Think of this as giving an insurance company a super-fast, tireless analyst that can read millions of past policies and claims, learn patterns of risk and fraud, and then help underwriters price policies better, settle claims faster, and spot suspicious behavior automatically.
Traditional insurance relies heavily on manual reviews, rigid rules, and slow, backward‑looking processes for underwriting, pricing, and claims handling. Machine learning promises to automate risk assessment and claims triage, improve pricing accuracy, detect fraud earlier, and generally cut cycle times and costs across the insurance value chain.
Proprietary historical claims and policy data combined with embedded ML models in core underwriting and claims workflows create a defensible advantage that improves as more data and outcomes are collected.
Classical-ML (Scikit/XGBoost)
Feature Store
High (Custom Models/Infra)
Data quality and label completeness for historical claims and underwriting decisions; regulatory constraints on model explainability and fairness.
Early Majority
The focus is on broad, portfolio-level transformation of underwriting, pricing, and claims operations using supervised ML models rather than just point solutions like chatbots or marketing optimization.