Best Practices for Responsible Machine Learning in Credit Scoring

TL;DR

This paper reviews best practices for responsible machine learning in credit scoring, emphasizing fairness, reject inference, and explainability to promote ethical and transparent lending.

Contribution

It provides a comprehensive overview of methods and metrics for mitigating bias, improving transparency, and addressing data limitations in credit scoring models.

Findings

Guidelines for fairness and bias mitigation in credit models

Techniques for reject inference to handle limited data

Methods to enhance model transparency and explainability

Abstract

The widespread use of machine learning in credit scoring has brought significant advancements in risk assessment and decision-making. However, it has also raised concerns about potential biases, discrimination, and lack of transparency in these automated systems. This tutorial paper performed a non-systematic literature review to guide best practices for developing responsible machine learning models in credit scoring, focusing on fairness, reject inference, and explainability. We discuss definitions, metrics, and techniques for mitigating biases and ensuring equitable outcomes across different groups. Additionally, we address the issue of limited data representativeness by exploring reject inference methods that incorporate information from rejected loan applications. Finally, we emphasize the importance of transparency and explainability in credit models, discussing techniques that provide insights into the decision-making process and enable individuals to understand and potentially improve their creditworthiness. By adopting these best practices, financial institutions can harness the power of machine learning while upholding ethical and responsible lending practices.