pinpoint

Pinpoint Predictive (Pinpoint.ai) provides artificial-intelligence and predictive analytics products for property and casualty insurance carriers. Using only a name and address as input, the platform returns signals intended to support decisions across marketing, underwriting, ratemaking, and claims. Product lines include marketing intelligence models that predict profitability, response, and conversion of prospective leads; underwriting models presented as compliant and customizable; ratemaking analytics covering filed and non-admitted pricing and IRPM selection; and claims models that flag attorney involvement and litigation, fraud, large losses, and cause of loss.

A "Discovery" offering lets carriers build custom binary-classifier models for any two-outcome event, such as churn, premium leakage, or program adoption, and test, validate, and deploy them across underwriting, claims, and retention workflows without dedicated data science resources. The company also publishes self-service tools for prospects, including an ROI calculator for projecting loss ratio improvement and a workbook validation template for preparing loss outcomes, adding predictions, and reviewing lift charts and financial impact. Pinpoint states a mission of preparing the insurance industry for a more equitable, profitable, and efficient future.

Business model

Pinpoint sells predictive scores and models to insurance carriers as a platform, with implementations spanning marketing, underwriting, ratemaking, claims, and custom discovery models; prospects are engaged through discovery calls, an ROI calculator, and validation workbooks [0][1][3].

Traction

Published case studies describe deployments with national auto and home carriers. Reported outcomes include a 4-point loss ratio reduction on a personal auto book of 714,577 policies used for marketing spend optimization; a 3-point reduction using claims severity predictions on the same book; identification of an additional 6% of third-party claimants likely to seek attorney representation from a model trained on 130,000 claimants; a 3-point reduction for a home carrier with roughly 300,000 policies using claims frequency predictions; a 4-point reduction for a home carrier with roughly 150,000 policies targeting non-weather water losses; a 4.6-point reduction for an auto carrier with 80,000 policies using loss cost predictions; and a 7-point reduction for a home carrier with 200,000 policies in a book-roll acquisition strategy. Carrier executives quoted on the site include representatives of Ohio Mutual [0][1][3].