Commercial auto insurance brokers operate in a complex environment where risk varies significantly across drivers, routes, and operating behavior. Traditional pricing models often struggle to capture these nuances, resulting in inconsistent decisions and missed profitability targets. Risk-based pricing powered by telematics and predictive analytics helps brokers translate real-world driving behavior and operational data into sharper pricing accuracy and more informed underwriting decisions.
Problem Statement
A leading auto insurance broker faced inconsistent pricing decisions across commercial auto accounts due to fragmented use of telematics, FMCSA, and claims data. Underwriters lacked a unified, actionable view of risk, and existing models did not translate data into clear pricing guidance. The organization needed a scalable, risk-based pricing approach to improve accuracy, consistency, and underwriting efficiency.
Impact
- Improved loss ratio by ~10-15 percentage points through usage-based risk-scoring and driver behavior data
- Reduced underwriting & rate-development lead time by ~40% via pre-built scoring models and telematics-enabled decisioning
- Increased retention of safer fleets by ~15 percentage points by offering value-added telematics/operations services tied to the scoring model
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