- H2O Driverless AI can be used to build loss-cost and risk models from your own policy, claims, and behavioral data, with reason codes on every prediction.
- Individual signals, such as how, where, and when a vehicle is driven, can refine risk estimates beyond vehicle year, make, and model.
- Models work beside your actuaries and generalized linear models, not against them, and each one is documented for review.
- Granular risk segments help you price renewals so good risks stay and poor risks pay for the risk they bring.
Broad rating classes hide individual risk
Rates have long been set from a few simple factors. In auto, that meant the year, make, and model of the vehicle, and it said little about how, where, and when the car is actually driven.
You now collect far more data at the individual level than a rating table can use. Segments help, but even fine segments average away differences that matter, and good risks end up paying for poor ones.
Every rating change also has to pass your actuaries, model risk, and compliance. A model that cannot explain its factors will not get past that review.
How personalized rate management works
H2O Driverless AI builds explainable risk models that sit beside your actuarial models and feed the rating decisions your team owns.
Step 1: join the data
Combine policy, exposure, and claims history with individual signals you are permitted to use, such as driving behavior, location, and time of day.
Step 2: model the risk
H2O Driverless AI trains regression models for claim frequency and severity, and classification models for events such as a serious accident.
Step 3: find the drivers
Automatic feature engineering surfaces behaviors, such as hard braking or night driving, that move risk. Clustering groups policyholders with similar behavior into granular segments.
Step 4: explain every factor
Reason codes and Shapley values show how each factor moves a prediction, so actuaries can compare the model with their generalized linear models and decide what goes into the rate.
Step 5: document and deploy
Each model is documented as it trains and ships as a scoring pipeline for quoting and renewal.
Watching for drift
Driving patterns, repair costs, and weather shift over time. Watch for model drift and retrain before a rate review, not after.