why AI agents need different fraud models

a reddit comment nails the risk: if we give agents card access, they'll become the new stolen-card problem unless fraud controls evolve.

traditional fraud detection watches for:

• unusual purchase location (IP geolocation)
• rapid-fire transactions (velocity rules)
• high-risk merchant categories (gambling, gift cards)

agents break all three signals:

• they use API keys, not browser sessions — no meaningful IP
• they might legitimately make 6 purchases in 90 seconds (price comparison)
• they might buy from a brand-new vendor with no reviews (best price)

agent-native fraud models need to track:

1. policy compliance — did the agent stay within its spending rules?
2. vendor reputation — does the merchant have an agent fico score?
3. intent alignment — does the purchase match the original instruction?

mnemopay's fraud module scores every transaction on those three axes. an agent that buys a $40 SSD when you said "cheapest 2TB SSD" gets flagged — even if the card network sees nothing wrong.

the fraud surface for agentic commerce isn't smaller than human commerce. it's different. and current fraud tools weren't built for it.