Objective

To Create a Highly Scalable and User-Friendly Credit Decisioning Model for a US Retail Bank’s Credit Card Portfolio

CRISIL's Solution

• Data structuring and converting multi-class labels in target variable to binary completed in a single click
  
• Pre-processing used mean, median, and mode, along with imputing with KNN algorithm
  
• Exhaustive EDA carried out quickly by employing easy-to-use interface
  
• Data imbalance issues addressed with advanced SMOTE analysis and undersampling/oversampling techniques
  
• Feature selection and performance measured with algorithms (such as genetic algorithms), principal component analysis, weight of evidence, etc.
  
• Advanced machine learning and deep learning algorithms used for comprehensive model building exercise
  
• Deep learning, coupled with multiple optimization techniques, used to improve model performance
  
• All advanced ML techniques integrated with customized model tuning parameter
• Advanced feature selection, along with automated fine and course classing
• Key concerns such as model interpretability of machine learning algorithms addressed with the help of algorithms such as LIME, ICE, and SHAP
• Customizable scorecard generated
• Exhaustive performance analysis performed using tests such as ROC, KS, GINI, Precision, and Recall
• GridSeachCV gave the best performing algorithm
  

Client Impact

• New model enhanced performance efficiency, reducing analysis cycle time and minimizing error rate
• Results accessible to client through customizable scorecard
  

Questions

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