MACHINE LEARNING APPROACHES IN CREDIT RISK MODELLING IN B2B TRANSACTIONS IN THE NON-FINANCIAL SECTOR
TL;DR
This study evaluates the use of machine learning methods to predict credit risk in B2B transactions, using 4,828 observations from large companies in Bosnia and Herzegovina over a five-year period and shows strong predictive performance.
Abstract
Credit risk in business-to-business (B2B) transactions can threaten financial stability if it is not managed effectively. This study evaluates the use of machine learning (ML) methods to predict credit risk in B2B transactions, using 4,828 observations from large companies in Bosnia and Herzegovina over a five-year period. Three ensemble-based ML models (Bagging Decision Tree, Random Forest, and Gradient Boosting) were compared with logistic regression. All ML models showed strong predictive performance, with Gradient Boosting performing slightly better overall. Liquidity, activity, and leverage indicators were the most important predictors across all models, while non-financial variables made only a limited contribution. The findings highlight the importance of accounting information in credit risk assessment and are relevant to IFRS 9, where probability of default is a key input to expected credit loss estimation under the general approach. These results provide a basis for further development of credit risk models for non-financial companies.