The Credit Risk Problem—A Developing Country Case Study

Abstract

Crediting represents one of the biggest risks faced by the banking sector, and especially by commercial banks. In the literature, there have been a number of studies concerning credit risk management, often involving credit scoring systems making use of machine learning (ML) techniques. However, the specificity of individual banks’ datasets means that choosing the techniques best suited to the needs of a given bank is far from straightforward. This study was motivated by the need by Credins Bank in Tirana for a reliable customer credit scoring tool suitable for use with that bank’s specific dataset. The dataset in question presents two substantial difficulties: first, a high degree of imbalance, and second, a high level of bias together with a low level of confidence in the recorded data. These shortcomings are largely due to the relatively young age of the private banking system in Albania, which did not exist as such until the early 2000s. They are shortcomings not encountered in the more conventional datasets that feature in the literature. The present study therefore has a real contribution to make to the existing corpus of research on credit scoring. The first important question to be addressed is the level of imbalance. In practice, the proportion of good customers may be many times that of bad customers, making the impact of unbalanced data on classification models an important element to be considered. The second question relates to bias or incompleteness in customer information in emerging and developing countries, where economies tend to function with a large amount of informality. Our objective in this study was identifying the most appropriate ML methods to handle Credins Bank’s specific dataset, and the various tests that we performed for this purpose yielded abundant numerical results. Our overall finding on the strength of these results was that this kind of dataset can best be dealt with using balanced random forest methods.

Keywords

• credit risk
• machine learning
• random forest