PSD2 Explainable AI Model for Credit Scoring

• URL: http://arxiv.org/abs/2011.10367v3
• Date: Fri, 6 Aug 2021 16:18:04 GMT
• Title: PSD2 Explainable AI Model for Credit Scoring
• Authors: Neus Llop Torrent (1 and 2), Giorgio Visani (2 and 3), Enrico Bagli (2) ((1) Politecnico di Milano Graduate School of Business, (2) CRIF S.p.A, (3) University of Bologna School of Informatics and Engineering)
• Abstract summary: The aim of this project is to develop and test advanced analytical methods to improve the prediction accuracy of Credit Risk Models. The project focuses on applying an explainable machine learning model to bank-related databases.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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