Ongoing Analytics

For a NBFC

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  • The client is one of the NBFC in the country in the SME space.
  • Focus on SME loans and Venture Debt.
  • Objective of the project was to build a risk score card model which helped the client to find out the customers that can be offered a lending facility.
  • The second objective was to do back testing of their existing model.
  • G-Square analysed the different factors like demographics, banking behaviour and repayment history of customers and identified most important factors.
  • We checked and identified the relation of all the factors with respect to the Probability Of Default using various statistical tools and advanced Machine learning algorithms.
  • The credit risk score model gives score basis four parameters i.e. Financial, Business, Banking and Management for each customer.
  • Monitoring and an ongoing quarterly review is done to test the score card on future data and perform model tuning as the response.
  • We again checked relationship of the features as the data size increased and changed some factors as the input and further tuned our model to get a higher sensitivity which reduced the good loan rejection % by 2%.
  • G-Square’s productified solution is now implemented as an API which generates the risk score and track the ‘PD’ of the customers.
  • Customers having low risk score were identified as good customers.
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