Loan underwriting scores automated through ML APIs.

For a non-banking finance company

CLIENT & PROBLEM STATEMENT

  • Non-banking finance company offering SME loans needs ML-driven loan underwriting scores.
  • Requirement for real-time scoring via UI and APIs, with batch training of ML models.

APPROACH

  • Established secure Python-based ML APIs for efficient data transfer from client systems.
  • Developed ML Scorecards, a custom framework for creating and managing underwriting models.
  • Ingested historical loan data, SME financials, and market data to train robust logistic regression and tree-based models.
  • Implemented feature importance analysis to ensure model transparency and regulatory compliance.
  • Deployed models in a cloud environment for batch retraining, while exposing scoring endpoints for real-time API and UI integration.

SOLUTION & OUTPUT

  • ML Scorecards using APIs provide accurate, real-time loan underwriting scores via UI and APIs.
  • Faster, data-driven SME loan approvals with reduced risk, enhancing portfolio quality.
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