The interface is localized. Skill descriptions and documents are machine-translated by AI into your language; code, commands, paths and technical terms stay in the original, and the author's document governs.
Explore skills
Productivity · SKILL.md

fraud-detection

fraud-detection

Use to monitor, investigate, and prevent abuse within referral programs.

Independent GitHub snapshot · Automated classification · Original documentation governs

Skill documentation

GitHub

name: fraud-detection description: Use to monitor, investigate, and prevent abuse within referral programs.

Referral Fraud Detection Skill

When to Use

  • Designing safeguards for new referral initiatives.
  • Investigating suspicious referral spikes, duplicate accounts, or payout anomalies.
  • Reporting on program integrity for finance, legal, or compliance teams.

Framework

  1. Signal Collection – IP/device matching, velocity checks, blacklist databases, manual reviews.
  2. Scoring Model – assign risk scores by cohort (new accounts, high-volume referrers, geo mismatch).
  3. Workflow Automation – auto-flag, queue for review, or pause rewards until verified.
  4. Investigation Runbook – define evidence gathering, communication templates, and resolution paths.
  5. Feedback Loop – update heuristics, adjust incentives, and communicate policy changes.

Templates

  • Fraud monitoring dashboard outline (metrics, thresholds, owners).
  • Investigation log (case ID, referrer, signals, action taken, notes).
  • Policy update checklist (legal, comms, ops, partner notifications).

Tips

  • Combine automated checks with random manual audits for accuracy.
  • Align with legal/finance on clawback procedures before launch.
  • Share learnings with incentive-design to discourage risky behavior.

Saved skills