Success Stories

20% reduction in deployment time by operationalizing risk scoring models for Largest P&C Insurance company

Problems:

  • Rewrite the current financial risk scoring models from legacy SAS code to optimized Python script
  • Build the end-to-end model deployment pipeline on MS Azure to enable both real-time and batch scoring capabilities
  • Create Model APIs to allow integration with any front-end solution and build a model that can scale based on volume of request

Solution:

  • Optimized and parametrized python models to allow real-time batch scoring
  • 99.7% effective pass rate
  • API based integration with all data sources required to feed the model to calculate the risk scores
  • Containerized deployment of Model APIs, to provide the ability to scale on demand in real-time

Impact Delivered:

20%

Reduction in analytical model deployment time

7x

Faster analytics use case development

30ms

Improvement in median latency for model execution

Partners

Partnership with Adobe | Our Partners
Partnership with Argos Labs | Our Partners
Partnership with Arctic Wolf | Our Partners
Partnership with Baxter Planning | Our Partners
Partnership with Blitzz | Our Partners
Partnership with BlueLeap | Our Partners
Partnership with Conversight.ai | Our Partners
Partnership with Field Nation | Our Partners
Partnership with Microsoft | Our Partners
Partnership with Rank | Our Partners
Partnership with RedLambda | Our Partners
Partnership with Vertex | Our Partners

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