Credit Limit Decrease Decisioning Model for Risk Mitigation
Overview
A leading bank implemented a Credit Limit Decrease (CLD) model to proactively reduce exposure to high-risk customers. The model leveraged data from primary, secondary, and trigger sources to minimize portfolio risk while maintaining profitability.
Solution
- Risk-Based Segmentation: Identified customers with a high likelihood of delinquency using strategic and hygiene conditions, including utilization rate, repayment ratio, and credit history.
- Data-Driven Decisioning: Applied a structured waterfall approach with predefined criteria to filter the base, ensuring only high-risk customers were targeted for a credit limit decrease.
- Policy-Driven Implementation: Customers meeting the risk thresholds had their credit limits reduced as per the bank’s policy, leading to better risk management.
Impacts
Reduced Portfolio Risk – Delinquency rates lowered by 2% (# rate) and 5% ($ rate), improving overall portfolio health.
Optimized Customer Base – Increased attrition among high-risk customers, ensuring better long-term profitability and sustainability.
