compliance_monitoring · finance · workflow
Feedzai ScamAlert: Multi-Label Red Flag Detection with LLM Benchmarking for Scam Prevention
Traditional binary scam detection systems output only a scam likelihood score without explaining why something is risky or providing contextual guidance, leaving users without interpretable insight and prone to distrust when context is missing.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · User submits screenshot
Users submit a screenshot of the suspected scam to ScamAlert.
Tools used
ScamAlertLLMGPT-5Claude 4Claude 3.7Gemini 3 ProGemini 2.5 ProGemini 2.5 FlashClaude 3 Haiku
Outcome
ScamAlert provides interpretable red flag detection rather than a binary score, giving users transparency about why something appears suspicious and allowing domain experts to update flag definitions as fraud tactics evolve.
What failed first
The binary classification approach collapses nuanced scam signals into a single score, fails to account for contextual legitimacy signals the user may already hold, and provides no interpretable explanation, undermining user trust.
Results
Cost replacedsignificantly cheaper
Grounding & classification
Source type: technical build writeup
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