Using ML Observability in Underwriting

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The growing applications of AI and machine learning underwriting models and their potentially significant effect on business have garnered all stakeholders' attention. The technology has made it possible for a straight-through application processing in Insurance and Financial underwriting.

However, the rapid adoption of AI/ ML in decision-making also has concerns - Data bais, adapting to newer policy changes, and performance deterioration. Trust and fairness of the models have also been long-standing concerns for both internal and external stakeholders. The models' opacity does not offer enough assurance for their application in the automation of loan or lending decisions.

These threshold concerns become crucial as financial services, and insurance firms rely on ML models to inform everything from pre-eligibility checks to credit decisioning and premium pricing.

Using the ML Observability framework can drastically improve the usability of ML models for Insurance and Financial underwriting and ensure optimal performance as well as compliance in heavily-regulated industries.

About the workshop

Understand why model monitoring is a must-have for any organization. Get insights on the pressing issues faced by stakeholders leveraging machine learning and how to resolve them.

  • Understand ML observability
  • How ML observability fits into MLOps workflows
  • Best practices of ML observability in underwriting and use-cases
  • Introduction to AryaXAI - ML Observability framework
  • Deliverables of ML Observability platform during underwriting

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