Underwriters play a significant role in the insurance ecosystem to manage risk and simultaneously ensure the quick issuance and administration of insurance policies. However, with a large volume of policies to manage at varying risk levels (and their time a valuable but limited commodity) underwriters currently spend the same amount of time across policies regardless of the risk level. Policy submission triaging is currently inefficient, time-consuming, unreliable, and inflexible to changing corporate priorities – whether a heavy focus on risk or insurance carrier initiatives to prioritize new products, territories, etc. A poor submission triage process can incur significant operational costs without returns and leave the firm open to risk.
Synechron developed the Cognitive Machine Learning Accelerator for Intelligent Underwriter Scoring to better understand the risk profile of recently submitted profiles and automatically enable underwriters to prioritize opportunities that will maximize the top line, minimize risk, and maximize human value. The application uses historical policy scores combined with real-time policy data and predictive modeling to rank and prioritize policies requiring underwriting. This includes Bind Conversion Probability, Estimated Profitability, Long Term Potential, Estimated Premium Size, Producer/Agency Relationship, and Opportunity to Cross-sell, Current Corporate Initiatives, and Underwriter Work Required. Based on these criteria, an application is assigned a single score and prioritized based on the work-to-reward ratio.
Key features and benefits of the Cognitive Machine Learning Accelerator for Intelligent Underwriter Scoring are:
Know how Synechron’s InsurTech Accelerators help in Intelligent Submission Scoring
To learn more about our Synechron’s InsurTech Accelerators built on Cognitive Machine Learning and the work we’re doing email us at firstname.lastname@example.org
How we are innovating