An Evaluation of Bayesian Network Models for Predicting Credit Risk on Ugandan Credit Contracts

Abstract

Credit risk prediction is a task that continues to be studied extensively for various crediting schemes. Machine learning has become a viable option for developing credit risk prediction models for uncertain cases involving a considerable number of instances. In particular, the framework of Bayesian networks is a very suitable option for managing uncertainty. Although there have been several studies on the use of Bayesian networks in credit risk prediction, there is scarce literature about their application to cases from developing economy contexts. In this chapter, we exhaustively apply and evaluate several Bayesian network models for credit risk prediction based on cases from a Ugandan financial institution. Credit risk prediction quality from some Bayesian network models is satisfactory and compares with prediction quality from other state-of-the-art machine learning methods. Evaluation results show that one Bayesian network model learned through a global optimization hill climbing method always leads to the highest prediction quality so far on Ugandan credit contracts.