Abstract Machine Learning (ML) can reveal the complex nonlinear relationships between applicant attributes, offering new opportunities to strengthen underwriting risk classification in insurance. However, concerns about algorithmic opacity and subgroup bias have hindered the use of ML in underwriting. Existing studies have mostly concentrated on claims prediction and pricing, with limited attention to classification, interpretability, and fairness.