Data product · Credit & risk · real-time
Fraud / Transaction-Risk Score
A governed, reusable real-time risk score returned inside the auth window - published as a data product with contract, SLA, entitlements, and SR 11-7 monitoring.
A real-time Transaction Risk Score must return inside the ~100-200 ms auth window. Class imbalance makes raw accuracy misleading, and chargeback labels land weeks late.
Without a governed, reusable score product, fraud / auth / disputes / collections each rebuild bespoke models - no contract, SLA, entitlement, audit, or drift monitoring.
The solution - on the platform
- 01
Inputs: auth message, card/account context, device/channel CNP signals, behavioral store, consortium intel, delayed labels.
- 02
Features: velocity/aggregation + deviation + graph features in an online feature store (single-digit-ms reads, online/offline parity).
- 03
Model: tree-ensemble core + sequence/transformer for behavior; two-stage (light filter on every txn, heavy ensemble on flagged).
- 04
Serving: Kafka → Flink streaming; published as a data product with contract + SLA + row/column entitlements.
- 05
Governance: every score audited; SR 11-7 monitoring (PSI, FPR, recall). Reuse across auth / step-up / disputes / collections / credit-line is the value multiplier.
All four JPMC figures are reported / secondary coverage, not a primary Chase disclosure. Vendor figures are vendor-stated. Latency / availability / reuse targets are illustrative.
Success scorecard
- ›Reuse ratio ≥4 domains (illustrative)
- ›Time-to-first-score <1 sprint
- ›Latency p99 ≤50-100 ms (illustrative)
- ›Availability 99.95% (illustrative)
- ›Feature freshness ≤200 ms
- ›PR-AUC + KS tracked
- ›PSI <0.10 stable, >0.25 retrain
- ›Auto-resolution ~98% (Visa benchmark)
- ›FPR −50% / detection +25% (JPMC-reported)
- ›$ net fraud loss prevented
Sources & methodology
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