Behavioural Scorecard for Bank Loan

Better Classification of Good & Bad Applications


    Behavioural scorecard for a leading US Bank to predict.

  • Delinquency likelihood of the accounts.
  • Assess expected losses on the delinquent accounts (DQ).
  • Rank the accounts by severity in order to prevent severe losses.


  • Variable screening using both statistics and business acumen. Iteration with different subset of variables is supported by Smart tool.
  • Determination of optimal probability cut off to obtain low error. In the validation sample, the model achieved very low error rate minimizing the cost arising from both type of errors – an attractive feature of ScoreBuilder, to make the decision.
    False positives: Non-DQ accounts misclassified as DQ – Collection costs - 5% False Negatives: DQ accounts misclassified as Non DQ – Loss incurred as no action was taken - 8%
  • High discriminatory scorecard as evident from the KS Statistic value of 0.63.
  • Benefited the bank to lower losses by xxx%.

Download Case study

    Share This Case Study