Credit Card Behaviour Scorecard Model

for a Large Bank

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  • The Client is a leading Universal bank in Asia
  • It has strong customer base in retail, corporate and NR space
  • The Client wanted to create a behaviour scorecard model in order to predict whether the existing customer who is holding credit card is going to default.
  • Internal behaviour data i.e. payment, transaction, delinquency, transaction, month on books etc. of customers was used to develop the model
  • Roll rate analysis was done in order to get the good and bad definition. Vintage analysis was done in order to get the performance widow
  • Two out of time samples were taken to check the performance of the model
  • Binning of variable was done to get the weight of evidence(WOE) and information value(IV).
  • Finally, logistic regression was fitted to predict the probability of default.
  • Model performance was checked using KS , AUROC , GINI , PSI , Accuracy , F1 score , Precision , Recall.
  • Three scorecards were created for three types of cards.
  • The segmentation of the cards was done on the basis of days past due (DPD)
  • Points to double the odds methodology was used to arrive at the scores.