Success Stories

Operationalizing Risk Scoring Models for one of the largest Global Property & Casualty (P&C) Insurance Firm

About the client

One of the largest global Property & Casualty (P&C) insurance Firm

Challenges

  • Lack of unified source and interface for real time modeling of risk scores and premiums
  • Inability to handle increase in volumes as existing solution was unscalable
  • Delay in handling requests leading to poor customer experience

Approach

  • Devised a solution which was scalable & ensured business continuity with no disruption
  • Built Python based models capable of handling complex scoring rules
  • Ensured seamless data integration by APIfication of models on Azure platform

Solution

  • Reconfigured the current financial risk scoring models from legacy excel (VB) Macros to optimized Python script to allow real-time batch scoring
  • Built end-to-end containerized model deployment pipeline on MS Azure to enable scalability based on volume
  • Constructed API based framework to allow integration with any front-end solution
  • Enabled data integration with external sources like Dun and Bradstreet, Experian etc., to feed the model to calculate risk scores
  • Built analytical models with 99.7% effective pass rate

Impact Delivered

20%

Reduction in analytical model deployment time

7X

Faster analytics use case development

30 millisecond

Improvement in median latency for model execution

Tech Stack

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