Fraud Detection with AI — a lender's playbook

Most fraud content is either vendor marketing disguised as education or an academic taxonomy that can't help you at 9:30am on a Monday when a ring is pulling money out the back of your platform. This playbook is neither. It's the compact, opinionated, field-tested version of what we'd hand a new fraud lead on day one.

THE FOUR PATTERNS COVERED

1. Synthetic identity — fake humans built from real pieces.

2. Document manipulation — surgical edits that fool a tired underwriter.

3. Ring fraud — coordinated applications, easy to see in aggregate, invisible one-by-one.

4. Re-verification and payout-stage scams — the workflow ones.

WHAT'S INSIDE

• 12 fraud-detection prompts (F1–F12), each with a caught-fraud example and a false-positive example.

• Three decision trees: onboarding-stage, payout-stage, post-disbursal.

• "What AI still misses" chapter — the hardest and most honest one.

• A ready-to-use red-flag checklist (PDF + Markdown).

• An illustrative case-study teardown: a composite synthetic-ID ring, built from observed patterns across multiple small-to-mid-sized lenders, with the six signals that would have caught it.

WHO IT'S FOR

Risk/fraud teams at small-to-mid-sized lenders, fintech pilots, or founders worried fraud will sink their first cohort. Not for banks with mature in-house fraud ops.

MULTI-JURISDICTION FRAMING

Regulatory notes include US (FDCPA), UK (FCA), EU (consumer credit directive), and India (RBI). 80%+ of the content applies wherever you operate.

Single-company licence up to 25 seats. 14-day refund.