Think of this as comparing two types of "risk calculators" for insurance: the old, simple one is like a basic spreadsheet formula; the new explainable-AI one is more like a smart assistant that can capture complex patterns but also tells you, in plain terms, why it thinks a customer is high or low risk.
Insurers need models that are both accurate and explainable to underwriters, regulators, and customers. This work evaluates whether modern explainable AI methods can outperform traditional linear models while still providing transparent reasoning for pricing, underwriting, and risk scoring.
Proprietary insurance data and well-governed modeling processes (including explainability frameworks and documentation) create a defensible edge; the algorithms themselves are largely commodity, but how they are tuned, validated, and embedded into underwriting workflows can be sticky.
Classical-ML (Scikit/XGBoost)
Structured SQL
Medium (Integration logic)
Model governance and explainability-at-scale (generating and storing explanations for large policy/customer volumes) rather than raw compute limits.
Early Majority
Positions explainable AI as a plausible successor or complement to traditional generalized linear models in insurance, aiming to preserve interpretability while achieving non-linear performance gains. The comparative, evidence-based framing is more rigorous than typical vendor marketing and is aligned with actuarial and regulatory standards.