Explainable AI for Claims Reserving: Bridging Actuarial Practice and Machine Learning

This session brings together actuarial and machine learning perspectives to explore how AI can support reserving practices.The actuarial perspective will cover the practical limitations of current reserving workflows, where judgment enters traditional methods and why consistency is difficult, and what actuaries need from AI, including transparency, validation, and governance. It will also include a case study comparing traditional results with AI-supported modeling and how ML results can serve as a second-opinion framework.The machine learning perspective will cover the Bayesian ML framework, including its architecture, model selection based on predictive power and cross-validation, and how explainability is built into the model. It will also discuss why full distributions matter more than point estimates and share technical lessons from applying ML to insurance triangles.