This is like upgrading an insurer’s old spreadsheet-based risk calculator to a smart assistant that not only predicts which policies are risky more accurately, but also clearly explains which customer or policy features drove each prediction.
Traditional insurance risk and pricing models (e.g., GLMs) often trade off accuracy vs. explainability. This work uses the TabNet deep learning architecture on tabular insurance data to improve predictive accuracy (e.g., loss ratios, claim propensity, severity) while preserving human-readable feature attributions so actuaries and regulators can understand and validate the model.
Potential for proprietary actuarial datasets and applied know‑how (how to tune TabNet for specific insurance lines, rating plans, and regulatory constraints), combined with integration into existing pricing and policy administration workflows.
Classical-ML (Scikit/XGBoost)
Structured SQL
High (Custom Models/Infra)
Training complexity and hyperparameter tuning for deep models on large, high-cardinality tabular insurance datasets; plus serving latency and cost if deployed at scale across pricing or real-time quote flows.