Key Takeaways
- Combine rules with machine learning for best results.
- Device, behaviour and network signals reveal fraud patterns.
- Human review handles edge cases and improves models.
Fraud patterns change quickly. Static rules alone cannot keep up.
Signals used
- Device fingerprint and location
- Behaviour: typing, navigation speed
- Transaction patterns and velocity
- Network links between accounts
- Identity and document checks
Approach
| Layer | Role |
|---|---|
| Rules | Known patterns, compliance |
| ML scoring | Detect new patterns |
| Case management | Analyst review |
| Feedback loop | Improve models |
See fintech security.
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Frequently Asked Questions
How does AI detect fraud?
By scoring transactions and users based on patterns learned from historical fraud and normal behaviour.
Will AI block genuine customers?
Well-tuned systems reduce false positives; human review handles borderline cases.
What is a mule account?
An account used to move fraudulent funds, often detectable through network patterns.
Do small fintechs need AI fraud tools?
Even basic risk scoring and velocity rules help; AI adds value as volume grows.
