Machine learning methods for short-term probability of default: A comparison of classification, regression and ranking methods

Abstract

Probability of default estimation via machine learning on historical data is widely studied in credit risk modeling. In this work, we investigated the use of machine learning for a finer-grained risk estimation task, namely spot factoring. Here, the goal is to estimate the likelihood that an invoice will be paid in an acceptable timeframe. In this case, risk is more related to the overdueness of an invoice. Based on this observation, we investigate three possible machine learning tasks for estimating this risk: binary classification for a predetermined overdue days cutoff; regression of the overdue days; and learning-to-rank which learns to optimize the risk-related ranking for the full range of instances. We model and evaluate these tasks using real-life spot factoring data. Finally, we perform a profit-driven evaluation that shows that regression models can lead to higher profits and better spread the risk than classification and ranking models for spot factoring.