Explainable Machine Learning Models for Credit Risk Analysis: A Survey

Data analytics in financial applications has evolved as a result of technological improvements, increased data availability, and the necessity for financial institutions to make better-informed decisions in an increasingly complicated and competitive industry. Machine learning models have recently advanced and been integrated into data analytics, allowing researchers and practitioners to make sense of large and complex data and extract relevant insights and patterns. Financial institutions can use machine learning models to identify potential borrowers who may fail to meet financial obligations and to take appropriate action to avoid such situations. Such predictive analysis is termed credit risk analysis and is extensively used by lending organizations. In this case, financial and other information about potential borrowers is gathered from various sources and supplied to machine learning models, which may estimate a lender’s eligibility and categorize them accordingly. Although these models predict problematic or safe borrowers with high accuracy, they provide no rationale or explanation for their judgments. As a result, these models are commonly misinterpreted, and the process is typically implemented as a black-box model, in which the model accepts data as input and produces some output without explaining the decision-making process. Nonetheless, because regulated financial services must utilize a transparent decision-making process, machine learning models’ explainability becomes crucial for acceptable adoption. Recently, researchers addressed this issue by offering explainability techniques that can explain the decision-making process of machine learning models, which will encourage financial institutions to employ machine learning models for data analytics. This chapter investigates recent advances in credit risk analysis using explainable machine learning models and offers insights for future research in this area.