PSD2 Explainable AI Model for Credit Scoring

TL;DR

This paper presents an explainable AI model for credit scoring that combines high predictive accuracy with interpretability, using CatBoost and SHAP to identify key features and provide understandable insights into credit risk predictions.

Contribution

The study introduces an explainable machine learning approach for credit scoring that achieves high accuracy while maintaining interpretability through SHAP analysis.

Findings

CatBoost achieved a GINI of 0.68 after tuning.

SHAP provides both global and local explanations of model predictions.

Top 20 features identified as most influential in credit risk prediction.

Abstract

The aim of this project is to develop and test advanced analytical methods to improve the prediction accuracy of Credit Risk Models, preserving at the same time the model interpretability. In particular, the project focuses on applying an explainable machine learning model to bank-related databases. The input data were obtained from open data. Over the total proven models, CatBoost has shown the highest performance. The algorithm implementation produces a GINI of 0.68 after tuning the hyper-parameters. SHAP package is used to provide a global and local interpretation of the model predictions to formulate a human-comprehensive approach to understanding the decision-maker algorithm. The 20 most important features are selected using the Shapley values to present a full human-understandable model that reveals how the attributes of an individual are related to its model prediction.