Factors Influencing Loan Defaults in Tanzania: A Case Study of CRDB, NMB, TPB, and EXIM in Iringa Municipality

Factors Influencing Loan Defaults in Tanzania: A Case Study of CRDB, NMB, TPB, and EXIM in Iringa Municipality

Reinfrida Nahonyo, David Mosoma

Department of Accounts and Finance, Ruaha Catholic University, Iringa Tanzania

DOI: https://dx.doi.org/10.47772/IJRISS.2025.907000125

Received: 26 June 2025; Accepted: 30 June 2025; Published: 02 August 2025

ABSTRACT

This paper explored the determinants of loan defaults among the CRDB, NMB, TPB and EXIM bank customers in Iringa Municipality, Tanzania. A mixed method approach was used and 140 borrowers and 8 banking employees were questioned and interviewed. Quantitative analysis used descriptive statistics and correlation and regression analyses; qualitative data was collected through in‐depth interviews. The result suggests that bank specific variables such as high interest rate, weak credit appraisal system and lack lustering in monitoring are the major cause of default. Borrower-side factors such as the use of loan funds and unemployment, and macro factors such as inflation and income tax, also play significant roles. The analysis is informed by theories derived from the Individual Lending Model and the Theory of Planned Behavior. Although the study was conducted in one town, it has implications for wider policy measures in Tanzania. The study calls for strengthening credit appraisal, training and policy overhauls to control such defaults.

Keywords: Loan default, credit risk, bank lending, borrower behavior, Tanzania

INTRODUCTION

Loan delinquency is a chronic problem, which poses an imminent hazard on the stability and viability of financial institutions worldwide. Surging loan defaults in Tanzania have piled massive challenges on the shoulders of the banks, dampened investors’ confidence and led to the constriction of lending opportunities for would-be borrowers. This is eroding FDI as the main source of long-term financing for capital-intensive projects and it also undermines the core business of banks, i.e. lending, and increases their credit risk exposure (Waweru & Kalani, 2009).

Loan default, in most cases, is the inability of borrowers to meet debt service payments for over 90 days and thus interrupting of cash flow to lending institutions (Alton & Hazen, 2001). Previous researches (eg Magali, 2013; Saba et al., 2012) find loan defaults to be due to bad credit risk management, inadequate evaluation of borrowers’ worthiness and also macroeconomic situation. Borrower delinquency, lack of monitoring and loan diversion are also mentioned in other regional studies in Kenya and Nigeria as major causes (Bichanga, 2013; Felix et al., 2018).

Even though there are risk mitigation measures in place, such as credit guarantees, insurance programmes, and tighter lending measures, defaults still abound. This in turn warrants the need of micro level empirical studies to understand the determinants of loan default at district level. This paper thus, focuses on CRDB, NMB, TPB, and EXIM banks in Iringa Municipality and aimed at investigating how borrower characteristics, bank-specific factors and macroeconomic variables interact to determine default behavior.

Building on the Model of Individual Lending and the Theory of Planned Behavior, this study helps to have a stronger theoretical and data-based discussion. Although the location in question is restricted the results provide practical guidance to financial institutions, policy makers, and the borrowers for all of Tanzania.

THEORETICAL FRAMEWORK

Individual Lending Model

This lending model subscribes to the assumption that since loans are directly given to the borrower, it is therefore the sole responsibility of the borrower to make full payments of the principal amount plus interest without soliciting financial support from a group in case they default. It is however recommended that banks should provide technical assistance, payment schedules and business management training to borrowers (Hazeltine and Bull, 2003).

As observed by the individual lending model, banks place themselves at great risk whenever they grant loans to individual borrowers since institutions cannot compel other individuals to make repayments in case of loan defaults. Lending involves a lender providing a loan in return for a promise of interest and principal repayment in future (Kay, 2005). Owing to the risk of default in loan repayment, lenders should assemble all relevant information regarding individual borrowers, for accurate screening of their credit worthiness. Some of the factors that the MFIs consider prior to granting loans include: the borrower’s character, amount requested by the client, loan purpose, borrower’s business management ability, repayment source and insurance. A major observation is that this model is costly and requires mass recruitment of loan officers who closely monitor individual borrowers, and engage in extensive field research to identify credit worthy borrowers, especially those who do not possess tangible collateral(s) or credit history.

Planned Behavior Theory

The Planned Behavior Theory postulates that human action is mainly guided by; behavior beliefs, normative beliefs and control beliefs. Behavioral belief refers to one’s conviction regarding the likely results of a specific behavior and their evaluation of the same, while normative beliefs center around normative expectation of other people and one’s motivation to comply with them, and control beliefs are the beliefs about the existence of factors that may affect the performance of the behavior (Azjen, 2009).

Behavioral belief is often attributed to the favorable or unfavorable attitude towards a specific behavior, while normative beliefs significantly contribute to one’s development of subjective norms and perceived behavior control is attributed to control beliefs. Attitude pertaining to behavior, subjective norms and perceived behavioral control leads to the formation of certain behavioral intentions. The prime contribution of the behavioral model is that despite the lack of an absolute relationship between behavioral intention and actual behavior, intention remains a credible measure of human action in the theory of planned behavior.

The theory of planned behavior guided the current study, and was used to explain the probable behaviors of loan borrowers in various scenarios. Using this model therefore, we investigated the influence of borrowers, bank specific factors and macroeconomic factors particularly; business skills, diversion of loan use, loss of jobs, income source of repayment, amount requested, interest rates, credits assessment, lending policies, credit size, loan software, staff skills, inflation rates, unemployment rates, natural disasters and exchange rates.

Integration with the Study

Both theories offer complementary insights. The Institutional and Procedural Lending dimensions of lending are anchored in the Individual Lending Model, while the Theory of Planned Behavior accounts for borrower motives and action departures. Together they paint a comprehensive picture that goes between borrower behavior, bank practices and the wider economic environment.”

In the application of these theories in the collection and analysis of data, it allowed the researchers to conceptualize the survey and interpret findings more analytically. References to these approaches in the discussion and conclusion sections provide a point of reference for the results and a justification for interventions to improve lending practices and borrower performance.

METHODOLOGY

The mixed approach, that allows for the application of both quantitative and qualitative research approaches was employed to investigate factors influencing loan defaults. The quantitative research approach was used to obtain respondents’ views on the causes of loan defaults while the qualitative research approach was used to accrue supplementary data following the quantitative phase. The study constituted a two-phase process, with the first phase including; the collection and analysis of quantitative and later qualitative data. The descriptive survey research design as used in the current study enabled the researchers to collect information from a cross- section of a given population and the cross-sectional design as used in the current study enabled the collection, analysis and interpretation of data at a single point in time (Creswell and Clark, 2011).

 The study sample constituted of; borrowers, credits officer/loan officers, commercial managers/team leaders, branch managers selected from a population of 246. Overall, 3 banks located in Iringa municipality participated in the current study and from these, 140 borrowers and 11 loan officers constituted the sample that informed the study. Both purposive and non-purposive sampling techniques were applied and out of the 3 districts in Iringa region, Iringa municipality was purposely selected, based on the adequate availability of financial institutions and their beneficiaries. The second phase of the sampling procedure involved the purposive selection of CRDB, NMB, EXIM and TPB out of 7 banks that are operational in Iringa municipality, owing to the fact that they are the biggest banks in the study area that use both the individual and group-based lending models. Similarly, 2 key informants were purposively selected from each of the selected banks and these included; branch managers, credit officers and loan officers, totaling to 8 key informants. The sample size was convenient according to Creswell (2009) who recommends a qualitative sample of 4 to 10 respondents.

The selection criteria of all 8 key informants were mainly based on their substantive knowledge regarding the factors influencing the likelihood of loan default in the study area. Lastly, the non-purposive sampling technique was used to simple randomly select a total of 140 loan borrowers, from CRDB, EXIM NMB and TPB in fairly equal proportions.

Primary data was directly collected from loan borrowers and bank personnel using a structured questionnaire which sought information on; borrower’s specific factors (business skills, funds diversion, illness, level of income, asset ownership and marital status), bank factors(interest rates, credits assessment, lending policies and credit size) and macroeconomic factors(inflation rate, unemployment rate, natural disasters, exchange rates and income tax). Secondary data was obtained through documentary review of sources such as: banks annual reports on loan defaults, journals (published and unpublished) and websites. Unpublished documents on management and supervisors’ reports concerning loan defaults and published documents such as Annual Financial reports, journals and related documents were reviewed. Only secondary data on loan defaulters as of 2015 to 2020 from the individual lending model was taken into consideration.

Percentages and frequencies were the main descriptive statistics used to analyse quantitative data regarding; demographic characteristics, bank specific factors, borrower-specific factors and macroeconomic factors and the correlation and regression analysis were also computed to investigate the extent of influence.

The multiple linear regression model was utilised as adopted from Corder and Foreman (2009) as follows;

= Loan defaults

= y intercepts (value of dependent variable in the absence of all independent variables)

= attached coefficients of independent variables (indicating by how much the dependent variable changes as a result of unit change in the independent variable)

= error term (takes account of variables that are not included in a model but has influence on the dependent variable and also takes into account the model misspecification).

RESULTS

This part presents findings following the analysis of the influence of bank, borrowers’ and macroeconomic factors on loan defaults. The first part of the section presents the descriptive statistics, followed by inferential statistics.

Relationship between borrower-specific Factors and loan defaults

In reference to the influence of borrowers’ specific factors on loan defaults, it was revealed, as shown on Table 1 below that 65(46.4%) respondents agreed that lack of business skills contribute to loan defaulting behavior among borrowers. 69(49.6%) respondents agreed that diversion of funds is a major attribute to loan defaults while 64(45.7%) agreed that loan defaulting behavior was likely in the event that a borrower lost their job.  Furthermore, 106(75.7%) and 97(69.3%) respondents attributed loan defaulting behavior to income sources and amount requested respectively.

 Munyua (2016) similarly identified several factors that influence loan defaulting behavior among borrowers to include; lack of business skills among borrowers, loan collection procedures, loan use diversion, financial management practices, and amount of loan borrowed by members of women groups affiliated to Microfinance Institutions. Further revelations were that business failure was also attributed to loan defaults, and hence recommended that prospective borrowers should be provided with basic training in businesses management prior to granting loans followed by close monitoring of businesses by bank officers, as supported by Mzingula (2013).

The above findings are also supported by key informants, and particularly one from CRDB Bank had this to say;

“I have over six years of experience working in the loan department and from my observation; loan default is not only caused by lack of business skills among most entrepreneurs but also the misallocation of loans. Most loan defaulters, especially men use loans to secure more women or purchase furniture instead of allocating the money to the initial business plan. I vividly remember one defaulting client from Kihesa who took 1.5Million in loan, only to purchase a fridge and other home furniture, which were later confiscated and sold off by the bank in an attempt to recover the loan.”

Another loan officer from NMB stated that;

“The majority of loan defaulters are self-employed and not salaried borrowers, which is very unlikely unless in the occasion that they have lost their jobs. Loan defaulting behavior is also lower among women in comparison to their male counterparts, there are times that wives bear their husband’s loan burden, because they are constantly in hiding from the banks that pursue them to repay their loans.”

Table 1: The influence of borrowers’ specific factors on loan default (n=140)

Source: Field data (2021)

The relationship between bank specific factors and loan defaults

The current study sought to identify the relationship between bank specific factors and loan defaults and as portrayed on Table 2 below, 83(59.2%) respondents attributed loan defaults to the high interest rates charged by the banks while 82(58.6%) agreed that loan defaults were caused by poor monitoring and assessment by bank loan officers. Furthermore, 70(50.0%) were of the view that loan defaults were a result of stringent lending policies, 56(40.0%) attributed the same to credit size determined by banks and 62(44.3%) to lack of skills in loan management among bank loan officers. Okrpugie (2009) similarly found a positive link between high interest rates charged by Nigeria’s microfinance banks and the alarming default rates. Sheila (2017) also identified inadequate financial analysis as a prime cause of loan defaults, and further revelations were that the loan department’s screening processes of applicants were inadequate in determining potential borrowers’ actual financial base, which is imperative for mitigating future possibilities of loan defaults. Felix et al (2018) also conducted a similar study in Nigeria and found that; high interest rates, bureaucratic loan process, high transaction costs, collateral risk and asymmetric information were major attributes to borrower loan defaulting behavior.

The above findings are similarly supported by the views of key informants, and as one from NMB bank are directly quoted;

“……. Yes, as a bank we acknowledge the existing problem of loan defaults which is prevalent at 12 percent, this is attributed to higher interest rates charged and the relatively short repayment timeframe provided by the bank. We usually charge more interests as penalty for late payment however in rare extreme cases we have had to confiscate properties of customers who completely fail to pay. The majority of defaulters however eventually complete payment although very late”.

Another loan officer from TPB bank also supports that;

“The current rampant cases of loan defaults are mainly attributed to high interest rates and poor customer appraisal prior to loan issuance. Many borrowers have lost properties owing to repayment failure. The banks usually charge penalties once customers delay payment however, in the event that properties must be confiscated to recover loans, rifts have been created in families as the banks do not really consider the actual owner of the property and consequently, marriages have broken and families have gone bankrupt. In essence, most poor people don’t not benefit from commercial bank loans and agriculture development bank loans are a better alternative since they charge lower interest rates.”

Table 2:  The influent of bank specific factors on loan defaults(n=140)

Source: Field data (2021)

The relationship between macroeconomic factors and loan defaults

A prime focus of this study was also to determine whether inflation rates, unemployment rates, natural disasters, exchange rates and income tax rates influence loan defaults in financial institutions in Iringa Municipality. As depicted on Table 3 below, there were mixed feelings on inflation rates as 54(8.6%) agreed that it’s a major attribute to loan defaults while 50(35.7%) disagreed. 62(44.3%) respondents also agreed that high unemployment rates and natural disaster occurrences are to blame for borrower loan defaulting behavior. Furthermore, 65(46.4%) attributed loan defaults to high exchange rates and 73(52.2%) to high income tax rates. These findings imply that the borrowers’ ability to successfully complete loan repayment greatly depends on; size and stability of personal income, irrespective of whether they are employed or self-employed thus, borrower loss of job or business failure will likely result to loan default, with the latter mainly caused by inflation, natural disasters and exchange rates.

One loan officer from TPB bank also asserts that;

“… In my opinion loan defaults have both bank and customer related attributes however, some reasons are beyond banks and customers. For instance, in the past two years, many businesses had to shut down following the Covid 19 outbreak however, there are also many businessmen who simply use the pandemic as an excuse while in actual sense, they have diverted loans to other uses for example, one of my clients secures loans to pay schools fees ”.

Table 3: The influence of macroeconomic factors on loan defaults (n=140)

Source: Field data (2021)

The Pearson correlation was also computed to determine the extent of influence of each factor on loan defaults and findings are as indicated on table 4.6 below. In this case LD (loan defaults) BOF (Borrowers specific factors), BRF (Bank Specific Factors) and MEF (Microeconomic Factors).

Upon measuring the influence of borrowers’ specific factors, bank specific factors and macroeconomic factors on the likelihood of loan defaults, it was revealed that bank related factors have the strongest influence on the likelihood of loan defaults with a coefficient of 0.211* and p value 0.12.  Microeconomic factors have a positive, however weak relationship with loan default while borrower’s specific factors were found to have a negative relationship with loan defaults. These findings imply that high interest rates charged by banks, lending procedures, amount of loan issues, inadequate skills possessed by loan officers and poor monitoring and assessment processes significantly contribute to loan defaults in selected financial institutions in Iringa Municipality.

The Influence of Bank related factors, Borrowers Factors and Macroeconomic Factors on Loan Default

Similarly, findings from the regression analysis support those established through the Pearson correlation and as implied, bank related factors are strongly associated with the likelihood of loan defaults as the coefficient p value is 0.019 which is less than 0.05.  Borrowers’ specific factors were found to have a negative influence while Macroeconomic factors were found to have weak relationship with loan defaults.

Source: Field data (2021)

CONCLUSION AND IMPLICATIONS

Our conclusions are that bank related factors strongly influence loan defaults, followed by macroeconomic factors which have a weak positive relationship and lastly borrowers’ specific factors, which have a negative relationship with loan defaults. The implication is that banks should make amendments regarding issues raised for instance; reviewing interest rates charged training of their staff on credit issues, proper monitoring and screening of customers, and review of lending policies. Since these are the main bank related factors that contribute to loan defaults, changes in these areas will no doubt curb loan defaults to a significant extent. Loan officer credit education should be intensified and precautious loan monitoring should be vigorously pursued. Additionally, loan appraisal systems should be robust with the application and development of credit scoring systems that will factor in key variables of loan default.

REFERENCES

1. Abdulsaleh A.M and Worthington A (2013). Small and Medium Sized Enterprises Financing. A review of Literature. International Journal of Business Management vol 8 issue 14 page 36-54
2. Adebayo, O. (2010). Informal Financial Institution and Poverty Reduction in an Informal Sector in Nigeria: Case of rotating Savings and Credit Associations (ROSCA). Thesis in Art & Development studies, Graduate school for development studies. The Hague Netherlands
3. Ahmad, S.A. (1997). Natural Hazards and Hazard Management in the Greater Caribbean and Latin America Publication No. 3
4. Ameyaw-Amankwah, I. (2011).Causes and effects of loan defaults on the profitability of OkomfoAnokye Rural Bank. Thesis, unpublished. KNUST
5. Asfaw R, Bogale D and Teame K (2016). Factors affecting Non Performing Loans: Case study of Development Bank of Ethiopia Central Region. International Journal of Scientific and Research Publications Vol.6, issue 5
6. Baklout I(2012). Determinants of Microcredit Repayment. The case of Tunisian Microfinance Bank. African Development Review
7. Balogun, E.D. and Alimi, A. (1990). Loan Delinquency Among Small Farmers in Developing Countries: A Case Study of the Small-Farmer Credit Programme in Lagos State of Nigeria, CBN Economic and Financial Review, 26(3).
8. Barus, A. C. & Erick (2016). AnalisisFaktor –Faktor yang MempengaruhiNon Performing Loan pada Bank Umum di Indonesia, 6, 113-122. Retrieved from https://www.mikroskil.ac.id/ejurnal/index.php/jwem
9. Bhatt N and Tang S.Y (2012). Determinants of Repayment in Microcredit. Evidence from Progrms in the United States. International Journal of Urban and Regional Research Vol 26 issue 2 pg 360-376
10. Bos, J., Li, R., & Sanders, M. (2018). Hazardous Lending: The Impact of Natural Disasters on Banks’ Asset Portfolio. Maastricht University, Graduate School of Business and Economics. GSBE Research Memoranda.
11. Brei M, Mohan P and Strobl E(2018). The impact of Natural Disasters on the Banking Sector. Evidence from Hurrican Strikes in Carribean. The Quarterly Review of Economic and Finance vol 7 issue 2
12. Calcaginini G, Giombini G and Lenti E (2015). Gender Differences in Bank Loan Access. An Empirical Analysis. Electronic Journal vol 1. Issue 2
13. CGAP, (1999). Measuring microcredit delinquency: Occasional paper no. 3, CGAP secretariat1818 h street. Government printers
14. Felix S.S, Wahome D and Ariemba J.M (2018). Financial Institutions Factors Influencing Loan Default by SMEs in Kitui Central Sub country. International Journal of Current Research Vol 10. Issue 01
15. Garikipati, S. (2012) Microcredit and Women’s Empowerment: Through the Lens of Time-Use Data from Rural India, Development and Change, 43(3), pp. 719-750.
16. Juma C.R(2003)“ Reasons Why Banks Customers Fail to Service their Loans in Kenya. A data Survey of Standard Chartered Bank. Unpublished Master’s thesis University of Nairobi
17. Kamalzulzaman S.N.H, Koe W.L and Ismail S(2020). Factors that influencing Default Loan Repayment Intention Among Microentrepreneurs. http.www.researchgate.net/publication/340115913
18. Kiarie F.K, Nzuki D.M and Gichuhi A(2016). Influence of Socio demographic determinants on Credit cards default risks in Commercial Banks in Kenya. International Journal of Science vol 4. Issue 5
19. Marili’s R (2015). Impact of Macroeconomic Environment on Credit Risk in Commercial Banks. Unpublished Research work from Kaunas University of Technology.
20. Mpofu T.R and Nikolaidou E (2018). Determinants of Credit Risks in the banking system in sub Saharan African. Review of Development Finance Vol 8 issue 2 pp 141-153
21. Mpofu, T. R. and E. Nikolaidou (2018). “Determinants of credit risk in the banking system in Sub-Saharan Africa.” Review of development finance 8(2): 141-153
22. Murray, J. (2011). Default on a loan. United States Business Law and Taxes Guide
23. Muthoni M.P (2016). Assessing Borrowers and Business Factors Causing Macrocredit Default in Kenya. A comparative analysis of Microfinance Institutions and Financial Intermediaries. Journal of Education and Practice vol 7. No.2
24. Mzingulla E.P (2019). Determinants of Loan Repayment in Community Village Bank (VICOBA) in Lushoto District Tanzania. A Case of Mshikamano VICOBA. International Journal of Arts, Humanities, Literature and Science Volume: 04 Issue: 05
25. Nelson, M. W.& Victor M.K. (2009). Commercial Banking Crises in Kenya: Causes and Remedies. African Journal of Accounting, Economics, Finance, and Banking Research, Nairobi Vol. 4.BIBLIOGRAPHY \l 1033
26. Okpugie, G. (2009). High Microfinance Interest Rates Cause Loan Defaults in Nigeria, The Guardian, Nigeria
27. Oni O.A, Oladele O.I and Oyewole I.K (2015). Analysis of Factors Influencing loan defaults among Poultry Farmers in Ogin State Nigeria. Central European Journal of Agriculture Vol 6 no 4 pp 619-624
28. Saunders, M., P. Lewis, et al. (2009) Research Methods for Business Students. Harlow, England: FT Prentice Hall, Pearson Education.
29. Sheila A L.(2011). Lending Methodologies and loan losses and default in a Microfinance deposit-taking institutions in Uganda. A case study of Finca Uganda Kabala Branch (MDI). Research report presented to Makerere University, Uganda.
30. Szarowska, I. (2018). Effect of macroeconomic determinants on non-performing loans in Central and Eastern European countries.” International Journal of Monetary Economics and Finance11(1): 20-35
31. Waweru, N. M. & V. M. Kalani (2009). Commercial banking crises in Kenya: causes and remedies. Canada: York University Press
32. Yeboah E and Oduro I.M(2018). Determinants of Loans Defaults in Some selected Credit Union in Kumasi Metropolis of Ghana. Open Journal of Business Management vol.6 no.3
33. Yegon J.C, Kiptemboi J, Kemboi J.K. and Chelimo K.K, (2013), Determinants of a State-Owned Agricultural Loan Scheme in UasinGishu County, Kenya, Journal of Emerging Trends in Economics and Management Sciences Vol 5(1) pp 51 – 5
34. Zarook T, Rahman M.M and Khanam R(2013). The Impact of demographic factors on accessing finance in Libya. International Journal for East European Management Studies vol 17 issue 1 pg 104-130
35. Zhang K.Z and Ting C.L.M (2011). Education of New Chinese Immigrants Children in Hong Kong; Challenges and Opportunities. Support for Learning Journal vol 26 issue 2