Case Study | P&C Carrier Improves Risk Prediction Model By 10%+

About the Case Study

ForMotiv engaged with the Data Science and Modeling team at a Property and Casualty Insurer that sells primarily through the directto-consumer channel with eCommerce representing 85% of their transaction volume. Like most insurers, the client was looking to add additional datasets to their fraud, pricing, and underwriting models. The carrier has done studies where they concluded that 17% of their policies are misrepresented.

Using ForMotiv, the client was looking to accomplish 3 goals:

  1. Identify and reduce misrepresentation by customers during the application process
  2. Reduce Premium Leakage by applicants
  3. Improve Loss Ratio by uncovering bad or unknown risks

Trusted Across the Insurance Industry

lincoln financial formotiv customers
new york life formotiv customers

Trusted Across the Insurance Industry

Why Use ForMotiv Data?

Simple Integration

Easy Javascript integration deployed through your Tag Manager

Glass-Box Approach

5,000+ behavioral data points captured in each application. Accessible in real-time or batch file.

1st Party Behavioral Data

Granular, curated 1st-party data easily combined with your existing data sets

Intuitive Data Features

Capture dozens of intuitive behaviors like Hesitation, Error Rate Collections, Cognitive Loads, and more

Totally Safe & Secure

Zero PII Captured. GDPR & CCPA Compliant