Behavioral Scorecard Model to help a Leading Bank Boost its Collections Efficiency
Overview
Since the credit risk in banking services is getting more complex, a leading financial institution sought to enhance its early-collections strategy for its Business Interest Loan (BIL) portfolio. While most customers made timely EMI payments, a subset began missing payments early, driving up bounce rates and collection costs.
For an efficient delinquency management system, we developed a Behavioral Scorecard Model using logistic regression. The model identified customers who are current on payments (Bucket 0; DPD = 0 as of the 24th) but are likely to default in the next EMI cycle (by the 5th of the following month). This enabled the collections team to act proactively, reducing delinquencies before they happen.
Solution
TransOrg’s banking analytics solutions team built a behavioral scorecard (probability of default driven output model) using internal and external data sources to assess each borrower’s repayment risk.
The model assigned every customer a probability of default (PD) and grouped them into three actionable priority buckets:
- High Priority – immediate follow-up needed
- Medium Priority – monitor and engage if resources allow
- Low Priority – no action required
Each month, these insights were automatically updated and integrated into the client’s collection workflow, ensuring faster decision-making and targeted communication before the EMI due date.
The Impact
The loan portfolio risk management model successfully captured 80% of potential bad accounts within the top two deciles, allowing early, targeted interventions.
Post-implementation, the bad rate dropped from 2.5%–3.5% to 2%–3%, improving portfolio quality by roughly 0.5%.
Consistent month-on-month improvements were recorded in EMI bounce rates, improving payment regularity.
This behavioral scorecard model can be retrained every two years with new data, ensuring continued accuracy and adaptability.
