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Credit Scorecard Model for a Top 10 Credit Card Company

AI-POWERED CREDIT SCORING

Leverage advanced AI/ML (Machine Learning) models to enhance underwriting accuracy, reduce bias, and meet regulatory standards. Our intelligent scoring solutions improved model KS by 8% and secured $22M in revenue for a top credit card provider.

Business Objectives

  • Leverage AI/ML technology to improve underwriting efficiency and accuracy.
  • Enable model explainability to meet stringent regulatory and compliance standards.
  • Streamline model implementation and maintenance for long-term scalability.

Challenges

  • Convincing the client’s risk team to adopt AI/ML-based modeling methods.
  • Overcoming high deployment standards for model integration.
  • Addressing explainability challenges during the client’s MRM (Model Risk Management) review.

Solution

  • Leveraged XGBoost and ML techniques to develop a predictive, fair, and high-performing credit scorecard model.
  • Corrected sampling bias using advanced reject inference methods like Fuzzy Augmentation to refine risk predictions for rejected applicants.
  • Ensured compliance and transparency with model explainability tools such as SHAP (Shapley Additive Explanations) and Partial Dependence Plots.
  • Conducted comprehensive model evaluations, including Population Stability Analysis, Backtesting, and KS/Gini performance comparisons.
  • Designed reason codes and verbiage for declined applicants to align with regulatory requirements.

Strategic Impact

  • Secured total contracted revenue of $22 million for the project.
  •  Improved model KS (Kolmogorov-Smirnov statistic) by 8%, significantly enhancing predictive power.
  • Gained client confidence in AI/ML solutions, integrating them into their long-term risk strategy.
  • Delivered a scalable and maintainable model framework aligned with future regulatory changes.

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