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Fraud Detection

Objective

Develop a default prediction scorecard solution to identify the predict the defaulters at the time application for loan.

Methodology

  • Profile data was merged with the transaction data to create a single snapshot of data.
  • In Data preparation-data audit reports were created having detail report on Missing value treatment, Outliers detection and treatment.
  • Detail trend analysis like correlation with target, Cross tab to see the relationship, binning to investigate inter attribute relationship was done.
  • Features were created using statistical methods like multivariate analysis, correlation analysis, binning using decision treeetc. Business knowledge was also used with help of discussion with client in features creation.

success-finance7

Impact

  • Now business has system to identify the risky customer at the time of loan application itself.
  • The default rate of the customer have gone down with more than double.
  • Business is able to risk management in a better way by optimizing the use of insurance.