Explainability and business sense in machine learning models for credit risk assesment

Abstract

The changing landscape of credit risk modeling, due to technological advancement and the proliferating generation of platforms with alternative financing alternatives to traditional institutions, has raised critical questions regarding the effectiveness of machine learning in meeting the demands of regulators and users, particularly in the P2P lending market. In this context, we address some gaps identified in the literature regarding modeling, its business-oriented applicability, and the interpretability of machine learning results. We propose alternative modeling strategies workflows, incorporating business sense predictability and explainability. We show that this approach not only improves the accuracy of predictions compared to traditional alternatives but also allows the identification of the critical variables and, in turn, risk profiles or segments of higher profitability...