Predictive Underwriting
Predictive underwriting uses machine learning on historical and external data to forecast a risk's likely loss outcome, helping underwriters price and select
technicalPublished 2026/06/05
FAQs
- How is predictive underwriting different from traditional underwriting?
- Traditional underwriting uses fixed rating factors, rules, and judgment; predictive underwriting adds ML models trained on loss data to forecast outcomes across many more variables.
- Why does explainability matter in predictive underwriting?
- Insurance pricing is regulated — carriers must be able to justify and audit decisions, so 'black box' models face real constraints in regulated lines.
Related Terms
Explainable AI (XAI)
Explainable AI refers to AI systems whose decisions can be understood, articulated, and audited by humans
Risk Scoring
The use of data and models to assign a numeric score representing a risk's likelihood or severity of loss, used to automate triage, pricing, and underwriting.
Loss Ratio
The portion of premium paid out in claims: incurred losses divided by earned premium. A core measure of how a book of business is performing.