Create Rating Models to assess borrower creditworthiness and calculate capital charges
Client : US Commercial Bank
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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
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