Precise Underwriting at the Speed of AI

Traditional Pre-Pricing Rating Process
‍There are still insurance companies, before the final auto premium is produced, the customer’s rating factors such as age, claim history, vehicle details, and location must be reviewed through a manual and fragmented process. Information is often collected from multiple forms, supporting documents, and internal systems, then checked by staff using rule tables, paper-based workflows, or disconnected underwriting tools. This slows down pricing and increases the chance of inconsistency across cases.Because location-based risk is not always assessed in a structured and explainable way, underwriters and operations teams spend significant time validating whether the customer lives in a higher-risk area, whether theft or catastrophe exposure should affect pricing, and whether the available data is reliable enough to support the final premium. The result is slower turnaround, higher operational cost, inconsistent rating quality, and too much human effort spent on routine assessments instead of exceptions.
AI-Powered Rating Evaluation with Human Review
‍Bernoly redesigns this step as a seamless human–AI workflow before the final premium is generated. AI evaluates the customer’s situation in real time using key rating inputs such as age, claims history, and especially location intelligence. Based on the user’s address and surrounding risk context, the system classifies the area into a primary settlement class urban_high_traffic, urban_standard, suburban, or rural and adds risk flags such as high_theft_area and catastrophe_prone_area, together with a confidence score showing how reliable the assessment is.The AI also calculates and summarizes the relevant rating signals in a structured report, so straightforward cases can move forward quickly while exceptions are escalated to human staff with clear reasoning. Instead of replacing human judgment, Bernoly creates a collaborative model where AI handles repetitive evaluation and humans focus on edge cases, low-confidence results, or sensitive pricing decisions. This improves rating consistency, speeds up premium generation, reduces manual workload, and helps insurers price risk more accurately with lower operating cost.