PREDICTION OF LOAN REDEMPTION IN A TRANSITION COUNTRY: A COMPARISON OF LOGIT MODELS AND ARTIFICIAL NEURAL NETWORKS

Abstract

Banks provide a financial intermediary service by channeling funds efficiently between borrowers and lenders. Bank lending is subject to credit risk when loans are not paid back on a timely basis or are in default. The ability or possessing a methodology to evaluate the creditworthiness of a borrower is therefore crucial to managing the bank’s risk management and profitability. In transition countries like Poland, creditworthiness evaluation is especially difficult due to the transitional nature of the financial markets. This paper looks at a comparison of using logit models and artificial neural networks models to evaluate a borrower’s credit risk. In particular, this paper shows that artificial neural networks model is a better predictive tool than logit models of credit risk.