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Go Back       IAR Journal of Business Management | IAR J Bus Mng, 2(2), | Volume:2 Issue:2 ( April 10, 2021 ) : 58-64
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DOI : 10.47310/iarjbm.2021.v02i02.009       Download PDF       HTML       XML

The Effect of Customer Credit Risk Monitoring on Performance of SACCOs in Kakamega County, Kenya


Article History

Received: 16.03.2021 Revision: 22.03.2021 Accepted: 30.03.2021 Published: 10.04.2021


Author Details

Brian Wakhungu Olukwo1, Dr. Gabriel Waweru2 & Dr. Mohamed Shano3


Authors Affiliations

1Department of Finance and Accounting School of Business


2Meru University of Science and Technology, Kenya


Abstract: Savings and Credit Cooperative Societies (SACCOs) operate in an environment of considerate risks and uncertainty. Credit risk monitoring is one of the main challenges faced by financial institutions as well as the savings and credit cooperative societies in many parts of the world. Thus, the primary focus of this study was to assess the effect of credit risk monitoring practices and performance of SACCOs in Kakamega County, Kenya. The study applied the descriptive research design and the use of inferential statistics in results presentation. We employed a systematic sampling procedure to identify the SACCOs and the sample size included all the SACCOs in Kakamega County. Qualitative data was reduced into simplified format while quantitative data was analyzed using Statistical Package for Social Science (SPSS) Version 26.0. ANOVA report was employed to assess the overall significance of the applied regression model. The model was found to be significant at 95% confidence level showing a positive relationship between independent and dependent variables. Thus the study found that there was a positive statistically significant effect between customer credit risk monitoring and performance in the SACCOs.


Keywords: Credit risk; Credit monitoring; Microfinance; Performing Loan


INTRODUCTION

There is growing recognition that the percentage of Non-performing Loans (NPLs) is related to failure of SACCOs in Kenya and the world. Savings and Credit Cooperative Societies (SACCOs) are mostly private or members owned intermediaries where members are sole owners through shares holding and membership is mostly open and voluntary, operating for profit basis by its members. Savings and credit cooperative societies have continued to provide savings, credits, and financial training at the grassroots level (World Bank, 2012). The best indicator for the health of the banking and SACCOs industry in a country is its level of NPLs. Moreover, the issue of loan defaults is becoming an increasing problem threatening sustainability of SACCOs.


Credit monitoring is recognized in today’s business world as an integral part of good management practice. It entails the systematic application of management policies, procedures and practices to the tasks of identifying, analyzing, assessing, treating and monitoring risk (Haneef S., Riaz T., Ramzan., Rana M., Ishaq H., & Karim Y., 2012). Credit risk monitoring is defined as identification, measurement, monitoring and control of risk arising from the possibility of default in loan repayments (Coyle B., 2000).


The Co-operative movement in Kenya was started by the European farmers in 1908 when they started the first Co-operative called Lumbwa Farmers‟ Co-operative Society for the purpose of marketing their cereals, fruits and dairy products. It was not until the mid-1940 that the colonialists agreed to introduce Co-operatives in the colonies as a piece meal programme for the development of Africa. In 1945, Kenya enacted the Co-operative Ordinance which was followed by the creation of a department under the Registrar of Cooperatives in 1946, whose objectives were to farm and to promote farm products (Kibanga, 2001). Today there are many types of Co-operatives in nearly all the sectors of the Kenyan economy. Some are haphazardly formed without the necessary considerations in mind and as such many don't go very far before disintegrating or being liquidated because of poor management, lack of records and financial systems, misappropriation of funds among others.


In Kenya, credit extended to borrowers may be at the risk of default such that whereas financial institutions extend credit on the understanding that borrowers will repay their loans, some borrowers usually default and as a result, financial institutions income decrease due to the need to provision for the loans. Every financial institution bears a degree of risk when the institution lends to business and consumers and hence experiences some loan losses when certain borrowers fail to repay their loans as agreed. Such unpaid loans are referred to as non-performing loans (Kithinji, 2010).


According to (Kangogo, 2014), it is necessary to control non-performing loans for the economic growth in the country, otherwise the resources can be jammed in unprofitable projects and sectors which not only damages the financial stability but also the economic growth. A study on the effect of credit risk monitoring on loan portfolio quality of tier one Commercial Financial institutions in Kenya concluded that credit risk monitoring influences the level of nonperforming assets which affects loan portfolio quality thus affecting the general performance of the bank (Onuko, et al., 2015). In view of the foregoing, the study will assess the relationship between credit monitoring practices and performance of SACCOs in Kakamega County, Kenya since performance is a critical element for good financial performance of financial institutions.


Majority of savings and credit cooperative society in Kakamega County operate Front Office Services (FOSA) as well as Back Office Services (BOSA) thereby accepting deposits from members. They operate savings accounts just like financial institutions as well as loan accounts which attract interest respectively (Kimathi, 2007). In Kakamega County there are at least 20 vibrant savings and credit cooperative society with a client base of over two hundred thousand members. The SACCOs offer a wide variety of services which include salary processing, loan processing, dividends and deposits processing, produce payment, cheques clearance, bankers cheques, interests on savings under FOSA, farmers account, counter withdrawal charges, notice fees charges on lump sum withdrawals among other services (Kimathi, 2007).


A number of studies have been done locally and internationally in relation to credit risk monitoring and performance. (Walsh L., 2018) carried out an assessment of the credit monitoring process of credit unions. The study found that credit unions are deficient in the credit control department. A study conducted by (Ahlberg and Anderson, 2012) on credit risk, Credit Assessment, Basel III, Small Business Finance in 95 small and large financial institutions in Sweden found out that most financial institutions had a well-developed credit process where building a mutual trust relationship with the customer is crucial.


Review of Related Literature

Credit risk identification is very important when dealing with risk management in financial institutions and more specifically in the savings and credit cooperative societies’. However, to manage risks in SACCOs effectively, the management should identify the risk associated with every transaction before any loan advancement. The first step on risk identification by SACCOs is by identifying the key risks which can as well be revisited, reviewed and approved by the board of management with guidance of the credit monitoring committee. The SACCO should also evaluate the degree of loans advancement risks, the level of tolerance and time of repayment among the positive and negative impact of credits if not controlled. The SACCO also need to analyze the risk faced in the areas of credit monitoring, liquidity, strategic risks, interest rates risk and operations risks (Sinkey, 1992).


In current status of many financial institutions such as financial institutions and savings and credit cooperative societies’ risk analysis goes behold evaluation to include other things like decision making process in risk management (Griffins, 2009). Another important aspect of credit monitoring is brainstorming, which is the main sensitive practice involving a group generating ideas. In this groups the people generate ideas with a philosophy of nobody knows better or nobody is wrong which gets the idea on board (Strutt, 2003). Brainstorming is quick and simple to apply but it lacks the comprehensive approaches in comparison to the more sophisticated techniques (Strutt, 2003).


Credit monitoring also helps the credit officers to be more industrious by knowing their work and being the subject of continues review on the credit portfolio. The major challenge faced by majority of financial institutions and financial institutions in late 1980s and early 1990s was that there was failure by financial institutions to monitor borrowers on basis of collateral values of assets before advancing loans. Majority of financial institutions during that period neglected acquiring of financial information of the credit borrowers in relation of real estate facts and monitoring and evaluating quality of collaterals (Malimba & Ganesan, 2009). In their result, many financial institutions and financial institutions failed to identify early on signs quality of assets to protect the institutions financials which in effects affected the institutions assets and those of the stakeholders. Lack of monitoring in the financial institutions by then lead to costly processes by senior management in determining the dimensions and severity of the problematic loans which resulted to large losses.


A study carried out by (Addae-Korankye, 2014) on the causes and control of loan default/delinquency in microfinance institutions in Ghana. The study found that the causes of loan default included high interest rate, inadequate loan sizes, poor appraisal, lack of monitoring and improper client selection. Measures to control default were found to include training before and after disbursement, reasonable interest rate, monitoring of clients, and proper loan appraisal. It was recommended among others that SACCOs should have clear and effective credit policies and procedures and must be regularly reviewed. It was concluded that the government and hence Bank of Ghana should regularly monitor and supervise the SACCOs so as to ensure safety of clients’ deposits and customers’ confidence.


Most of research is on causes of poor performance in SACCOs and too little is found on the relationship between credit monitoring practices on performance of SACCOs. The immediate consequence of large amount of poor credit monitoring practices in the SACCO system is SACCO failure as well as economic slowdown. The causes of nonperforming loans are usually attributed to the lack of effective monitoring and supervision on the part of banks, lack of effective lenders’ recourse, weaknesses of legal infrastructure, and lack of effective debt recovery strategies.


Non-performance of loans has been pointed out as one of the major factors leading to poor performance of SACCOs worldwide as well as SACCO failures. From the available literature, factors affecting performance include: Macro-economic factors such as GDP, interest rates, inflation, unemployment and bank specific factors such as bank Credit risk monitoring practices. Further, available empirical literature concedes that there is a strong relationship between credit risk monitoring and performance in banks especially commercial banks. A negative relationship has been found between credit risk monitoring and Non-performing loans by a majority of the studies conducted locally and internationally.


Limited research has been conducted to establish how Credit risk monitoring is related to the performance of loans specifically for in Kenya SACCOs in Kenya. It is upon the insufficiency in literature related to Credit risk monitoring that we sought to analyze the impact of customer credit risk monitoring on performance of SACCOs in Kakamega County, Kenya.


Methodology and Research Design

The study adopted a descriptive design because of its appropriateness in establishing relationships between variables and facilitating the collection of information for achieving the principal goal of the study. Further, (Mugenda & Mugenda, 2003) denoted that descriptive research enables subjects to give more information on the issue of interest under study. This research design was therefore appropriate in investigating the impact of credit risk monitoring on performance in SACCOs.


Target Population Sample and Sampling Technique

We targeted the SACCOs operating in Kakamega County, licensed by SASRA at as per 2017. According to SASRA there are 12 deposits taking SACCOs operating in the study area and they will form our target population. The Sacco managers, IT employees, credit managers, internal auditor and the front office person formed our respondents.


(Cooper & Schindler, 2006), established that for small sample target population (less than 100), a sample size of over 50% is adequate for descriptive study. We opted to cover all the 20 active SACCOs and a samples size which is over 50% and it was in line with (Cooper & Schindler, 2006). Thus, the study sample size was 12 savings and credit cooperative societies in which 60 respondents were sought across. 5 employees were selected in each SACCO randomly.


Data Analysis and Presentation

Before analysis, the data collected was checked thoroughly after which it was classified into qualitative and quantitative data; qualitative data was summarized into themes and sub-themes related to specific research objectives and verbatim. Quantitative data was edited, coded, summarized, quantified and classified into forms that were suitably be used to prepare the report. From the information, the study developed narratives and interpretive report by explaining the situation within the SACCOs.


Statistical Package for Social Science (SPSS) version 26.0 was used to analyze the quantitative data and to establish the relationship between credit monitoring and performance. The descriptive data was presented inform of frequency tables, pie charts, and graphs. To arrive at conclusions that go beyond the immediate data alone and make inferences to accommodate general conditions, inferential statistics was also be used. Conclusions, suggestions and recommendations was drawn from the analysis.


Methods of correlation and regression were used in order to analyze the extent and the nature of relationships between variables. Correlation analysis was used to understand the nature of relationships between two individual variables. For example in investigating the relationship between credit monitoring practices and performance of SACCOs in Kakamega County, the two variables could be specified as the amounts of FDI and GDP for the same period.

Correlation coefficient ‘r’ is calculated through the following formula:


Picture Image is available at PDF file


Where, x and y are values of variables, and n is size of the sample.

The value of correlation coefficient can be interpreted in the following manner:


If ‘r’ is equal to 1, then there is perfect positive correlation between two values;


If ‘r’ is equal to -1, then there is perfect negative correlation between two values;


If ‘r’ is equal to zero, then there is no correlation between the two values.


Results and Discussions

The respondents were to indicate whether their SACCOs had a sound credit risk monitoring system. Results indicated that 93% of the respondents said that their SACCOs had a sound credit risk monitoring system while 7% of the respondents indicated that their SACCOs did not. According to (Onuko, et al., 2015), the board of directors should have responsibility for


approving and periodically (at least annually) reviewing the credit risk system and significant credit risk policies of the SACCO. The system should reflect the SACCO’s tolerance for risk and the level of profitability the SACCO expects to achieve for incurring various credit risks. This is evident based on the responses as indicated by the findings in figure (1).


Figure Image is available at PDF file

Figure 1: Sound credit risk monitoring system


The survey asked respondents to indicate the extent of their agreement on the various aspects of credit risk environment. The statistics are summarized in table (1). Findings from the study indicate that the senior management in our SACCO develops policies and procedures for identifying, measuring, monitoring and controlling credit risk had the highest mean of 4.4444 and a standard deviation of 0.57188. The findings concur with what (Nyong’o, 2014) conclusion that most SACCOs in Kenya operate under a sound credit risk monitoring process that reduces loan default which leads to low non-performing loans. The study also concluded that SACCOs take into consideration potential future changes in economic conditions when assessing individual credits and their credit portfolios. For proper credit monitoring process, SACCOs should have management information systems that provide adequate information on the composition of the credit portfolio which is the sole responsibility of the senior management.







Std.


N

Min

Max

Mean







Deviation

The board of directors approves the credit risk






strategy and significant credit risk policies of the

54

1.00

5.00

4.1111

1.14376

SACCO






The senior management in our SACCO strictly






implements the credit risk strategy approved by the

54

3.00

5.00

4.2778

.68451

board of directors






The senior management in our SACCO develops






policies and procedures for identifying, measuring,

54

3.00

5.00

4.4444

.57188

monitoring and controlling credit risk






The credit risk policies and procedures developed






address credit risk in all the SACCOs activities and at

54

2.00

5.00

3.9444

.85598

both the individual credit and portfolio levels






Our SACCO identifies and manages credit risk inherent







54

2.00

5.00

3.7778

.71814

in all products and activities






The SACCO subjects new credit products and activities






to adequate risk management procedures and

54

2.00

5.00

3.9815

.98077

controls before being introduced or undertaken






Valid N (listwise)

54












Extent of establishment of an appropriate credit risk monitoring

We evaluated the extent to which the SACCOs had established an appropriate credit risk environment was. The respondents were to indicate their level of agreement as no extent, little extent, moderate extent, great extent and very great extent. The findings are summarized in Table (2) indicate that the extent of establishment of an appropriate credit risk monitoring was at moderate extent (51%) and great extent (31%).


Table 2: Extent of establishment of an appropriate credit risk environment Extent of establishment of appropriate Frequency Percentage credit risk monitoring



No extent

0

0


Little extent

6

11


Moderate extent

28

51


Great extent

16

31


Very great extent

4

7


Total

54

100






Extent of an appropriate credit risk monitoring effect on performance

The extent to which the SACCOs’ appropriate credit risk monitoring effect on performance was evaluated. In this study, the respondents were to indicate their level of agreement as no extent, little extent, moderate extent, great extent and very great extent. Findings abridged in Table (3) indicate that the extent of appropriate credit risk environment effect on performance was at a great extent (54%).


Table 3: Extent of an appropriate credit risk monitoring effect on performance. Extent of an appropriate credit risk monitoring Frequency Percentage effect on performance



No extent

4

7





8

15


Little extent


Moderate extent

7

13


Great extent

29

54


Very great extent

6

11


Total

54

100


Regression Analysis

A linear regression analysis was conducted to test relationship between the variables. Table (4), provides the summary of the regression model applied in this study. Coefficient of determination explains the extent to which changes in dependent variable can be explained by the change in the independent variable or the percentage of the variation in the dependent variable (Performance) that is explained by all the independent variable (Credit risk monitoring).


Table 4: Model summary




Adjusted R


Model

R

R Square

Square

Std. Error of the Estimate

1

.936(a)

.875

.870

.18705

Predictor: (Constant), Credit risk monitoring


According to the direct relationship model applied in this study, Adjusted R Square was 0.870 implying that the independent variable explain 87.0% of the relationship between credit risk monitoring and performance in SACCOs in Kenya. The ANOVA report which assesses the overall significance of the regression model applied in this study indicated that, p<0.5 (Sig. =0.000) and therefore our model was significant at 95% confidence level.


Table 5: ANOVA


Sum of





Model

Squares

Df

Mean Square

F

Sig.







Regression

7.626

1

7.626

59.260

.000(a)

Residual

6.691

52

.129



Total

14.317

53




a. Predictors: (Constant), Credit risk monitoring

b. Dependent Variable: Performance


Table (6) projects the coefficients for the regression model applied in the study; a composite index was calculated by finding the average of the independent variable and the dependent variable. It indicates the means provided figures used in obtaining coefficients using SPSS are indicated in Table (6).


Table 6: Table of Coefficients


Unstandardized

Standardized




Coefficients

Coefficients





Std.





B

Error

Beta

t

Sig.

(Constant)

-.259

.107


-2.417

.019

Credit risk monitoring

.304

.026

.337

11.807

.000

  1. Dependent Variable: Performance


The results in Table (6) imply that credit risk monitoring relates positively with performance, the relationship is statistically significant at the 99% confidence level (β=0.337, p<0.01; p=0.000).


Conclusion

The primary purpose of the paper was to investigate the effect of credit risk monitoring and performance in SACCOs in Kenya. Findings indicated 93% of the respondents denoted that their SACCOs had a sound credit risk monitoring system. From the indicators enlisted, the senior management in the SACCOs developing policies and procedures for identifying, measuring, monitoring and controlling credit risk was an important key credit risk aspect since it had the highest mean of 4.4. Results further indicated that the senior management in the SACCO strictly implementing the credit risk strategy approved by the board of directors was another key credit risk environment aspect hence positively affecting the performance of SACCOs in Kenya. Further, the extent of establishment of an appropriate credit risk monitoring was at moderate extent (51%) and great extent (31%). The inferential statistics analysis of the study findings indicated that credit risk monitoring positively affected performance of the SACCOs in Kenya. We therefore conclude that the performance of saccos is significantly affected by credit risk monitoring.


References

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