Emenike O. Kalu
Kampala International University, Uganda
E-mail: emenikekaluonwukwe@yahoo.com
Bashabe Shieler
Kampala International University, Uganda
E-mail: tumwineshela@gmail.com
Christian U. Amu
Federal University of Technology Owerri, Nigeria
E-mail: chrisumu@yahoo.com
Submission: 05/04/2017
Accept: 04/07/2017
ABSTRACT
The objective of this study was to
evaluate whether relationship exist between credit risk management techniques
and financial performance of microfinance institutions in Kampala, Uganda. Specifically, the study
examined whether there is a relationship between credit risk identification,
credit risk appraisal, credit risk monitoring, credit risk mitigation and
financial performance of microfinance institutions in Kampala using sample of
60 members of staff in finance and credit departments of three licensed
microfinance institutions in Kampala, Uganda namely Finca Uganda Ltd, Pride Microfinance Ltd, UGAFODE Microfinance Ltd.
Primary data was collected using questionnaires and it comprised of closed
ended questions. Secondary data was collected from the microfinance
institutions (MDI’s) annual reports (2011 - 2015). Frequencies and descriptive
statistics were used to analyse the population. Pearson linear correlation
coefficient was adopted to examine relationship between credit risk management
techniques and financial performance. The findings indicate that credit risk
identification and credit risk appraisal has a strong positive relationship on
financial performance of MDIs, while credit risk monitoring and credit risk
mitigation have moderate significant positive relationship on financial
performance of MDIs. The study recommends, among others, that the credit risk
appraisal process should identify and analyse all loss exposures, and measure
such loss exposures. This should guide in selection of technique or combination
of techniques to handle each exposure.
The study concludes that MDIs should continually emphasise effective
credit risk identification, credit risk appraisal, credit risk monitoring, and
credit risk mitigation techniques to enhance maximum financial performance.
JEL Classification Numbers:
G21, G32, N27
Keywords: credit risk management, credit
risk identification, credit risk appraisal, credit risk monitoring, credit risk
mitigation, financial performance, microfinance institution, Uganda
1. INTRODUCTION
Credit risk,
according to Basel (2000), is the potential that a bank borrower or
counterparty will fail to meet its obligations in accordance with agreed terms. It is a risk of borrower default, which occurs when
counterparty defaults on repayment. The reasons for loan default / loan
delinquency are when the obligor is in a financially stressed situation (GESTEL;
BAESENS, 2008).
Inadequate financial analysis,
inadequate loan support according to (SHEILA, 2011) are the causes of loan
default. Credit risk management is the identification, measurement, monitoring
and control of risk arising from the possibility of default from loan repayment
(EARLY, 1966; COYLE, 2000).
Credit risk management also refers
to the systems, procedures and controls, which a company has in place to ensure
the efficient collection of customer payments thereby minimising the risk of
non-payment (MOKOGI, 2003).
There are different techniques of
credit risk management that this study will focus on, they include credit risk
identification, credit risk appraisal, credit risk monitoring and credit risk
mitigation.
Numerous countries across the globe have implemented credit
risk management in their business operations because of the understanding that
risk exists as part of an environment in which various organizations operate
(TCHANKOVA, 2002). The concept of credit is old and can be traced back in
history.
Ditcher (2003) observes that banks in USA gave credit to
customers with high interest rate which discouraged borrowing. As a result, the
concept of credit did not become popular until the economic boom in USA in1885
when banks had access to liquidity and wanted to lend excess cash.
In Africa, credit was largely appreciated in the 1950’s
when most of banks started opening the credit sections and departments to give loans to white settlers. Uganda is one of
developing countries in Africa that has recently started to promote
microfinance institutions. As a result, non-performing loans is on the increase
thus lowering the level performance of microfinance institutions (MDI’s).
Available statistics from the Bank of Uganda annual
supervision report, 2015 indicates high incidence of credit risk reflected by
increasing non-performing loans (NPLs) by MDI’s. The situation has adversely
impacted on their profitability and overall asset quality has deteriorated.
The NPL ratio (NPLs to total gross loans) increased from
3.2% in December 2011 to 5.3% December 2012 it decreased marginally in December
2013 to 3.4% and again rose to 4.2% in December 2014 and then rose to 6.6% in
December 2015. This trend not only threatens the viability and sustainability
of MDI’s but also hinders the goals for which they were intended to achieve
that is provision of micro finance services mainly to small and medium
enterprises (SME’s) (MFPED, 2001).
Failure to control credit, could result in insolvency as
success of MDI’s largely depends on the effectiveness of their credit risk
management practices (ALFRED, 2011).
There have been a number of studies on credit risk
management and financial performance both in developed and developing countries
(see for example, OTIENO; NYAGOL; ONDITI, 2016; ALSHATTI, 2015). There is no
study, to the researcher’s knowledge, that has examined the relationship
between credit risk management techniques and financial performance of Micro
finance deposit taking institutions in Kampala, Uganda. As a result, this study
intends to close the empirical literature gap.
The objective of this study therefore, was to evaluate the
relationship between credit risk management techniques and financial
performance of microfinance institutions in Kampala, Uganda. Specifically, the
study aimed at analysing whether a relationship exist between credit risk
identification, credit risk appraisal, credit risk monitoring, credit risk
mitigation and financial performance of microfinance institutions in Kampala
Uganda.
Understanding
the relationship between credit
risk management techniques and financial performance will benefit microfinance
institutions and monetary authorities. Microfinance
institutions, for example, will adopt appropriate credit risk management measures
to avoid default from a borrower or counterparty to meet its obligations in
accordance with agreed terms.
Monetary
authorities will
also benefit through the understanding of effective credit risk management
techniques and thus make proactive policies measures to enforce their adoption,
thereby control accumulation of non-performing loans. The study will further
enrich existing knowledge on relationship between credit risk management
techniques and financial performance of microfinance institutions in Africa as
well as provide literature for future researchers on related subject.
The remainder of this paper organised as follows: Section 2
contains review of empirical literature. Section 3 describes data and
methodology. Section 4 presents results ad discussions, and section 5 provides
conclusions and recommendations.
2. REVIEW OF EMPIRICAL LITERATURE
Various researches have analysed the linkage between credit
risk management and financial performance, and how effective credit risk
management contributes to reduction of defaults by counterparty as well as
restricting uncertainty of achieving the required financial performance.
Otieno, Nyagol and Onditi (2016) evaluated the relationship between credit risk
management and financial performance of microfinance banks in Kenya using
Pearson correlation coefficient.
The population of the study comprised of 12 licensed
microfinance Banks. Longitudinal research design utilising panel data covering
the period from 2011 to 2015 was used. The results show that credit risk
management with PAR and LLPCR parameters had a strong negative correlation with
both ROAA and ROAE performance measure. Thus, the study concludes that credit
risk management impacts performance of MFBs.
The study recommends that credit managers should operate
under a sound credit granting process with well-defined credit-granting
criteria detailing the MFB’s target market, a thorough understanding of the
borrower’s purpose and source of repayment.
Justus, Dickson and Harrison (2016) assessed the influence
of credit risk management practices on loan delinquency in SACCOS in Meru
County, Kenya. The study adopted a descriptive research design and the
population consisted of all the 44 credit officers of SACCOs in Meru County.
Questionnaire was used to collect data. Multiple linear
regressions were used in data analysis. Analyzed data was presented in
percentages and frequency tables. The study revealed that there exist a strong
relationship between credit risk controls, collection policy and loan
delinquency in SACCOs.
Thus the study concludes that credit risk management
practices significantly influenced loan delinquency in SACCOs in Meru County.
The study recommends adoption of a more stringent policy on credit risk management
practices in SACCOs for effective debt recovery
Kimotho and Gekara (2016) conducted a study on the effect
of credit risk management and financial performance of commercial banks in
Kenya. The purpose of study was to examine effect of credit risk management
practices on financial performance of commercial Bank in Kenya.
The study adopted descriptive research design and target
population consisted of credit risk managers, credit analyst and debt recovery
managers. The study revealed that credit risk management procedures are used to
influence profitability of the bank positively and also recommends the
management of the banks to oversee facilitation of credit risk management as a
substantial degree of standardisation of process and documentation.
The study recommended that the bank should consider risk
identification as a process in credit risk management and focus on interest
risks and foreign exchange risks to great extent in the risk identification
map.
Alshatti (2015) examined the effect of credit risk
management on financial performance of the Jordanian commercial banks during
the period 2005 to 2013. Thirteen commercial banks were chosen to express on
the whole Jordanian commercial banks.
The research revealed that the credit risk management affects
financial performance of the Jordanian commercial banks as measured by ROA and
ROE. Based on findings, the researcher recommends amongst others that banks should
improve their credit risk management to achieve more profits, banks
should take into consideration the indicators of non-performing loans/gross
loans, and that banks should establish adequate credit risk management policies
by imposing strict credit estimation before granting loans to customers.
Lagat, Mugo, and Otuya (2013) analysed the effect of credit
risk management practices on lending portfolio among savings and credit
cooperatives in Kenya using data on risk identification, risk analysis, risk
monitoring, risk evaluation and risk mitigation obtained from 59 SACCOs in
Nakuru County. The study applied regression models in the analysis, and the
results indicate significant effect of all the risk management practices on
loan portfolio except risk evaluation which did not register significant effect
on the lending portfolio of the SACCOs.
The findings further showed almost all (99%) the
respondents who participated in the study noted that monitoring was part of
their credit management activities and it was influencing their lending
portfolio to a great extent. From the findings of the study it was concluded
that majority of the SACCOs have adopted largely risk management practices as a
means of managing their portfolio.
Moti, Masinde, Mugenda, & Sindani (2012) examined the
effectiveness of credit management system on loan performance of microfinance
institutions. Specifically it sought to establish the effect of credit terms,
client appraisal, credit risk control measures and credit collection policies
on loan performance. The researchers adopted a descriptive research design.
The respondents were the credit officers of the MFIs in
Meru town. The results show that the credit management system variables have
significant impact on loan performance of microfinance institutions. It also
reports that collection policy has a higher effect on loan repayment at 5%
significance level. The study recommends
that microfinance institutions should consider credit insurance, signing of
covenants, credit rating, reports on financial condition, and diversification
in granting loans.
Mulondo (2011) investigated the relationship between credit
risk management and loan performance of two development finance institutions in
Uganda. The study found that loan appraisal showed a very strong significant
relationship as compared to other risk management techniques such as risk
transfer and risk diversification.
The study recommends that considering that there is a
significant positive relationship between loan appraisal and loan performance,
it is important for the bank to formulate appraisal process/procedures, format
that details ways of capturing all the credit risk.
The appraisal process should identify and analyze all loss
exposures, and measure such loss exposures. The appraisal process should
capture key issues like capital adequacy, capacity of applicant, value of
collateral, and repayment history.
Mutangili (2011) analysed the relationship between credit
risk management practices and the level of non-performing loans for commercial
banks in Kenya. The study documented evidence of negative linkage between the level
of non-performing loans and credit risk management practices in banks. He
concludes that level of non-performing loans is inversely related to credit
risk management practices.
He therefore recommends that commercial banks should adopt
various credit risk management practices to reduce the level of non-performing
loans. In addition, he further recommends that sustainable and reliable credit
database should be established for availability of credit information needed by
banks.
Ochola (2009) evaluated the relationship between credit
risk management and non- performing loans. The study show that a combination of
intensive credit risk management by the banks coupled with close supervision by
central bank has greatly enhanced the decline of non-performing loans ratio in
the banking sector.
Analysing the asset quality of financial sector for 2003 to
2008, the ratio of gross non-performing loans to gross loans declined from a
high 35% in 2003 to a low of 9.23 in 2008. This decline supports evidence of
close relationship of nonperforming loans and credit risk management.
The study employed both primary and secondary data. Primary
data was obtained through self-administered questionnaires distributed to staff
in the three MDI’S especially those with a credit function or with prior
experience in the credit function. The questionnaire was selected as an
instrument to collect the data because it is straight forward and less time
consuming for respondents.
The questionnaires were structured and were administered
through drop and pick later method and the researcher used e-mails and
telephone calls to contact the respondents. The secondary data was obtained by
analysing financial statements and annual reports of MDIs for the period of 5
years from year 2011 to 2015.
The target population of the study was 60 members of staff
in finance and credit departments from three licensed microfinance institutions
in Kampala, Uganda namely Finca Uganda Ltd, Pride Microfinance Ltd, UGAFODE
Microfinance Ltd. These respondents were considered for the study because of
knowledge and skills they possess in relation to the variables under the study
A census survey method was adopted in this study where the
researcher used 60 respondents (credit risk managers, credit officers,
Auditors, accountants and debt recovery officers) from the branches of the
three licensed microfinance institutions in Kampala, Uganda. Census survey
method was used because the target population was manageable and data was
collected from the whole population (SINGH; MASUKU, 2014).
Validity
was determined using Content Validity Index (C.V.I). C.V.I= (no of questions
declared valid/total no of questions). A CVI of 0.86 was used to declare that
the research instrument was valid since it was above 0.7 which is the minimum
CVI index required to declare a research instrument valid (AMIN, 2005).
An
instrument is reliable if it produces the same results whenever it is
repeatedly used to measure trait or concept from the same respondents even by
other researchers (AMIN, 2005). Reliability (internal
consistency and stability) of the instruments was tested using Cronbach’s Alpha
Coefficient (CRONBACH, 1946). The
Cronbach’s Alpha coefficient test indicated that the questionnaires where
reliable since the coefficient was above 0.5 (α=0.74).
The secondary data was analysed using Pearson‘s linear
correlation coefficient (PLCC) to determine the nature of relationship between
credit risk management and financial performance. The sign (+ or -) indicates
the direction of the relationship. The value can range from -1 to +1, with +1
indicating a perfect positive relationship, 0 indicating no relationship and -1
indicating a perfect negative or reverse relationship. The PLCC r is specified
thus:
Where, r is the correlation coefficient, n is the number of
observations, x and y are dependent and independent variables, in this case
credit risk management and financial performance. All data was analyzed at 5%
level of significance. Thus if the p-value was less than 0.05 the null
hypotheses were rejected. If, on the other hand, the p-value was greater than
0.05, the null hypotheses were not rejected.
4.1.
Analysis
of Respondents
The results that follow show the background characteristics
of the respondents that were involved in the study. 60 questionnaires were administered to
respondents in three licensed MDI’S in Kampala, Uganda namely Finca Uganda Ltd,
Pride Microfinance Ltd and UGAFODE Microfinance Ltd. Overall 60 responded to the questionnaires
which represented a response rate of 100% as reflected in the table 1 below.
|
Frequency |
Percent |
Cumulative Percent |
|
Valid |
Finca Uganda Limited |
18 |
30.0 |
30.0 |
|
Pride Microfinance Ltd |
23 |
38.3 |
68.3 |
|
Ugafode microfinance
Limited |
19 |
31.7 |
100.0 |
|
Total |
60 |
100.0 |
|
Source: primary data 2016
According to the results in the table 1 above, the greatest
number of respondents were from Pride Microfinance Ltd (23 respondents)
representing 38.3% of the total number of respondents followed by Ugafode
Uganda Ltd (19 respondents) representing 31.7% and Finca Uganda Limited (18
respondents) representing 30.0%.
Table 2: Respondent demographic characteristics
Gender |
Frequency |
Percentage |
Male |
33 |
55.0 |
Female |
27 |
45.0 |
Total |
60 |
100 |
Source: primary data 2016
According to the findings presented in table 2 above the
researcher established that majority of the respondents were male as shown by
55.0% whereas 45.0% of the respondents were female, this shows that both male
and females were well represented in this study and thus the finding of the
study did not suffer from gender bias
Table 3: Age of the respondents
Age |
Frequency |
Percentage |
20-30 years |
24 |
40.0 |
31-40 years |
30 |
50.0 |
41-50 years |
6 |
10.0 |
Total |
60 |
100 |
Source: primary data 2016
The study requested the respondents to indicate their age
category, from the findings as shown in table 3 above the study established
that majority of the respondents as shown by 50.0% were aged between 31 to 40
years. This could imply that this is the most active and mobile age group which
a microfinance institution can use in the supervision and monitoring of its
volatile loan portfolio. 40.0% of the
respondents were aged between 20 to 30 years and the remaining 10% of the
respondents were aged between 41 to 50 years. This is an indication that
respondents were well distributed in terms of age.
Table 4: Highest Education level Achieved
Education level |
Frequency |
Percentage |
Diploma |
4 |
6.7 |
Degree |
47 |
78.3 |
Master’s Degree |
8 |
13.3 |
>=PHD |
1 |
1.7 |
Total |
60 |
100 |
Source: primary data 2016
The study requested respondents to indicate their highest
education level, from the findings in table 4 above, 78.3% of the respondents
are bachelor degree holders, 13.3% of the respondents indicated their highest
education level as master degree, whereas 6.7% of the respondents are diploma
holders, and 1.7% of the respondents indicated their highest education level as
PhD. This is an indication that majority of the employee engaged in this
research had university degree certificates as their highest level of
education.
Figure 1: Position in
the institution
Source: primary data 2016
The study sought to determine the current rank of
respondents within organization. From the research findings, the study showed
that majority of the respondents as shown by 40.0% indicated to be the credit
officers, 18.3% of the respondents indicated to be the recovery officers
likewise 18.3% of the respondents indicated to be the accountants, 16.7% of the
respondents indicated to be the auditors and the remaining 6.7 % of the
respondents indicated no answer. This indicates that majority of the
respondents are credit officers.
Table 5: Previous Working Knowledge from Credit
|
Frequency |
Percentage |
Yes |
56 |
93.3 |
No |
04 |
6.7 |
Total |
60 |
100.0 |
Source:
primary data 2016
From table 5 above, about 93.3% of the respondents have
prior experience of working in the credit section and 6.7% has no prior
experience in credit department. This indicates that majority of the
respondents have prior experience working in the credit section.
Table 6 : Working experience in the banking
sector.
|
Frequency |
Percentage |
<2 years |
3 |
5.0 |
2 -5 years |
43 |
71.7 |
6- 10 years |
14 |
23.3 |
Total |
60 |
100.0 |
Source: primary data 2016
The results in the table 6 above indicate that 71.7% of respondents had working experience
in banking sector of 2 to 5 years, 23.3 % of respondents had working experience
in banking sector of 6 to 10 years and 5. 0 % of respondents had working
experience in banking sector of less than 2years. This indicates that majority
of the respondents had working experience in banking sector of 2 to 5 years.
Pearson linear correlation coefficient (PLCC) was used to
establish relationship between the variables considered in the model thus;
return on equity, credit risk identification, credit risk appraisal, credit
risk monitoring and credit risk mitigation. The results of PLCC are presented
in Table 7 below.
Table 7: Pearson linear correlation coefficient
|
|
ROE |
CRI |
CRA |
CRM |
CRMI |
ROE |
Correlation
Coefficient |
1.000 |
|
|
|
|
|
Sig.
(2-tailed) |
. |
|
|
|
|
CRI |
Correlation
Coefficient |
.647(*) |
1.000 |
|
|
|
|
Sig.
(2-tailed) |
.033 |
. |
|
|
|
CRA |
Correlation
Coefficient |
659(**) |
281 |
1.000 |
|
|
|
Sig.
(2-tailed) |
.008 |
.236 |
. |
|
|
CRM |
Correlation
Coefficient |
.514(*) |
.764(**) |
.477 |
1.000 |
|
|
Sig.
(2-tailed) |
.017 |
.002 |
.067 |
. |
|
CRMI |
Correlation
Coefficient |
508(*) |
315 |
.176 |
.662(*) |
1.000 |
|
Sig.
(2-tailed) |
.011 |
.227 |
.177 |
.043 |
. |
Note: **, * are significant correlations at the
0.01 and 0.05 levels (2-tailed) respectively.
As presented in table 7 above, p- value=0.033< 0.05 and
because the p-value (0.033) is less than the significance level (0.05), the
null hypothesis is rejected. This implies that a strong significant positive
relationship exist between credit risk identification and financial performance.
This finding is in line with the study of Kimotho and
Gekara (2016) who noted that there was positive association between credit risk
identification and financial performance of commercial banks in Kenya. This is
also consistent with the view of Fuser, Gleiner, and Meier (2011) who asserted
that risk identification process includes risk-ranking components and that they
help in sorting risk according to their importance and assists the management
to develop risk management strategy to allocate resources efficiently and
therefore improving their credit performance.
The computed correlation coefficient is 0.659 and p-
value=0.008< 0.05 and because the p-value (0.008) is less than the
significance level of (0.05), the null hypothesis is rejected. This implies that
there exists a strong significant positive relationship between credit risk
appraisal and financial performance.
This study finding concurs with the finding of Kimotho and
Gekara (2016) whose study revealed that there was a positive effect between
credit appraisal analysis and financial performance of commercial banks in
Kenya. He added that commercial banks used credit appraisal analysis to a great
extent.
Kurui and Aquilars (2012) in their study also revealed a
positive association between client appraisal and loan performance in MFIs in
Baringo County. Mulondo (2011) also document evidence to show that loan
appraisal has a very strong significant relationship as compared to other risk
management techniques like risk transfer and risk diversification.
The computed correlation coefficient is 0.514 and p-
value=0.017< 0.05and because the p-value (0.017) is less than the
significance level of (0.05), the null hypothesis is rejected. The researcher
revealed that there exists a moderate significant positive relationship between
credit risk monitoring and financial performance.
This implies that monitoring reports should be frequent,
timely, accurate and informative and should be distributed to appropriate
individuals to ensure action. The study is in line with literature of IRM,
AIRMIC and ALARM (2002) that an effective risk monitoring requires reporting
and review structure to ensure that risks are effectively identified and
assessed and that appropriate controls and responses are in place.
The computed correlation coefficient is 0.508 and p-
value=0.011< 0.05 and because the p-value (0.011) is less than the
significance level of (0.05), the null hypothesis is rejected. This implies
that there exists a moderate significant positive relationship between credit
risk mitigation and financial performance which concurs with the work of
Brannan, (2000), who argued that diversification, is the primary tool for
lenders to control borrower risk and realize financial performance.
The findings also concur with the study of Wilson (1998),
who advocates for diversification of loan portfolio across nations where the
benefits are much stronger than they are when diversification occurs across sectors in a
given economy. The study of Justus, Dickson, and Harrison (2016) also show that
a significant relationship exist between credit risk control and loan
delinquency in SACCOS in Meru County, Kenya.
The findings of study are also in line with the study of
Moti, Masinde, Mugenda and Sindani (2012) who documented that credit insurance,
signing covenants with customers, diversification of loans and credit rating of
customers had a positive effect on loan performance.
5. CONCLUSIONS AND RECOMMENDATIONS
From the findings of the study, we observe that majority of
respondents in this study ranged between 31-40 years (45.1%) and majority of
respondents were male (55.0%) and these respondents had attained bachelors'
degree as their highest academic qualification (78.3%), majority of
respondents had working experience in
the banking sector of 2-5 years (71.7%). We therefore conclude that credit risk
identification, credit risk appraisal, credit risk monitoring, and credit risk
mitigation are strong variables in determining financial performance of MDIs in
Kampala, Uganda.
Consequently, we recommend that credit risk identification
should not be a one off thing as some risks could be hard to detect or
overlooked by those tasked to identify them and therefore it should be a
continuous process which is carried out at different places and by different
individuals. It is important for the MDI’s to formulate an appraisal process/
procedures, format that details ways of capturing all the credit risk.
The appraisal process should identify and analyze all loss
exposures, and measure such loss exposures. This should guide in selection of
technique or combination of techniques to handle each exposure. The appraisal process should capture key
issues like the capitalisation of the business, capacity of the applicant, value
of the collateral, and repayment history and conditions that is economical,
political before a project is financed.
We also recommend that MDI’s should enhance their
credit risk monitoring techniques so as to improve their financial performance.
With through credit risk monitoring techniques, the MDI’s will be able to know
the credit score of their clients and also help the MDI’s management to
discover mistakes at an early stage thus take necessary steps to reduce their
non-performing loans. This will also prevent diversion and misapplication of
funds which are indentified as important causes of non-performing loans in
MDIs.
There is also need for MDIs to
enhance their credit risk mitigation this will help in decreasing default
levels as well as their non-performing loans. This will help in improving their
financial performance. There is also need for MDIs to increase use of insurance
firms in a bid to transfer or share risk in case of default. This may help in
decreasing loan default levels and improving their financial performance
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