Auto insurers face challenges in accurately assessing risk at the policy level, leading to unprofitable segments and lost revenue. Traditional underwriting methods often rely on broad segmentation, causing insurers to either exclude entire groups of potential customers or accept inadequately rated risks. This results in a trade-off between maintaining premium volume and achieving a desirable loss ratio.

Soteris offers a machine-learning-powered SaaS platform that enables auto insurers to improve underwriting precision and profitability. The platform analyzes policy-level data to predict risk in under one second, allowing insurers to accurately select and quote only adequately rated risks within any segment. By implementing Soteris, insurers can maintain all segments of their portfolio, reduce loss ratios, and identify opportunities to capture market share through optimized rate adjustments. The solution integrates with existing systems and provides data-driven insights for ratemaking and risk selection.

Policy-level risk scoring in under one second, enabling real-time underwriting decisions

Machine learning models trained on insurer's own loss experience for accurate predictions

Credit Estimator model that predicts credit score tiers without requiring a credit report pull

Ability to turn on all segments of the insurance book while accurately pricing individual policies

Identification of "sweet spots" for rate adjustments to capture market share

Integration with existing underwriting systems for seamless implementation

The primary target audience includes auto insurance carriers seeking to improve underwriting profitability, reduce loss ratios, and optimize risk selection processes.