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Case study

Improved loan decision making

Assisting lenders to make data-driven decisions for loans nearing default.



  • Lenders have different options to manage loans nearing default, such as restructuring, forbearance, refinancing, and more.

  • Traditional machine learning methods often fall short of predicting the best loss mitigating strategy, producing inaccurate forecasts due to confounders.

  • Our goal was to develop a model that, based on available data of the borrower, recommends the best action to the lender.


  • Assessed and cleaned the available historical loan data.

  • Enriched the data with additional macroeconomic factors.

  • Used expert knowledge and data-driven approaches to identify the causal predictors for the loan loss.

  • Trained a Causal AI model on historical data and the causal predictors to accurately predict the outcomes of the lenders action on the loan loss.

  • Validated the trained model on hold-out data and on new data accumulated by the client during the project.

  • Based on the trained Causal AI model, available options to the lender, and data about the borrower, the loan officer is recommended the action predicted to incur the minimal loss.

  • Developed a causal model that more accurately forecasts the impact of decisions on loan loss than traditional machine learning techniques.

  • Delivered POC system that recommends best actions to the loan officer.

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