Application of One Class Models for Financial Risk Classification

Abstract

This project explores the use of One Class Classification methods to predict credit risk in highly imbalanced financial datasets. Unlike traditional supervised models, OCC approaches focus only on the majority class, in this case, customers with good payment behaviour, and aim to detect unusual patterns that might suggest a higher risk of default. The study is divided into three experimental phases. The first phase uses a limited set of 13 variables, selected and categorised by experts based on risk. The second phase removes this expert selection and uses all available features. In the third phase, a hybrid strategy is tested by adding the anomaly scores generated by OCC models as extra input variables to supervised models. The models are evaluated using ROC AUC and PR AUC, two metrics well suited for imbalanced classification problems. The main goal is to analyse whether anomaly detection techniques can support or improve current risk assessment strategies in a real business setting. However, the results did not confirm the initial hypothesis, as One Class models and hybrid approaches did not outperform traditional supervised methods.