Framework
Automated Cheater Detection
TL;DR
Framework for building automated fraud detection systems that can scale beyond human memory capacity, derived from Alibaba's $160M annual anti-counterfeit investment.
Framework for building automated fraud detection systems that can scale beyond human memory capacity, derived from Alibaba's $160M annual anti-counterfeit investment.
When to Use Automated Cheater Detection
When transaction volume exceeds human review capacity, when cheaters defeat manual detection, or when scaling a marketplace beyond thousands of users.
How to Apply
1
Identify Signals Cheaters Can't Easily Fake
Find detection vectors that require bearing the cost of honest behavior to replicate
Questions to Ask
- What signals require actual honest behavior to produce?
- Can cheaters replicate signal without bearing cost?
Outputs
- List of unfakeable signals: behavioral patterns, network relationships, micro-details
2
Build Adaptive Systems
Use machine learning that updates as cheating tactics evolve
Questions to Ask
- Do detection rules adapt when cheaters change tactics?
- Is there continuous model retraining?
Outputs
- Adaptive detection system architecture
3
Balance False Positives vs. False Negatives
Calibrate sensitivity to context-appropriate levels
Questions to Ask
- What's the cost of flagging legitimate users?
- What's the cost of missing cheaters?
Outputs
- Target: 95% detection, <1% false positive (Alibaba benchmark)
4
Invest Until Marginal Cost = Marginal Benefit
Perfect detection isn't the goal - optimal detection investment is
Questions to Ask
- Does additional detection investment create more value than it costs?
- What's the diminishing returns curve?
Outputs
- Optimal detection investment level