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fraud-detection

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Original

English
🇨🇳

Translation

Chinese

Referral Fraud Detection Skill

推荐计划欺诈检测Skill

When to Use

适用场景

  • 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.
  • 为新的推荐项目设计防护措施。
  • 调查可疑的推荐量激增、重复账户或支付异常情况。
  • 为财务、法务或合规团队提供项目合规性报告。

Framework

实施框架

  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.
  1. 信号收集 – IP/设备匹配、频率校验、黑名单数据库、人工审核。
  2. 评分模型 – 按群组（新账户、高量推荐者、地域不匹配）分配风险评分。
  3. 工作流自动化 – 自动标记、排入审核队列，或暂停奖励直至验证完成。
  4. 调查手册 – 定义证据收集、沟通模板和解决路径。
  5. 反馈循环 – 更新启发式规则、调整激励措施，并传达政策变更。

Templates

模板

  • Fraud monitoring dashboard outline (metrics, thresholds, owners).
  • Investigation log (case ID, referrer, signals, action taken, notes).
  • Policy update checklist (legal, comms, ops, partner notifications).
  • 欺诈监控仪表板大纲（指标、阈值、负责人）。
  • 调查日志（案例ID、推荐人、信号、采取的行动、备注）。
  • 政策更新检查清单（法务、沟通、运营、合作伙伴通知）。

Tips

提示

  • 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.
  • 将自动检查与随机人工审核相结合以确保准确性。
  • 在启动前与法务/财务部门就奖励追回流程达成一致。
  • 与
    incentive-design
    分享经验，以遏制风险行为。