PSD2 Explainable AI Model for Credit Scoring

11/20/2020

∙

The aim of this paper 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 PSD2-related databases. The input data were obtained solely from synthetic account transactions generated from a pool of commercial banks from a pool of Italian commercial banks. Over the total proven models, CatBoost has shown the highest performance. The algorithm implementation produces a GINI of 0.45 after tuning the hyper-parameters combined with their inherent class-weight resampling method. 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.

READ FULL TEXT

PSD2 Explainable AI Model for Credit Scoring

Transparency, Auditability and eXplainability of Machine Learning Models in Credit Scoring

Enabling Machine Learning Algorithms for Credit Scoring – Explainable Artificial Intelligence (XAI) methods for clear understanding complex predictive models

Analyzing Machine Learning Models for Credit Scoring with Explainable AI and Optimizing Investment Decisions

Predicting Credit Risk for Unsecured Lending: A Machine Learning Approach

Efficient Commercial Bank Customer Credit Risk Assessment Based on LightGBM and Feature Engineering

Monotonic Neural Additive Models: Pursuing Regulated Machine Learning Models for Credit Scoring

Explainable Ordinal Factorization Model: Deciphering the Effects of Attributes by Piece-wise Linear Approximation

PSD2 Explainable AI Model for Credit Scoring

Related Research

Transparency, Auditability and eXplainability of Machine Learning Models in Credit Scoring

Enabling Machine Learning Algorithms for Credit Scoring – Explainable Artificial Intelligence (XAI) methods for clear understanding complex predictive models

Analyzing Machine Learning Models for Credit Scoring with Explainable AI and Optimizing Investment Decisions

Predicting Credit Risk for Unsecured Lending: A Machine Learning Approach

Efficient Commercial Bank Customer Credit Risk Assessment Based on LightGBM and Feature Engineering

Monotonic Neural Additive Models: Pursuing Regulated Machine Learning Models for Credit Scoring

Explainable Ordinal Factorization Model: Deciphering the Effects of Attributes by Piece-wise Linear Approximation