Objective

To develop a rating model that allows a US commercial bank with a large corporate portfolio to assess the creditworthiness of borrowers/applicants and calculate credit risk capital charges.
 

CRISIL's Solution

• Develop a borrower model based on a hybrid approach employing logistic regression (LR) that directly predicts probability of default (PD) based on borrower characteristics
  
• Borrower data screened and variables shortlisted using suitable approaches such as binning, weight of evidence and information value
  
• Using stepwise logistic regression, established an econometric relationship between PD and risk attributes
  
• Using expert judgement-based model overlay, incorporated qualitative information and early warning signals on existing borrowers to calculate adjusted PD for each borrower, enhancing model effectiveness
  
• Tested model performance and stability on different test datasets using classification table and ROC curve
  
  

Client Impact

• Client used borrower rating model to strengthen its credit approval and pricing mechanism by enhancing its assessments of borrower creditworthiness
  
• The model helped lower losses from default by a significant extent
  

Questions

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