Early-warning credit risk: seeing consumer stress 60 days sooner
A predictive ML program that blends bureau and behavioral signals to flag at-risk accounts before they roll — and routes the right treatment to each one.
The problem
A consumer credit portfolio was managing risk in the rear-view mirror. By the time an account hit the collections queue, the miss had already happened; treatment was generic; and the data that could have predicted it — payment timing drift, utilization creep, deposit pattern changes — sat unused across three systems.
The hypothesis
Delinquency is rarely a surprise to the data. Our hypothesis: a model trained on behavioral signals layered over bureau data could flag accounts 30–90 days before a first miss, early enough for a soft-touch intervention to change the outcome.
The build
- Feature foundation — a governed pipeline unifying bureau pulls, transaction behavior, and servicing history into a single account-level feature store, refreshed daily.
- Early-warning models — gradient-boosted models scoring roll-risk per account, with explainability surfaced per score so analysts see why an account flagged.
- Treatment routing — scores mapped to actions: proactive limit reviews, payment-plan offers, hardship outreach — each with expected-value logic, so the cheapest effective treatment goes first.
Design choice that mattered: every score ships with reason codes. A risk score nobody can explain is a risk score nobody will act on — explainability was a product requirement, not a compliance afterthought.
Rollout
We back-tested against two years of portfolio history, then ran a champion/challenger split: half the flagged population received model-routed treatments, half received business-as-usual. The lift showed up in roll rates within two cycles, which is what unlocked full deployment.
Results
Risk visibility moved roughly 60 days earlier in the account lifecycle. Roll rates into later delinquency buckets fell measurably in the treated population, and collections effort concentrated where it changes outcomes instead of where the queue happened to sort it.
What we'd tell you
- The feature store is the asset. Models get retrained; the unified behavioral view compounds.
- Champion/challenger beats opinions. Run the split, let the roll rates argue.
- Pair every score with a treatment. A prediction without a next action is trivia.
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