Client Background

Client is one the India’s biggest NBFC (Non-Banking Finance Company) providing consumer durable loans. These loans are unsecured interest free loans offered to a customer when he walks in to purchase any big ticket consumer durable goods. It’s imperative for client to have quick and reliable credit appraisal so as to not lose the customer and the same time control for fraud.

 

Business Objective

Presently the fraud stood at 3 million USD per annum.Identification of fraud happens after loan has been disbursed and customer takes delivery.There is absolutely no mechanism to assist the service representative at the store to flag off a particular case as potential fraud.Any framework/tool should be able to quantify the potential risk and hence move the case from ‘Instant mode’ to ‘Normal mode’ as the cost of outright reject is very high.

It was desired to develop a ML based Fraud detection framework for POS loan approvals so as To identify customers who are more likely to commit fraud/default on consumer durable loans and hence streamline loan approval process according to customer risk profiles.

 

Solution

Our team developed a ML based real time Fraud Detection engine integrated with Point of Sale. Engine classifies loan applicants into high, medium and low risk categories for potential fraud. Key features of fraud detection

  • Assigns fraud score for applicant at point of lending.
  • Higher fraud score applications routed through a stringent verification process.
  • Machine Learning algorithms monitored periodically for any new fraud patterns.

 

Outcome
  • Substantial decrease in loan disbursement to fraudulent cases at Point of Sale
  • Almost 10% of the originations are referred to ‘Normal process’ in which the fraud incidence is as high as 5% which translates into a gross saving of almost 1.5 million USD i.e. 50% of the VaR
  • Substantial decrease in the third party cost of loan amount recovery from the fraudulent cases.