Customer Repay/Default of Loan Prediction Modeling with Multiple Discriminant Analysis. A Case Study of Financial Institutions in the Eastern Region of Ghana
A PAPER PRESENTED AT THE KOFORIDUA POLYTECHNIC’S SECOND (2ND ) APPLIED RESEARCH CONFERENCE: - 22ND to 23RD April, 2009
Godfred Kwame Abledu(firstname.lastname@example.org)
Department of Applied Mathematics, Koforidua Polytechnic
Banks that lend to small businesses and individuals need to quickly assess the creditworthiness of prospective borrowers so as to reduce the probability of issuing bad loans while attempting to maintain their own profitability. It was for these reasons that credit institutions have made several attempts at modeling and reliably forecasting credit default using numerous statistical approaches.
The objective of the study was to develop a model which could be used to identify likely future defaulters. The population for the study was all financial institutions in the Eastern Region of Ghana. A bank that could give the needed data for the study was purposefully chosen. Data on a sample of 150 customers was analysed using the SPSS version 17.
The study identified four important influences - total asset, total income, family size and number of years with current employer as the most discriminating variables between the repay and default group. The validity of the model was confirmed using several diagnostic analytical procedures. The importance of examining a model’s sensitivity and specificity in the context of one’s specific, real-world objectives was also discussed.
Key words: Repay; default; creditworthiness; prospective borrowers;, bank loan; model; discriminant function; discriminating variables.