Drivers of Individual Credit Risk of Retail Customers—A Case Study on the Example of the Polish Cooperative Banking Sector

Abstract

The main aim of the research was to determine the key factors determining the level of credit risk of individual clients (clients in the form of natural persons, excluding companies) on the example of Polish cooperative banks according to the following features: transaction characteristics, socio-demographic characteristics of the customer, the customer’s financial situation, the customer’s history of cooperation with the cooperative bank where they applied for a loan, and the customer’s history of cooperation with other financial institutions. For the research gathered data from 1000 credit applications submitted by individual customers when applying for a credit in five different cooperative banks were used for the analyses. To assess the credit risk of retail clients we use logit regression models, and additionally, score cards were calculated. The results of the research indicate that among the factors with high predictive power there were the features characterizing the client’s history of cooperation with the cooperative bank, where they applied for a loan. It may mean that when assessing credit risk related to financing individual customers, cooperative banks due to their local character, have an advantage over other financial institutions.

Credit Risk Prediction Based on Psychometric Data

“…Recent studies in credit risk assessment have emphasized the diverse factors contributing to individual credit risk. Balina and Idasz-Balina [25] examined the drivers of individual credit risk within the cooperative banking sector, highlighting the importance of localized financial conditions. Mhlanga [26] discusses the role of machine learning and artificial intelligence in enhancing financial inclusion in emerging economies through more accurate credit risk assessment.…”