Disclaimer: Independent educational project. Not affiliated with JPMorgan Chase. Built by Kaushal Khodifad. Data from public sources; figures are estimates.return to portfolio
Disclaimer: Independent educational project. Not affiliated with JPMorgan Chase.
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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.

0-99 · ~400 attrs · <1 msvendor-stated
Visa Advanced Authorization / Visa Protect score, trained on billions of VisaNet txns (269B+/yr)
3.2B · 98.83% · ~$33Bvendor-stated
Visa Decision Manager 2023: txns screened, auto-resolved, fraud prevented
>1B txns/dayreported
JPMorgan Chase across 100+ countries (reported, secondary)
−50% FP · +25% detection · ~$250Mreported
JPMC AI fraud models: false positives, detection, annual savings (reported, secondary)
The problem

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.

The data-product gap

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

  1. 01

    Inputs: auth message, card/account context, device/channel CNP signals, behavioral store, consortium intel, delayed labels.

  2. 02

    Features: velocity/aggregation + deviation + graph features in an online feature store (single-digit-ms reads, online/offline parity).

  3. 03

    Model: tree-ensemble core + sequence/transformer for behavior; two-stage (light filter on every txn, heavy ensemble on flagged).

  4. 04

    Serving: Kafka → Flink streaming; published as a data product with contract + SLA + row/column entitlements.

  5. 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.

Online feature storeTree ensemble + transformerTwo-stage scoringKafka → Flink servingSR 11-7 drift monitoring
Caveat to print

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

Adoption / reuse
  • Reuse ratio ≥4 domains (illustrative)
  • Time-to-first-score <1 sprint
Time-to-value / SLA
  • Latency p99 ≤50-100 ms (illustrative)
  • Availability 99.95% (illustrative)
  • Feature freshness ≤200 ms
Quality / governance
  • PR-AUC + KS tracked
  • PSI <0.10 stable, >0.25 retrain
  • Auto-resolution ~98% (Visa benchmark)
Business impact
  • FPR −50% / detection +25% (JPMC-reported)
  • $ net fraud loss prevented

Sources & methodology

Real · live Public-sourced · cited Synthetic · illustrative
  • 01
  • 02
  • 03
  • 04
Synthetic dataFictional partnerIllustrative metricsGrounded-inference topology

↑ Independent educational project by Kaushal Khodifad. JPMorgan Chase is a real company in the card data & analytics space; this design and the underlying prototype were built by Kaushal as a portfolio study. Not affiliated with, endorsed by, or representative of JPMorgan Chase's actual product. Data is from public sources. Figures are estimates.

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