Case Study | Predicting home insurance claims risk
| Sector | Home insurance |
|---|---|
| Products | cipher:risk |
| Application | Risk/pricing |
Predicting future claims behaviour at point of quotation is essential insight for the development of an intelligent pricing strategy that protects both profits and customer loyalty. This case study, for a well known general insurance underwriter, shows how Fraudscreen was able to accurately predict which policy holders were most or least likely to make a claim, plus the potential value of that claim. From this, we were able to clearly characterise segments of policy holders who were unprofitable for the client, as well as those that offered the greatest profit. These insights were used to enhance pricing models in order to reduce the losses from the worst segments and encourage renewal from the best.
