Effect of Innovative Credit Management Practices on Loan Portfolio Performance of Commercial Banks: A Case of Commercial Banks in Lira City, Uganda

Effect of Innovative Credit Management Practices on Loan Portfolio Performance of Commercial Banks: A Case of Commercial Banks in Lira City, Uganda

Lanyero Esther¹, Dr. Nyakundi Andrew² & Dr. Manyange Michael³

¹Postgraduate Student Kampala International University, Uganda

^2,3Faculty of Business and Management, Kampala International University, Uganda

DOI: https://dx.doi.org/10.47772/IJRISS.2024.8110199

Received: 11 November 2024; Accepted: 16 November 2024; Published: 18 December 2024

ABSTRACT

Commercial banks play a vital role in economic development of a country through offering financial services. However, they are experiencing increased level of non-performing loans (NPLs) ,consequently affecting their profitability and growth. Therefore the objectives of the study were to: evaluate the effect of credit assessment, credit monitoring and credit risk management on loan portfolio performance. The study adopted correlational research design and quantitative research approach.Total population was 156 and a sample of 112.Stratified random sampling and purposive sampling were used to select respondents. Data was collected using a Questionnaire and analyzed using multiple linear regression technique with help of SPSS. The independent variables explained 40.5%(R²=.405) of variations in loan portfolio performance .All the variables tested revealed statistically significant effects: Credit assessment(t=5,205,P=0.000,P<.05),Credit monitoring (t=6.992,P=0.000,P<.05) and Credit risk management(t=5.300,P=0.000,P<.05).The study concluded that the independent variables selected had an effect on loan portfolio performance of commercial banks. Therefore the study recommended enactment of favorable regulations or laws by the government and also banking sector to introduce innovative programmes to reduce NPLs.The results can be beneficial to the government, banking sector and researchers.

Key words: Commercial banks. Credit monitoring, credit assessment, Credit risk management, loan portfolio performance

INTRODUCTION

In USA, the performance of bank loans is closely tied to economic conditions, regulatory oversight, and risk management practices (Kazbekova et al., 2020). Historically, during periods of economic growth, banks tend to experience lower levels of loan delinquencies and defaults, resulting in strong loan performance, while economic downturns can lead to deterioration in loan performance as borrowers face financial challenges. Loan performance is assessed using key metrics such as loan delinquencies, non-performing loans, charge-off rates, and loan portfolio credit quality. Additionally, the COVID-19 pandemic disease and government relief programs have significantly influenced loan performance, with many banks offering forbearance and relief to borrowers during the economic uncertainty (Saleem el al., 2024). Prudent underwriting and effective risk management practices are essential for maintaining healthy loan performance in the dynamic financial landscape of the United States (Yaqoob et al., 2024).The rise of fintech and online lending platforms has introduced a competitive landscape and necessitated the banks to adapt digital innovations while managing associated cybersecurity risks. Striking a balance between supporting economic growth and managing credit risk poses a central dilemma (Shyu, 2023)

In China, the loan portfolio of commercial banks’ performance  also experienced a challenge (Zhang et al; 2024).The banks have a challenge of  higher percentages of non-performing loans (Cao, Duan & Ibrahim, 2023).Attempt by Chinese Government to address non-performing loans(NPLs) has  yielded little impact (Chaoqun et al., 2024;Yao, & Song, 2023).Statistics  show upward trend of borrowers in China,270 borrowers per 1000 in 2019(Yang,2019) to 426 in 2022,therefore increasing potentially credit risk in Commercial banks (Twum etal., 2022)

Germany has experienced low levels of NPLs (Saliba, et al.,2023) due to culture of focusing  on conservative underwriting standards, emphasizing creditworthiness and collaterals (Kaivo-oja & Santonen, 2023).Also solid regulatory structure has been adopted (Boussaada et al.,2023). Germany’s diverse and export-oriented economy also mitigates risk by spreading lending across various industries (Di Carlo et al., 2023). Adoption of digital banking and fintech innovations, banks are also adjusting their lending practices and exploring innovative credit scoring and risk assessment models to keep their loan portfolios in good condition (Matthews et al., 2023).

In Kenya, the commercial banks have faced an increase in non-performing loans (NPLs), (Muindi &Ambrose, 2023; Lunani, 2023).The potential size of Kenya’s home mortgage market is estimated by World Bank at ksh800 billion (Walley 2011) and is experiencing   2.5% mortgage debt-to-GDP ratio which may expand to 32.5%.(Nyangiye et al., 2022).In Uganda non-performing loans is not unique, therefore is an issue . According to the Bank of Uganda annual report, 2019/2020, non-performing loans increased  from 3.8% in 2019 to 6.0% in 2020.Some banks have closed down due to increased ratio of NPLs such as Crane Bank, whose  NPLs  rose from Shs19.36 billion in 2014 to Shs142.3 billion  in 2015,an increase of 122.94 billion(Francis et al., 2022).There is insufficient information on how commercial banks can boost loan portfolio performance (Assifuah-Nunoo, 2023).

Statement of the problem

Commercial banks play a critical in economic development of any country primarily in the provision of financial services. Therefore maintenance of low percentage of non-performing loans (NPLs) will make them maintain financial stability and remain in business (Mburu et al., 2020).Despite control measures implemented to reduce NPLs, the commercial banks in Uganda have accumulated sizeable NPLs since 2019 (Katusiime, 2021).The NPLs increased from3.8% in 2019 to 6..0% in 2020 ,quantitatively from Ushs138.8 billions in 2021 to Ushs 228 billions in 2022(Bank of Uganda Annual Report,2022). The issues of concern consist of the effect of credit assessment, credit monitoring and credit risk managementon loan performance of commercial banks havebeen inadequately analyzed.If the issue of non-performing loans is not treated with the seriousness and its increase curbed, the opportunities that could haveotherwise available to the borrowers including Medium and Small Enterprises (SMEs) to grow their businesses will become foreclosed. This in the long run will make it difficult for Uganda to grow the economy.

Objectives

1. To evaluate the effect of credit assessment on Loan portfolio performance in Commercial Banks
2. To assess the effect of credit monitoringon loan portfolio performance in Commercial Banks
3. To examine the effect of credit risk managementon loan portfolio performance in Commercial Banks

Research Hypotheses

Ho1: There is no significant effect of credit assessment on loan portfolio performance in Commercial Banks

Ho2: There is no significant effect of credit monitoring on loan portfolio performance in Commercial Banks.

Ho3: There is no significant effect of credit risk management on loan portfolio performance in Commercial Banks

Conceptual framework is shown in figure 1

Figure 1.Interrelation between independent and dependent variables

SS

Source. Field data,2024

LITERATURE REVIEW

Effect of Credit Assessment on Loan Portfolio Performance of commercial banks

Riro and Mbuva, (2023)studied the impact of credit risk assessment, client credit risk checks, and credit regulatory compliance on loan performance at Kenyan commercial banks.Result showed that there was enhanced lending performance because of  client credit checks with a correlation coefficient of 0.534%,.Therefore customer credit checks and loan portfolio performance were positively correlated .It further indicated that for every unit increase in credit risk assessment  loan performance improved by 0.621 units.

Twinomugisha, (2020) examined the impact of credit management strategies on the loan performance of Ugandan commercial banks, concentrating on Centenary Bank .The findings showed that credit recovery methods improve loan portfolio performance at Centenary Bank (adjusted R2 = 0.780*, p value = 0.000). and credit monitoring techniques had significant impact   on loan portfolio performance at Centenary Bank (adjusted R2 = 0.501*, p-value = 0.000).

Kamugisha and Rutaro, (2019) investigated the effect of  credit policy and  PMF Uganda Ltd.’s loan portfolio performance. The results showed that at PMF Uganda, credit terms are positively correlated with better loan portfolio performance as indicated by r=0.825 and an adjusted R2(.669) of 66.9%.  Other findings indicated that credit standards and credit collection procedures had a significant impact on the performance of the loan portfolio at PMF Uganda and PMF Uganda, respectively.

Muthoni, Mwangi and Muathe, (2020) researched the impact of credit management approaches on loan performance in Kenyan commercial banks. Results of the study revealed that lending and debt collection tactics significantly improved the performance. Findings of client appraisal  had no meaningful effect on loan performance at Kenyan commercial banks. Thus, the study found that the effectiveness of commercial banks’ credit management procedures was highly correlated with their loan performance.

Sola (2021)investigated the link between credit management procedures and loan performance in microfinance institutions in Nigeria. The results showed that collection policies had a negative but non-significant impact on loan performance, credit conditions had a favorable but insignificant effect also loan performance was significantly improved by client appraisal.

Francis, Caleb and Eton, (2022) studied the relationship between loan performance and credit risk management measures at commercial banks in Mbarara City. Results showed that credit risk assessment, monitoring, and control had an impact on loan performance and there was a strong correlation between loan performance and credit risk detection.

Obae and Jagongo, (2022) examined how the country’s commercial banks’ credit management strategies affected the performance of their loans.The findings of the regression analysis demonstrated a positive correlation between loan performance and the model’s predictions (R = 0.759). Significant predictors included credit limitation and customer appraisal; more credit rationing may have the effect of improving loan performance. The resultsfurther showed that debt collection had a significant effect on loan performance and that it is beneficial to collect debt because commercial bank loans perform better when the debt collection period is shorter.

Doye(2021) studied an assessment of the impact of credit risk management and Performance on Loan Portfolio at International Bank Liberia Limited from 2015 to 2017.The study’s findings indicated that the primary factors the bank considers when selecting loansinclude credit score and credit history which  were significant with response rate from respondents at  25% and 32% respectively.Muigai and Mwangi, (2022) studiedhow credit referencing influences Kenyan lenders’ loan performance. The findings showed that the influence was non-significant and unfavorable correlation between the performance of loans from Kenyan lenders and all credit referencing variables.

Effect  of  Credit Monitoring on Loan Portfolio Performance of commercial banks

Protase, (2022) conducted a study to look at the relationship between Centenary Bank’s performance, recovery tactics and credit monitoring in Uganda.The results indicated that the variables had a strong positive association,(R²=0.209),which implied thatrecovery strategies and credit monitoring influenced  bank’s performance by 20.9%.Kinyua, Kiiru and Njoroge, (2022) examined how loan monitoring programs and client appraisals affected Kenyan revolving fund repayment.The results showed  a positive and significant effect on repayment performance (coefficient 2.715, P-0.002).Further findings showed that loan monitoring strategies have a significant beneficial impact on repayment performance in Kenya.\Annah, (2022) investigated the effects of financial risks on financial performance of commercial banks in Uganda. The study found that a bank’s ability to manage its credit risk and its overall financial performance were highly correlated and  a strong correlation between the bank’s financial performance and its liquidity risk existed

Credit Risk Management on Loan Portfolio Performance of commercial banks

Bhatt eta al.(2023) studied the factors influencing credit risk management and performance at commercial banks. The results indicated that credit risk management significantly impacted on performance of commercial banks.Nyende and Kasozi-Mulindwa,(2019) investigated the relationship between portfolio performance and credit risk management.The study reported direct correlation in that credit risk management increases portfolio performance.Habamenshi and Gasana(2023)studied the impact of credit risk management on loan performance in microfinance institutions. The study found that RIM Ltd’s loan performance was positively impacted by client appraisal (overall μ = 4.87; σ =0.325), credit risk control (overall μ = 4.74; σ = 0.381), collection policy (overall μ =5.00; σ =0.000), and terms of credit (overall μ =4.65; σ =0.407).

Karanja and Simiyu, (2022) investigated the effect of credit management practices on loan performance in Kenyan microfinance firms. The objectives included evaluation of credit policy, customer assessment, collection policy, credit circumstances and credit risk management. Results indicated fewer than 20% of loans defaults and the effect was significant.Ntanzi, (2022) examined the relationship between credit risk management, credit information sharing, and loan portfolio performance among Ugandan commercial banks. The findings showed that credit risk management and loan performance are positively correlated, hence  information sharing and credit risk management significantly affected the variability of loan portfolio performance.

Akugizibwe, (2022) investigated the variables at Stanbic Bank in Uganda resulting to high non-performing loan(NPL).In 2018 ,NPL was at Ushs 535.8 billion  which increased to Ushs685.7 billion in 2019 an increase of  about 28%.The findings showed weak credit risk management strategies. Muratenyi and Olando(2022)investigated how Kenyan commercial banks’ financial performance was affected by falling loan portfolio quality.The study revealed a significant positive correlation between Kenya’s commercial banks’ loan portfolio quality and  financial performance. Return on equity and return on assets had coefficients of determination of 0.0363 and 0.1620, respectively Kajirwa and Katherine(2019).studied the impact of credit risk on the financial performance of commercial banks registered on the Nairobi Securities Exchange in Kenya.The findings showed a reasonably substantial association between credit risk and financial success based on ROE (r = -.601**, p =.003). Credit risk and ROE, therefore there was   a significant association (β = -.601, p = 007, α < 0.01).The model explained32.3 %(R²=.323) of the variation in commercial banks ‘financial performance

RESEARCH METHODOLOGY

The study adopted correlational research design and quantitative research approach  (Demaria ,2023;Salter,2023).The accessible study population  was 156 which was drawn from 13 commercial banks operating in Lira City(Employees Annual Report of  Commercial Banks,2022) from which a sample of 112 was drawn from. Stratified random sampling, created strata to cater for all banks and purposive sampling were used to select respondents for the study. The questionnairewas the main instrument used to collect the data. To control quality of research instrument, both validity and reliability were tested and found to be valid with content validity index(CVI) of 0.912 and reliability of  above 0.70(Gani et al.,2020).Data Analysis was done by use of multiple linear regression technique with help ofstatistical package for social science (SPSS version 28).the mode is indicated below.

Y =Bo+β₁X₁+β₂X₂+β₃X₃+e

Where:

Y = Loan portfolio performance

X₁ = Credit assessment

X₂ = Credit monitoring

X₃ = Credit risk management

Bs = Coefficients of independent variables

e   = Significant level (5%)

RESULTS

Effect of Credit Assessment  on Loan Portfolio Performance of Commercial Banks

The results have been tabulated in table 1 below.

Table 1. Credit Assessment and Loan Portfolio Performance of Commercial Banks

Source: Field data, 2024

Key: 1- Disagree Strongly (DS), 2- Disagree (D), 3- Agree (A), 4- Strongly Agree (SA).

Statistics indicate that out of 88 respondents (N) who participated in the study, 2 (2.3%) strongly disagreed, 6 (6.8%) disagreed, 33 (37.5%) agreed, and 47 (53.4%) strongly agreed that the repayment history of a client is key before giving out a loan.Further 1 (1.1%) disagreed, 25 (28.4%) agreed, and 62 (70.5%) strongly agreed that credit worthiness and guarantee of clients is aligned with the size of the loan to be issued. Also 1 (1.1%) strongly disagreed, 12 (13.6%) disagreed, 43 (48.9%) agreed, and 32 (36.4%) strongly agreed that the Credit Appraisal Committee approves the loan that is issued. Also it was  revealed that 5 respondents (5.7%) strongly disagreed, 13 (14.8%) disagreed, 39 (44.3%) agreed, and 31 (35.2%) strongly agreed that the Credit Department records various loans applicants. On failing to discover the client credit history,33 respondents (37.5%) strongly disagreed, 24 (27.3%) disagreed, 13 (14.8%) agreed, and 18 (20.5%) strongly agreed .

For  analysis  by banks concerning the financial status of clients before extending loans, 1 respondent (1.1%) strongly disagreed, 12 (13.6%) disagreed, 43 (48.9%) agreed, and 32 (36.4%) strongly agreed. Finally16 respondents(18.2%) strongly disagreed, 18 (20.5%) disagreed, 37 (42.0%) agreed, and 17 (19.3%) strongly agreed that the character of the client is paramount when extending loans.The overall mean was 3.03 with standard deviation of 0.815,which implies that majority of respondents were in agreement that credit assessment  affects  loan portfolio performance of commercial banks

 Effect of Credit Monitoring on Loan Portfolio Performance of Commercial Banks

The findings are shown in table 2 below.

Table 2. Credit monitoring and Loan Portfolio Performance of Commercial Banks

Source: Field data, 2024

Key: 1- Disagree Strongly (DS), 2- Disagree (D), 3- Agree (A), 4- Strongly Agree (SA).

Statistics indicated that 5 respondents (5.7%) strongly disagreed, 27 (30.7%) disagreed, 36 (40.9%) agreed, and 20 (22.7%) strongly agreed that the Loan Department periodically meets loan clients for further advice. Regarding the statement that the bank periodically gathers information about clients, 28 respondents (31.8%) strongly disagreed, 31 (35.2%) disagreed, 21 (23.9%) agreed, and 8 (9.1%) strongly agreed. For the statement that  all bank debtors are visited regularly to assess the financial performance of their businesses, 8 respondents (9.1%) strongly disagreed, 20 (22.7%) disagreed, 45 (51.1%) agreed, and 15 (17.0%) strongly agreed. Concerning whether the loan department hold recovery meetings regularly with the clients, 20 respondents (22.7%) strongly disagreed, 24 (27.3%) disagreed, 34 (38.6%) agreed, and 10 (11.4%) strongly agreed. Further, results indicated that 1 (1.1%) strongly disagreed, 7 (8.0%) disagreed, 38 (43.2%) agreed, and 42 (47.7%) strongly agreed that the loan department officials periodically send notifications to clients for the outstanding amount. For Compliance of the approval terms and conditions being strictly enforced and monitored, 4 (4.5%) strongly disagreed, 13 (14.8%) disagreed, 38 (43.2%) agreed, and 33 (37.5%) strongly agreed. Finally, for the statement that regular reviews are done on collection policies to improve state of credit management, 8respondents (9.1%) strongly disagreed, 13 (14.8%) disagreed, 38 (43.2%) agreed, and 29 (33.0%) strongly agreed. The overall mean was 2.80 and 0.866 standard deviation. This means that most of respondents agreed that credit monitoring  affect loan portfolio performance of commercial banks

Effect of Credit Risk Management on Loan Portfolio Performance of Commercial Banks

The results are indicated in table 3.

Table 3.Credit risk management and loan portfolio performance of Commercial Banks

Source.Field Data, 2024

Key: 1- Disagree Strongly (DS), 2- Disagree (D), 3- Agree (A), 4- Strongly Agree (SA).

Findings that the bank reminds clients of their repayment installments prior to  due date through SMS/calls, 5  respondents (5.7%) strongly disagreed, 8 (9.1%) disagreed, 31 (35.2%) agreed, and 44 (50.0%) strongly agreed. Also 2(2.3%) respondents strongly disagreed, 5 (5.7%) disagreed, 38 (43.2%) agreed and 43 (48.9%) strongly agreed that the bank reminds clients of their repayment date through SMS/calls .As for the clients’ files being tracked and surveyed from loan acquisition to final payment of the principal, 3 (3.4%) strongly disagreed, 6 (6.8%) disagreed, 35 (39.8%) agreed, and 44 (50.0%) strongly agreed. Also 4 (4.5%) strongly disagreed, 8 (9.1%) disagreed, 35 (39.8%) agreed, and 41 (46.6%) strongly agreed that the bank tracks collateral from loan acquisition to repayment. As fordefaulters being given stringent repayment conditions once they default on the first installment, 3 (3.4%) strongly disagreed, 10 (11.4%) disagreed, 46 (52.3%) agreed and 29 (33.0%) strongly agreed. Finally as regards to the credit department continuously assessing the accounts of debtors, 3 (3.4%) strongly disagreed, 6 (6.8%) disagreed, 40 (45.5%) agreed, and 39 (44.3%) strongly agreed. The overall mean  was 3.3 with standard deviation of 0.773 which means that majority of respondents agreed that Credit risk management  has an effect on Loan Portfolio Performance of Commercial Banks.

Results based on research hypotheses

Model Summary

A value of 0.637 (R= .637) indicates a good level of prediction of dependent variable(Loan Portfolio performance). The independent variables explained 40.5% (R²=.405) of the variability in dependent variable while 59.5%could be accounted for by other factors not covered in the study

ANOVA^a

1. Dependent variable: Loan Portfolio performance
2. Predictors(Constant):Credit Assessment(CA)Credit Monitoring(CM), Credit Risk Management(CRM)

Analysis of variance (ANOVA) above tests whether the overall regression model is good fit for the study data. ANOVA table indicates that the credit management practices statistically significantly predicted  loan portfolio performance, F.05 (3,84)=19.098, P<.05. Therefore, regression model was a good fit for the data.

Coefficients^a

1. Dependent variable: Loan portfolio performance

General formula for regression model:

Y=3.502-0.078 X₁-0.107 X₂-0.041 X_{3……………………………………………………….1}

H_o1: There is no significant effect of   credit assessment on loan portfolio performance in Commercial Banks

The result showed that there was a significant effect of credit assessment on loan portfolio performance  of Commercial Banks(t= 5.205, P=0.000, P<0.05). Null hypothesis was rejected in favour of alternative hypothesis (H_a). One additional unit of credit assessmentcan decrease loan portfolio performance in commercial banks by -0.078 units.

H_o2_: There is no significant effect of credit monitoring on loan portfolio performance in Commercial Banks

The findings revealed that there was a significant effect of credit monitoring on loan portfolio performance (t= 6.992, P=0.000, p<0.05). Null hypothesis was equally rejected and alternative hypothesis accepted (H_a).Addition of one unit of credit monitoring decreases loan portfolio performance in commercial banks by -0.107 units.

H_o3_: There is no significant effect of credit risk management on loan portfolio performance in Commercial Banks

The result indicated that there was a significant effect of credit risk management on loan portfolio performance (t= 5.300, P=0.000, P<0.05). Null hypothesis was rejected in favour of alternative hypothesis. One additional unit of credit risk managementcan decrease loan portfolio performance in Commercial Banks by -0.041 units.

DISCUSSION

On the effect of credit assessment on Loan portfolio performance in Commercial Banks in Lira City in Uganda, the study found that majority of respondents agreed that credit assessment had  an effect on  loan portfolio performance ,overall mean was 3.03and credit assessment significantly affects loan portfolio performance in commercial banks(t=5.205,P=.000,P<.05).One unit of credit assessment decreases loan performance by -.078 units. The findings is supported by various researchers who reported significant effect(Twinomugisha,2020;Kamugisha&2019).However, the study disagrees with findings of  Riro and  Mbuya (2023),who reported that every unit increase in credit assessment  increases loan performance by 0.512 units which is contrary to the findings of this study.

As for the effect of credit monitoring on loan portfolio performance of Commercial Banks in Lira City in Uganda, the findings of the study included overall mean of 2.80 from respondents with standard deviation (SD) of  0.866,implying that a good number of respondents agreed that credit monitoring affects  loan portfolio performance of commercial banks. This finding is supported by Smith et al.(2019) who reported  a mean score of 3.38 and SD of 0.683.The result is also in agreement with the finding of Kinyua and Simiyu(2022) who reported that  a number of monitoring visits affects repayment performance. The other main finding was that credit monitoring had significant effect  on portfolio loan performance(t=6.992,P=0.000,P<.05) .This finding is supported by the finding of Protase (2022) who reported significant positive relationship between credit monitoring and financial success of a bank.

Concerning the effect of credit risk management on loan portfolio performance in Commercial Banks in Lira City in Uganda, the result  had  an overall mean responses of  3.30 with standard deviation of .773 and the  effect was statistically significant on  loan portfolio performance(t=6.992,P=0.000,P<.05).The regression model explained 40.5%(R²=.0405) of variability in loan portfolio performance. The result is in disagreement with the study  of Jajirwa and Katherine(2019), who reported  variability of 32.5%(R²=0.325). However, The findings  are supported by various studies which reported  significant impact and positive relationship (Bhatt et al.,2020; Muratenyi& Olando,2022) but differs also with the finding of Mrindoko et al.(2020) who found negative association.

CONCLUSIONS AND RECOMMENDATIONS

The study concluded that credit assessment, credit monitoring and credit risk management significantly affect loan portfolio performance in commercial banks in Uganda. Therefore recommended that the government to enact relevant regulations and laws to save the banking sector and top management of Banking Sector proactively introduce innovative  programs to continually reduce non-performing loans.

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