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.
