Effects of Credit Appraisal Methods on Non-Performing Loans in Government Owned Financial Institutions, A Case of Kenya Commercial Bank Limited

Abstract

Financial institutions have in the recent past become a major player in the Kenyan economy. Consequently, for the institutions to sustain viable credit programmes, the criteria for assessing credit risk are essential so as to minimize the loan default. There are various known methods and tools which can be employed by credit offices to ensure that they lend quality loan. These methods try to establish the creditworthiness of the potential borrower. Quality loans means good returns to the business since there is less provisions for bad debt in the books of the lending institution. One of the criteria for establishing the creditworthiness of a borrower is the C’s of credit model. These are initials for;-character, capacity , capital, collateral , common sense, contribution and conditions The C’s of credit refers to the borrower’s specific attributes which, if well used can help the lender arrive at a better decision of whether to lend or not to lend, the amount to lend and possibly the period to allow. Lending institutions, however, have continued to record non-performing loans, despite there being elaborate known methods to aid in credit appraisal. The objectives of this study were to establish the factors that have contributed to non performing loans in financial institutions, whether the appraising persons are well versed on C’s of credit and challenges affecting appraising persons during the appraising process. To arrive at reliable findings, the researcher engaged respondents from selected financial institutions who were supplied with questionnaires in order to collect the sought data. The researcher employed descriptive research design which entails distributing questionnaires to respondents. The respondents were sampled from twenty financial institutions where respondents were chosen randomly. The target population in this case was credit officers irrespective of their cadres. For ease of collection of data, the study was located in Nairobi County where most financial institutions are based. The data so collected was coded to facilitate analysis. The researcher employed both descriptive and inferential statistics to analyze the data. The researcher further employed the credit scoring model which uses data on observed borrower characteristics, either to calculate the probability of default or to sort out borrowers into different default risk classes.