Machine learning based detection of fraudulent acquirer transactions

This invention describes a computer system that watches for fraudulent activity at ATMs. It gathers information about ATM withdrawals, like the transaction card used and details from the ATM itself. This data is fed into a machine learning model, specifically a Decision Tree Classifier, which analyzes it to identify suspicious patterns. If fraud is detected, the system takes steps to address it, involving communication between specialized processing circuits.

Why it matters: The core machine learning approach for fraud detection was already well-established in 2023. The ongoing challenge remains the continuous need to update and adapt models to counter rapidly evolving fraud tactics.

Status: ExpiredProtection ended and was never renewed
How hard to build: SpecializedSoftware logic, machine learning model

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