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Credit Scorecard Model for a Top 10 Credit Card Company
AI-POWERED CREDIT SCORING
Leverage advanced AI/ML 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
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Leverage AI/ML technology to improve underwriting efficiency and accuracy.
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Enable model explainability to meet stringent regulatory and compliance standards.
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Streamline model implementation and maintenance for long-term scalability.
Challenges
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Convincing the client’s risk team to adopt AI/ML-based modeling methods.
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Overcoming high deployment standards for model integration.
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Addressing explainability challenges during the client’s MRM (Model Risk Management) review.
Solution
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Leveraged XGBoost and ML techniques to develop a predictive, fair, and high-performing credit scorecard model.
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Corrected sampling bias using advanced reject inference methods like Fuzzy Augmentation to refine risk predictions for rejected applicants.
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Ensured compliance and transparency with model explainability tools such as SHAP (Shapley Additive Explanations) and Partial Dependence Plots.
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Conducted comprehensive model evaluations, including Population Stability Analysis, Backtesting, and KS/Gini performance comparisons.
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Designed reason codes and verbiage for declined applicants to align with regulatory requirements.
Strategic Impact
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Secured total contracted revenue of $22 million for the project.
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Improved model KS (Kolmogorov-Smirnov statistic) by 8%, significantly enhancing predictive power.
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Gained client confidence in AI/ML solutions, integrating them into their long-term risk strategy.
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Delivered a scalable and maintainable model framework aligned with future regulatory changes.
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