial instruments. Even though

fintech has certainly made a significant impact, but traditional commercial banks could not be replaced. Instead,

technology adoption has allowed banks to innovate and operate in new ways. The integration of technologies

such as mobile banking, internet banking, P2P lending, crowdfunding, robo-advisors, artificial intelligence (AI),

machine learning, and cloud computing has reduced the need for customers to physically visit bank branches,

providing more convenient alternatives (Malaysia Fintech Report 2021; Yusof et al., 2022; Varma et al., 2022;

Velmurugan, 2019). Additionally, blockchain, cryptocurrency, and stock exchange apps have emerged as

alternative investment options, giving customers more control over their financial decisions. These fintech

innovations have not only made financial services more accessible but also increased the customer base, injecting

more funds into commercial banks, thereby strengthening their financial positions and enabling them to invest

in assets and generate higher profits (Almulla & Aljughaiman, 2021).

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This increase in asset investments and the overall digitalization of the financial system can have broader

economic implications, impacting macroeconomic variables such as GDP and inflation. Investment serves as an

injection into the economy, stimulating GDP growth. However, as the circulation of money increases due to

fintech adoption, inflation tends to rise in line with GDP growth. A rapid expansion of the financial sector can

drive economic growth, but it may also lead to higher inflation, which can depress financial activity and increase

long-term costs, negatively affecting future growth and GDP (Andres et al., 2004). Rising inflation leads to

higher prices for goods and services, indirectly increasing the cost of living. This, in turn, can reduce demand

for financial products like savings accounts, as inflation erodes the real returns on savings (Choi, Smith & Boyd,

1996). Furthermore, excessive money creation can reduce the real return on assets, leading to lower trading

volumes in equity markets over time (Huybens & Smith, 1999). Consequently, commercial banks may face a

decline in profitability.

Additionally, integrating fintech with financial services is not a straightforward task. It is highly regulated, and

the development of digital solutions comes at a high cost (Beccalli, 2007). Commercial banks in Malaysia are

facing rising costs due to large capital investments in research and development as they work to remain

competitive in technology (Konasilan & Murugiah, 2020). This aligns with findings by Gutu (2014), who noted

that digital innovation often has negative effects on commercial banks' performance by increasing operational

costs, which can erode market share and reduce profits for commercial banks. This may prompt commercial

banks to take on more risk, potentially undermining their financial stability (Safiullah & Paramati, 2022).

Moreover, fintech’s emergence has particularly impacted larger commercial banks, which face challenges in

adapting to digital innovation due to the costs of restructuring legacy systems. In contrast, smaller commercial

banks may have an advantage as they can more quickly adapt to new technology and changes in the market

environment. However, other studies (Monika et al., 2021) argue that larger commercial banks benefit from

economies of scale, which help them minimize costs and improve profitability because they possess the

resources, expertise, and commitment to technological innovation. (Alex & Ngaba, 2018; Ripain & Ahmad,

2018).

The age of a commercial bank can also influence its performance, with younger banks typically being more

adaptable to technology (Phan, Narayan & Hutabarat, 2018; Ikechukwu & Boniface, 2016). Younger banks are

generally more open to technological innovations and willing to reorganize their operations to align with new

advancements. In contrast, older banks may struggle with inefficiencies and rigidities as they age, leading to

slower growth and increased operational costs (Coad et al., 2014; Loderer & Waelchli, 2010).

These challenges highlight the need for a digital innovation index tailored to evaluate the performance of

commercial banks, especially in the context of fintech integration. The absence of such an index underscores the

importance of creating a comprehensive framework that can assess how effectively commercial banks are

adopting and leveraging digital technologies. Thus, this research aims to fill this gap by developing a digital

innovation index for evaluating the impact of fintech, aligned with the changes in age and size of commercial

banks towards the performance of commercial banks in Malaysia.

Research Objectives

The specific research objectives of this study are:

1. To evaluate the impact of fintech application (digital innovation index) on the performance of the

commercial banks in Malaysia.

2. To measure the impact of the size and the age of commercial banks on the performance in the commercial

banks in Malaysia.

3. To measure the impact of GDP and inflation as the controlled variables that can influence the performance

in the commercial banks in Malaysia.

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LITERATURE REVIEW

Underlying Theories – Theory of Demand and Supply

Digital innovation within the financial sector is influenced by both demand and supply factors. On the demand

side, consumer awareness of fintech and the perceived value of its development saw a dramatic increase in

demand as fintech improves user experiences, greater convenience, higher speed, and user-friendly features,

(Haddad & Hornuf, 2018). This growth has surpassed the pace of traditional financial services, driving the

creation of new fintech products, including robo-advisor, artificial intelligence and machine learning, insurtech

and regtech (Shapiro et al., 2022). Furthermore, the demand for fintech services is closely tied to consumer

expectations, particularly among the tech-savvy generation, with the invention of smartphones and abundant

supply of internet.

On the supply side, fintech innovation is primarily supported by technological advancements, such as internet

application programming interfaces (APIs), smartphones for mobile banking, digital currencies, cloud

computing, and blockchain technology (Carstens, 2018). Mobile banking apps now have billions of users,

becoming an essential part of e-commerce services, including food delivery, ordering, and ride-hailing.

Additionally, lower entry barriers for fintech intermediaries and the increasing willingness to adopt fintech

innovations have fuelled the supply of these services, helping balance the financial market (Shapiro et al., 2022).

Underlying Theories – Theory of Innovation

The theory of innovation is also central to this research. The technological revolution has led to the establishment

of a new technological paradigm that impacts all major economic functions. Several key innovation theories

have been influential in shaping the global economy, including Schumpeter's concepts of creative destruction,

Kondratieff's long waves, the Diffusion of Innovation theory, Open Innovation, and Disruptive Innovation.

Schumpeter’s concept of creative destruction emphasizes the importance of innovation in driving economic

change. He argued that innovative entrepreneurship and technological advancements are central to economic

development (Naqshbandi & Garib Singh, 2015). Schumpeter believed that the ability to innovate is linked to

the size of the enterprise. He initially posited that smaller enterprises possess greater innovative potential due to

their agility and responsiveness to market changes. However, he later recognized that larger firms who benefiting

from economies of scale, access to capital and research capabilities would often have superior capacity to

develop and implement new technologies. This dual perspective provides a theoretical basis for examining bank

size as a factor in the relationship between fintech adoption and bank performance. Larger banks may have

greater financial and technological resources to invest in fintech innovations, while smaller banks may rely on

flexibility and niche adaptation to remain competitive.

Kondratieff's "long waves" theory highlights long-term cyclical fluctuations in commodity prices, rent, interest,

foreign trade, coal mining, and other sectors such as mass transportation, mass production, and communication

(Allianz Global Investors Capital Market Analysis, 2010). These waves reflect significant cycles in economic

activity, where changes in basic capital investments, while costly and time-consuming, bring long-term benefits.

Kondratieff theorized that each of these long cycles includes a cluster of innovations, driven by advancements

in science, technology, social conditions, and economic relations. These cycles are continuous processes that are

deeply connected to the prevailing economic conditions and innovations in various fields. In the context of

modern banking, fintech capabilities such as data analytics, blockchain, and mobile platforms serve as strategic

resources that enable banks to sustain competitive advantage during periods of technological transition. Banks

that effectively integrate fintech innovations are better positioned to leverage the opportunities presented by the

ongoing digital transformation wave.

Rogers' Diffusion of Innovation theory explores the way innovation spreads over time through a social system

via specific communication channels (Naqshbandi & Garib Singh, 2015). This theory examines what factors

influence invention, the rate at which innovations are adopted, and how new ideas are accepted within a given

culture (Dongol, 2021). Chesbrough's Open Innovation model contrasts with traditional theories by advocating

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that businesses benefit from using both internal and external knowledge. Open innovation encourages companies

to use external sources of information to enhance internal innovation processes and expand markets for external

applications (Naqshbandi & Garib Singh, 2015). From a fintech perspective, the diffusion process within the

banking industry is shaped by both internal and external factors. Internally, bank-specific characteristics such as

organizational size, structure, and age influence the rate and depth of fintech adoption. Larger banks may have

more resources and infrastructure to experiment with innovative technologies, while smaller banks may adopt

selectively but with greater agility and responsiveness. Externally, factors such as regulatory frameworks,

customer digital readiness, and economic conditions can either accelerate or hinder the diffusion process

(Dongol, 2021). This interplay between internal readiness and external context determines the trajectory of

fintech adoption and its ultimate impact on bank performance.

Finally, Disruptive Innovation theory, as articulated by Christensen, explains the way new technologies or

business models can disrupt existing markets, replacing established products or services with superior

alternatives. Christensen focused on how disruptive technologies create new markets and networks, gradually

displacing older technologies and altering established industries. This theory highlights how technological

advancements can upend the status quo, leading to the replacement of outdated products and systems with more

innovative solutions. From a fintech perspective, disruptive innovation provides a theoretical foundation for

understanding the way emerging financial technologies challenge the traditional banking systems. Fintech

innovations such as peer-to-peer (P2P) lending, blockchain-based transactions, mobile payments, and digitalonly

banking platforms exemplify the disruptive process. Initially, these technologies catered to unbanked or

underbanked populations and small enterprises excluded from conventional financial systems. But however, they

have increasingly captured mainstream markets while threatening to erode the dominance of traditional banks as

fintech solutions have matured. This process mirrors Christensen’s model where disruptive entrants exploit new

technologies and cost efficiencies to challenge incumbent institutions.

Fintech Adoption in Malaysia

As the demand for digital finance and fintech products continues to rise, Malaysia is embracing the digital

transformation. According to the 12th Malaysian Plan, it is expected that the digital economy will contribute

25.55% to the country's GDP by 2025 (Fintech Roundtable Report, 2021). To support this shift, the Malaysian

government launched "MYDIGITAL," the Malaysian Digital Economy Blueprint, aiming to establish Malaysia

as a digitally-driven nation in the global economy.

According to Bank Negara Malaysia, the penetration rates of internet banking and mobile banking had reached

119.5% and 69.7%, respectively, by September 2021. Internet banking penetration grew from 97.6% in 2019 to

112.5% in 2020, and continued to rise to 119.5% in 2021. Meanwhile, mobile banking penetration increased

from 52.9% in 2019 to 61.8% in 2020, and reached 69.7% by September 2021. Additionally, the number of

individuals making digital payment transactions reached 13.1 million, and the total annual revenue from

digitally-enabled consumer payments had grown to RM12.3 million as of January 2021. The value of internet

banking transactions also saw consistent growth from 2016 to 2020, with amounts increasing from RM494.4

billion in 2016 to RM1,060.9 billion in 2020 (source: Bank Negara Malaysia, Statista 2021). Similarly, mobile

banking transactions more than doubled over the same period, growing from RM36.2 billion in 2016 to RM458.9

billion in 2020.

A VISA study revealed that Malaysia ranked second in Southeast Asia for the adoption rate of cashless payments,

at 96% (Visa Consumer Payments Attitude Study 2021). Since the onset of the Covid-19 pandemic, digital and

mobile wallets such as GrabPay, Boost, and Touch 'n Go have become increasingly popular and are now the

norm in Malaysia. The contribution of the digital economy to GDP grew to 20% in 2020, up from 19.1% in 2019

(Department of Statistics Malaysia, Bank Negara Malaysia). The number of consumers using digital and mobile

wallets for online and in-store shopping has also surged, making these payment methods the third most

commonly used after cash/bank transfers and credit cards. According to Bank Negara Malaysia, bank transfers

accounted for 28% of the 2020 e-commerce payment mix, followed by credit cards at 17%, and digital/mobile

wallets at 14%. Other payment methods, including cash on delivery, direct debit, and debit cards, each made up

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12%, with charge/deferred debit cards and prepaid cards contributing 2% and 1%, respectively. The remaining

2% accounted for other payment methods.

METHODOLOGY

The study relies on quantitative approach using the panel data analysis to measure the impact of fintech

application, bank size and age, GDP and inflation towards the performance of commercial banks in Malaysia,

from 2004 to 2023. The eight selected local commercial banks are Affin Bank Berhad, Alliance Bank Malaysia

Berhad, Ambank Berhad, CIMB Bank Berhad, Hong Leong Bank Berhad, Malayan Banking Berhad, Public

Bank Berhad and RHB Bank Berhad. The data was collected on an annual basis from financial statements of

selected commercial banks and sources from Bank Negara Malaysia.

Initially, the data undergo preliminary testing to verify its reliability and accuracy. To verify the robustness of

the regression analysis, the panel data was first tested using the Pooled Ordinary Least Squares (OLS) model as

an initial assessment, followed by the Breusch-Pagan LM test to confirm the suitability of the panel structure.

Once the correct model framework was determined, the Hausman test was conducted to decide between the fixed

effects and random effects models. The fixed effects model accounts for time-invariant characteristics specific

to each country, whereas the random effects model assumes no correlation between these characteristics and the

explanatory variables. Based on the Hausman test results, the study adopts the fixed effects model if it proves

more appropriate. Otherwise, the random effects model is utilized. Next, Levin, Lin and Chu (LLC) unit root

test is applied to check for stationary to avoid any spurious regression. If the LLC test reveals non-stationarity,

the data will be converted to first difference or higher degree difference to make it stationary.

To confirm the reliability of the regression results, the study performs diagnostic tests to detect potential issues

such as multicollinearity, heteroskedasticity, and autocorrelation. Multicollinearity is assessed using the Variance

Inflation Factor (VIF), heteroskedasticity is tested with Breusch-Pagan Test and autocorrelation is examined

through the Baltagi-Wu LBI Test. If any of these problems are identified, the study applies suitable corrections,

either by using robust standard errors or the generalized least squares method to ensure the validity of the

statistical inferences

Digital Innovation Index

The digital innovation index is constructed using three main components, which include technological

innovation, growth potential, and mobile banking penetration. In this study, technological innovation is assessed

by examining the bank’s expenditure on computers, software, and equipment, as it reflects the adoption of new

technologies to enhance existing services and products. Growth potential is a commonly used indicator of

financial performance, is measured through earnings per share (EPS), which provides insight into the bank’s

future expansion prospects. Meanwhile, mobile banking penetration which represents the extent of mobile

banking adoption within the financial system, is measured by the volume of mobile banking transactions per

capita. The weightage assigned to technological innovation, growth potential and penetration of mobile banking

is 30%, 20% and 50% respectively.

Estimation Model

To examine the impact of fintech application, bank size and age, GDP and inflation towards the performance of

commercial banks in Malaysia, this study transformed the economic functions into a static panel as below:

Yit= β0 + β1itX1it + β2itX2it + β3itX3it + β4itX4it + β5itX5it + εit

represents the financial performance, measured by Return on Equity (ROE), calculated as net income divided

by shareholders’ equity. X

1it

denotes the digital innovation index, which consists of three components:

technological innovation (measured by expenditure on computers, software, and equipment), growth potential

(measured using earnings per share), and mobile banking penetration (measured by mobile banking transaction

volume per capita). X

2it

refers to the size of commercial banks, measured by total assets, while X

3it

represents

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the age of the banks, measured by the number of years in operation. X

4it

and X

5it

correspond to GDP and inflation,

respectively, both serving as control variables. β

is the intercept of the regression, indicating the baseline level

of Y

when all independent variables are zero. β

through β

are the coefficients of the explanatory variables,

each reflecting the marginal effect of a one-unit change in its corresponding variable while holding others

constant. The subscript i denotes the cross-sectional unit (different commercial banks), and t represents the time

dimension (years). Finally, ε

is the error term, capturing unobserved factors at a 5% confidence level.

Hypotheses

Based on the underlying theories and the previous empirical studies, the hypotheses derived in the study are as

follows:

: Fintech applications can influence the performance of the commercial banks in the short run and long run

in Malaysia.

: Size of commercial banks can influence the performance of the commercial banks in the short run and long

run in Malaysia.

: Age of commercial banks can influence the performance of the commercial banks in the short run and long

run in Malaysia.

: GDP can influence the performance of the commercial banks in the short run and long run in Malaysia.

: Inflation can influence the performance of the commercial banks in the short run and long run in Malaysia.

FINDINGS

Hausman Test Result

The hypothesis for the Hausman test is stated that H₀: Random effects model is preferred (individual effects are

uncorrelated with regressors), while H₁: Fixed effects model is preferred (individual effects are correlated with

regressors).

Table 6.1 shows the test statistics for the Hausman test

Statistic

Value

Chi-Square (χ²)

3.54

Degrees of Freedom (df)

p-value

0.7392

Since the p-value (0.7392) > 0.05, the study fails to reject the null hypothesis. This means that the Random

Effects (RE) model is appropriate for the analysis. The Hausman test results indicate that the random effects

model is more suitable than the fixed effects model for examining the impact of fintech application, bank size

and age, GDP and inflation towards the performance of commercial banks in Malaysia.

Preliminary Testing Result.

Table 6.2 shows the Unit Root test results at level, first difference and second difference

Variables

At level

At First Difference

At Second Difference

Technological Innovation

-4.4921***

(0.0000)

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Earning per Share

-2.0792**

(0.0188)

Mobile Banking

13.3863

(1.0000)

1.7023

(0.9556)

-1.9658**

(0.0247)

Total Asset

1.1227

(0.8692)

-1.2229

(0.1107)

-4.7246***

(0.0000)

Age

-2.8284

(0.9977)

-7.5167***

(0.0000)

GDP

-3.5227***

(0.0002)

Inflation

-6.2185***

(0.0000)

Return on Equity

2.6841

(0.9964)

-0.0004

(0.4998)

-4.1131***

(0.0000)

p-values are presented in parentheses, where

***

and

indicate significance at 1%, 5% and 10% level

respectively.

Table 6.2 presents the results of the unit root analysis using the Levin, Lin, and Chu (2002) test, which is a panel

unit root test conducted to examine the null hypothesis of a unit root against the alternative hypothesis of

stationarity, considering cross-sectional interdependence among the data. The test result indicates that only four

variables, technological innovation, GDP and Inflation are stationary at 1% significance level, while earning per

share is stationary at 5% significance level. In contrast, mobile banking, total asset, age and return on equity are

not stationary at level. The probability value (p-values) held by the non-stationary variables are all near to 0.10

(10% significance level). Thus, it concludes that there is a unit root within the data of these variables. The

probability values (p-values) of those non-stationary variables are 1.0000 for mobile banking, 0.8692 for total

asset, 0.9977 for age and 0.9964 for return on equity. All the p-values are near to the maximum significance level

of 10% or 0.1.

Therefore, using the same panel unit root test of Levin, Lin and Chu (2002) the transformed data of first

difference is tested for stationarity. The first difference result shows that age is stationary at 1% significance level

and does not contain unit root given the p-values of 0.0000. The second difference result shows that all the

variables in the study are stationary and do not contain unit root given the p-values of 0.0247 for mobile banking,

0.0000 for total asset and 0.000 for return on equity. The p-values at the second difference are stationary at 5%

significance level for mobile banking, while 1% significance level for total asset and return on equity. Thus, the

analysis could proceed to the diagnostic tests to confirm the reliability of the regression results.

Diagnostic Test Result.

The diagnostic test results for multicollinearity, heteroscedasticity, and serial correlation assess the selected data

and its components to identify any potential issues that may affect the reliability and accuracy of the study’s

findings. The Mean Variance Inflation Factor (VIF) indicates the extent to which the variance of a regression

coefficient is inflated due to correlations among the predictor variables. If the value is 1, it indicates that there is

no correlation between the predictors. If the value of VIF is above 5 or 10, it indicates problematic

multicollinearity. In this study, the Mean Variance Inflation Factor (VIF) value is 1.06, which is well below the

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threshold of 10, indicating that the independent variables are not strongly correlated with one another. Therefore,

the regression coefficients can be interpreted with a high level of confidence. (Refer to Table 6.2).

Heteroscedasticity refers to the condition where the variance of errors is not constant across all levels of an

independent variable. The Breusch-Pagan Test checks for this issue in a regression model. The test statistics

result revealed a value of 282.06 (p-value = 0.0000) which is smaller than 0.05 threshold of significance. It can

be stated that there is significant evidence of heteroskedasticity in the model. Thus, Cluster Robust Standard

Errors (CRSE) is applied. After the statistical adjustment made by CRSE, the Wald chi

gives a value of 83.99

with p-value of 0.0000. This means the variance of the residual is constant across observations and it confirms

the assumption of homoscedasticity. (Refer to Table 6.3)

Serial Correlation occurs when the residual errors from the regression model are correlated across the

observations, which can violate the regression assumptions. The Baltagi-Wu LBI’s test statistics of -3.4189

(pvalue = 0.0112). This indicates a serial correlation in the model’s residual. Serial correlation takes place when

the errors from one period is correlated with those from another period, which can lead to unbiased estimation.

Thus, Feasible Generalized Least Square (FGLS) is applied to address serial correlation problem detected. After

the statistical adjustment made by FGLS, the Wald chi2 gives a value of 50.53 with p-value of 0.0000. This

means it has no serial correlation in the model residuals, the model’s error terms are independent and the

regression result are reliable. (Refer to Table 6.3)

Table 6.3 shows the diagnostic test results

Diagnostic Test

Test Statistics

p-value

Hypothesis

Variance Inflation Factor

(VIF)

Mean VIF = 1.06

Accept Ho. There is no

multicollinearity

Cluster Robust Standard

Error (CRSE)

83.99

0.0000

Accept Ho. There is no

heteroscedasticity

Feasible Generalized Least

Square (FGLS)

50.53

0.0000

Accept Ho. There is no

serial correlation

Model Estimation Result.

Table 6.4 shows the model estimation results

Independent Variables

Coefficient (Standard error)

Hypothesis

Technological Innovation

26.000

(0.331)

Fail to reject Ho at 10% significance

level

Earning per Share

-0.1270**

(0.019)

Reject Ho at 5% significance level

Mobile Banking

7.870*

(0.0776)

Reject Ho at 10% significance level

Total Asset

0.0849***

(0.001)

Reject Ho at 1% significance level

Age

1.3800

(0.205)

Fail to reject Ho at 10% significance

level

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GDP

-2.7200**

(0.011)

Reject Ho at 5% significance level

Inflation

0.7570

(0.297)

Fail to reject Ho at 10% significance

level

The p-value is presented in the parentheses where

***

and

indicate 1%, 5% and 10% significance level

respectively.

Table 6.4 provides the model estimation results. The coefficient for technological innovation is 26.000 (p-value

= 0.331) indicates an insignificant relationship. There are several reasons why technological innovation,

including software and hardware advancements may not have a significant impact on the performance of

commercial banks. Firstly, the high cost of technology investments can be a major factor since capital-intensive

upgrades such as advanced systems and cybersecurity infrastructure require substantial upfront spending. In

addition, hidden costs like ongoing maintenance, periodic system upgrades, and license fees can reduce net

returns. Since technology investments often take several years to generate measurable outcomes, immediate

performance improvements are unlikely. Secondly, stringent regulatory and compliance requirements may limit

the efficiency gains from technological innovation. Compliance with data protection, privacy laws, and banking

regulations often increases operational complexity and costs as new technologies typically demand extra

oversight and documentation, consuming additional time and resources. Lastly, the introduction of new or

innovative technologies can heighten risk exposure. Complex systems may create new vulnerabilities that could

cause potential data breaches, cyberattacks, or hardware failures and thus, affecting overall bank performance.

Next, the coefficient for earning per share (EPS) is -0.1270 (p-value = 0.019) indicates a significant relationship.

The negative significant relationship between EPS and return on equity (ROE) suggests that a one-unit increase

in EPS leads to a 0.1270-unit decrease in ROE. Several factors may explain why higher EPS can negatively

affect the performance of commercial banks. Firstly, an increase in EPS often reflects a focus on short-term profit

maximization which may result from cutting long-term investments such as R&D, cybersecurity, and employee

training. While this approach can temporarily raise EPS, it also increases the risk of future losses and weakens

long-term performance. Secondly, shareholders typically demand higher dividends when EPS rises. This can

strain the bank’s capital reserves, as excessive cash distributions reduce available funds for lending and

investment activities. This issue was evident during the 2008 Global Financial Crisis, when banks with high EPS

levels often exhibited lower capital adequacy ratios. Lastly, EPS growth is sometimes achieved through

operational downsizing including branch closures and workforce reductions. Such measures may undermine

customer confidence, diminish service quality, and lead to deposit withdrawals, which can ultimately weaken

the overall performance of commercial banks.

After that, the coefficient for mobile banking is 7.870 (p-value = 0.0776) indicates a significant relationship. The

positive and significant relationship between mobile banking and return on ROE indicates a modest positive

effect, where a one-unit increase in mobile banking corresponds to a 7.780-unit rise in ROE. Several factors help

explain this relationship. Firstly, cost efficiency and operational savings play a major role. Mobile banking

reduces reliance on physical branches and manual transactions, lowering operational expenses and allowing

banks to serve more customers at a lower marginal cost. This efficiency enhances profitability through improved

net interest margins and returns. Secondly, financial inclusion contributes to the positive effect. By reaching

unbanked and underbanked populations, mobile banking expands the customer base and transaction volumes.

This growth translates into higher sales of loans, insurance products, and fee-based income from digital

transactions, thereby strengthening overall bank performance. Thirdly, enhanced customer satisfaction from the

convenience of mobile services helps reduce customer attrition. Satisfied customers tend to remain loyal,

maintaining stable deposits and reducing sensitivity to interest rate fluctuations, which supports consistent bank

performance.

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Subsequently, the coefficient for size of commercial banks is 0.0849 (p-value = 0.001) indicates a significant

relationship. The size of commercial banks showed a positive and significant relationship with their performance.

The positive coefficient (0.0849) indicates that an increase in total assets is associated with an improvement in

ROE, with a one-unit rise in total assets corresponds to a 0.0849-unit increase in ROE, assuming other variables

remain constant. Several factors explain this significant positive relationship. Firstly, economies of scale enable

larger banks to distribute fixed costs over a wider asset base, thereby reducing the average cost per unit of asset

and enhancing profitability. Secondly, greater diversification opportunities come with larger asset bases,

allowing big banks to spread their risks across varied loan portfolios and investment activities, which can

stabilize earnings and improve returns. Thirdly, market power advantages allow larger banks to negotiate more

favourable terms on loans and deposits, strengthening their net interest margins and boosting overall profitability.

Lastly, investment capacity in advanced technology gives larger banks the ability to adopt innovative systems

that improve operational efficiency, enhance customer service, and ultimately contribute to stronger financial

performance.

Consequently, the coefficient for age of commercial banks is 1.3800 (p-value = 0.205) indicates an insignificant

relationship. The age of commercial banks, measured by their years of operation was found to have no significant

effect on ROE as the p-value is 0.205. Several factors may explain this finding. Firstly, the neutral balance

between experience and rigidity could be a reason. On one hand, older banks benefit from accumulated

experience, strong customer relationships, and established reputations that could enhance performance. On the

other hand, they may suffer from organizational rigidity, reduced innovation, and slower adaptation to

technological and market changes, which can hinder growth. These opposing effects may offset each other,

resulting in no overall impact of age on performance. Secondly, industry homogenization plays a role. In

Malaysia’s highly regulated banking environment under Bank Negara Malaysia, most commercial banks follow

similar operational standards, technologies, and compliance practices. Moreover, collaboration with fintech

companies has narrowed technological gaps between older and newer banks, making age less relevant to

performance outcomes. Lastly, the life cycle effect could explain the lack of significance. Banks may experience

performance improvements as they grow from new to mature institutions but eventually face decline as they

become older and less adaptable. This life cycle pattern makes it difficult to capture a consistent linear

relationship between age and the performance of commercial banks.

Furthermore, this study included gross domestic product (GDP) and inflation as control variables. The results

showed that GDP had a negative significant relationship with the performance of commercial banks, while

inflation had no significant effect. For GDP, the negative coefficient indicates that a one-unit increase in GDP

was associated with a 2.7200-unit decline in ROE. This suggests that economic growth may weaken bank

performance paradoxically. One possible reason is risky lending behaviour during periods of economic

expansion. When the economy grows, banks may become overly optimistic, extending credit to riskier or

unqualified borrowers in pursuit of short-term profits. Over time, this can lead to higher levels of non-performing

loans (NPLs), increased default rates, and the eventual formation of credit bubbles, all of which erode

profitability. Additionally, macroeconomic policy responses during economic booms may contribute to this

negative effect. As GDP increases, Bank Negara Malaysia often tightens monetary policy by raising interest rates

to control inflation. Higher interest rates can raise borrowing costs, reduce loan demand, and increase default

risks due to heavier debt burdens, ultimately lowering banks’ returns even in a growing economy. In contrast,

inflation showed no significant impact on bank performance, likely due to offsetting effects. On one hand, higher

inflation can boost interest income as banks raise lending rates. On the other hand, it may also lead to more loan

defaults if borrowers struggle with increased repayment costs. These opposing forces can neutralize each other,

resulting in an insignificant overall effect. Moreover, commercial banks are typically well-adapted to inflationary

conditions. They can adjust quickly by repricing loans and deposits, using floating-rate contracts, investing in

inflation-hedged assets, and employing risk management strategies such as portfolio hedging. Such adaptive

measures help stabilize profitability, making inflation’s impact on performance negligible.

Digital Innovation Index

Technological innovation, growth potential (measured by earnings per share), and mobile banking serve as the

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primary components in constructing the Digital Innovation Index (DII). Each component is assigned a different

weight: technological innovation (X

) contributed 30%, growth potential (X

) contributed 20%, and mobile

banking (X

) contributed 50% to the overall impact on the performance of commercial banks. The index that is

computed by using the Min-Max Normalization method, is expressed as:

DII = 0.3 × X

norm + 0.2 × X

norm + 0.5 × X

norm

Xnorm is the normalized value between 0–1, showing where that observation stands relative to others. X is the

actual raw value of the indicator for a specific observation.

Table 6.5 shows the Digital Innovation Index results

Bank

X1norm

X2norm

X3norm

DII

Affin Bank Berhad

0.2219

0.0253

1.0000

0.5717

Alliance Bank Malaysia Berhad

0.1308

0.0426

1.0000

0.5478

Ambank Berhad

0.0961

0.0463

1.0000

0.5380

CIMB Bank Berhad

0.6174

0.0508

1.0000

0.6954

Hong Leong Bank Berhad

0.5275

0.0741

1.0000

0.6731

Malayan Banking Berhad

0.5860

0.0543

1.0000

0.6867

Public Bank Berhad

0.1164

0.0679

1.0000

0.5485

RHB Bank Berhad

0.4991

0.0508

1.0000

0.6599

Based on the Digital Innovation Index (DII), CIMB Bank Berhad (0.6954), Malayan Banking Berhad (0.6867),

Hong Leong Bank Berhad (0.6731), and RHB Bank Berhad (0.6599) are identified as the digital leaders. These

banks demonstrate strong technological innovation capabilities, with scores ranging from 0.4991 to 0.6174,

reflecting higher investment levels and more advanced adoption of software and digital systems. In contrast,

Public Bank Berhad (0.5485), Alliance Bank Malaysia Berhad (0.5478), Affin Bank Berhad (0.5717), and

AmBank Berhad (0.5380) are relatively behind in technological innovation, with lower scores ranging between

0.0961 and 0.2219. Regarding mobile banking, all eight commercial banks achieved a normalized score of 1.000,

showing that each has reached the maximum benchmark in mobile banking performance. However, for growth

potential, the scores across all banks are relatively low and closely clustered, suggesting that this factor

contributes minimally to the overall DII. Overall, the average DII score of 0.6151 indicates that the eight

commercial banks, on average, are operating at 61.51% of the maximum observed digital innovation capacity,

reflecting a moderate but growing level of digital advancement within Malaysia’s banking sector.

CONCLUSION

Assessing the impact of financial technology on the performance of commercial banks in Malaysia is crucial for

bankers, investors, and customers. In the contemporary banking environment, financial technology has become

a key driver of profitability, efficiency, and competitive advantage. For customers, digital innovation enhances

convenience by streamlining, accelerating, and simplifying banking operations, ultimately improving their

overall banking experience. This study addressed three key research questions, (1) whether the adoption of

financial technology significantly improves the performance of commercial banks in Malaysia; (2) whether

bank-specific characteristics, such as size and age, influence their performance; (3) how macroeconomic factors,

namely gross domestic product (GDP) and inflation, affect bank performance. Using secondary data from eight

commercial banks over a 20-year period, the analysis examined the effects of fintech adoption, bank size, and

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bank age while including GDP and inflation as control variables. The results revealed that fintech adoption, bank

size, and GDP exerted a positive and significant influence on the performance of commercial banks, whereas

bank age and inflation showed no statistically significant effect. These findings suggest that fintech plays a

pivotal role in enhancing bank profitability and operational efficiency, particularly among larger banks with

greater resources to invest in digital transformation.

Additionally, future studies should consider examining the moderating effects of financial technology on the

relationship between bank-specific and macroeconomic factors towards bank performance. For instance, fintech

adoption could moderate the relationship between bank size and profitability which potentially allowing smaller

banks to compete more effectively with larger institutions by leveraging digital platforms, automation, and data

analytics. Similarly, fintech may influence the way macroeconomic conditions like GDP growth or inflation in

affecting banks’ financial outcomes through agile risk management and cost control mechanisms. Moreover,

future research could explore variations in fintech’s moderating impact across different types of banks, such as

domestic versus foreign-owned or Islamic versus conventional across various fintech domains which include

digital payments, blockchain, artificial intelligence and online lending. Such studies would not only broaden the

theoretical understanding of fintech’s role in the banking ecosystem but also offer practical implications for

policymakers and bank managers who aim to optimize technology-driven performance strategies in Malaysia

and beyond.

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