A comparative analysis of machine learning algorithms for predicting probabilities of default

TL;DR

This paper compares five machine learning algorithms with logistic regression for predicting loan default probabilities, highlighting their relative strengths and weaknesses in credit risk analysis.

Contribution

It provides a comparative analysis of ML algorithms versus logistic regression for PD prediction using a benchmark dataset, offering insights into their effectiveness.

Findings

XGBoost and Random Forest outperform logistic regression in accuracy.

Gradient Boosting shows competitive performance with better interpretability.

Decision Trees and AdaBoost have limitations in predictive power.

Abstract

Predicting the probability of default (PD) of prospective loans is a critical objective for financial institutions. In recent years, machine learning (ML) algorithms have achieved remarkable success across a wide variety of prediction tasks; yet, they remain relatively underutilised in credit risk analysis. This paper highlights the opportunities that ML algorithms offer to this field by comparing the performance of five predictive models-Random Forests, Decision Trees, XGBoost, Gradient Boosting and AdaBoost-to the predominantly used logistic regression, over a benchmark dataset from Scheule et al. (Credit Risk Analytics: The R Companion). Our findings underscore the strengths and weaknesses of each method, providing valuable insights into the most effective ML algorithms for PD prediction in the context of loan portfolios.