Case Study 46: Robo Underwriting Model

For a large bank

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  • A Large bank in the Private sector with a large customer base.
  • A relatively new credit card business but very fast growing.
  • The Client wanted to build a Credit scoring model for Credit cards for card granting decisioning on the go.
  • It required automate the process of credit card underwriting by using applicants PII data & underwriters’ comments data of past applications.
  • G-Square analyzed the demographics, various scores of applicants & textual comments & identified most important factors which will help in the Rejection/Approval of applications.
  • We looked into various classification ML models plus text mining models to arrive at the final model for approval/reject process.
  • Using our text analytics libraries, G-Square developed a robust Credit Underwriting Analytics Model using Machine Learning.
  • Final outcome were APIs delivered for the decision of Approved/Reject/Need more info for the on the spot decision.
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