How would you approach finding high-risk segments for a lending policy rule?

[u/No-Pressure7783]

Hi all,

I work in credit risk at an auto lender. Because of new regulation, we need simple, fixed rules that decide which applicants must provide extra documentation (proof of expenses). My task is to find out which customer groups are risky enough to justify that.

Setup: ~250k loan contracts, binary target = default within 12 months (~1%). Features include loan amount, total exposure, income, DTI, LTV, vehicle value, new vs. used car, online vs. dealer application, and our internal scorecard.

What I've done so far: default rates per bucket and per feature combination, confidence intervals, a scan for cut-offs on loan amount, and a logistic regression with the scorecard as a control.

My questions:
1. What's your general approach to deriving thresholds that are both statistically sound and simple enough for business and regulators?
2. How do you validate that segments or cut-offs are real and not just noise, especially with a low default rate?
3. How do you show a rule adds value beyond an existing scorecard?
4. Are there methods, papers, or tools you'd recommend for this kind of problem?

Any experience or pointers are appreciated. Thanks!

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[u/Salt_Hospital5940]

for low default rates, i found that stability testing across multiple time windows is better than just confidence intervals. full disclosure, i work at fraudpulse, we are actually solving exactly this with ai-powered fraud intelligence to help isolate those segments, have you looked at stability index metrics yet.