PD & Credit Setting Model

For a Large Bank


  • The client is one of the new age large listed bank in India.
  • The Client has various products in Assets and liabilities with vintage clients in different product segments.
  • Objective of this project was to build the probability of default model that enabled the client to find out credit worthy customers which can be offered the temporary lending facility.
  • The second objective was to Identify the credit limits for each customer.


  • G-Square analysed various factors like demographic variables, repayment history and product holding of the customers.
  • We identified the relationship of all the factors with respect to default expectancy and built a Probability of Default (PD) model using advanced machine learning algorithm.
  • The PD model gives the probability of default for each customer which help client to identify the customers which should not be given the lending facility with respective credit limits.


  • Customers with low probability of default were identified as Eligible customers for the lending facility.
  • The credit limits were also identified for disbursement.
  • Client is able to reduce the likely NPAs and knows the credit limit specific to each customer after the model was implemented.
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