As a leading regional insurance company with over 100 years of experience, our client wanted to improve how the underwriters and agents across policy systems evaluate the risk score for different policies. With that core objective, they reached out to Exavalu to build a Scoring engine that can improve the risk assessment process for agents & underwriters for their organization.
AWS Lambda Integration
As part of the new business strategy to improve the underwriting score generation process, our client was looking for assistance in optimizing the whole score generation and claims process to be faster, more accurate, and more efficient. Additionally, they wanted to automate the score generation based on factors such as type of worker and the state-wise jurisdiction. Lastly, our client also wanted the new scoring engine to reduce operational costs and increase overall business efficiency.
We began with a thorough understanding of our client's requirements, delivering a new scoring engine that provides accurate & quick responses. The scoring engine accelerated the risk assessment process; taking only 2-3 seconds to generate scores and get high-level confirmation, and only 6-8 seconds to generate scores for worker’s compensation with different factors & jurisdictions. We also utilized a design-first approach that involves modern design principles (API-led architecture) to create the integration layer. This helped to integrate the Policy Admin System (Guidewire) with the Scoring Engine deployed in AWS. We implemented Dynamo DB for the Audit Data Store and Experian Data Store as cache. Additionally, we also incorporated DLB for Cloudhub & integrated on-prem DB with Anypoint VPC.
Used Experian, Scoring engine, Slotting piece to determine the scores along with predominant class, class-code, renewal capping, rank frequency & pricing metrics. To design this business over enterprise Integration, we have used REST, Pub-Sub, Reliability & Circuit breaker architecture pattern.
Incorporated API governance for re-usable asset &security compliance. Along with that implemented functional monitoring with API health check and enabled notifications for failover.
Generated informative dashboard to get metrics on different business parameters with relation to jurisdiction, type of workers, etc. providing insight on business and cost model.
Implemented analytical solutions of Mule for real-time failover tracking, reducing resolution time by 40-45% and improving customer satisfaction.
A flexible model has been implemented to assess risk from existing data, and determine score, pricing-model, predominant-class for any new and renewal business process for Underwriter across jurisdictions
Established a persistent, secured, and reusable caching mechanism, helping customers reduce their transaction costs by 40-45% at the enterprise level.
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