MACHINE LEARNING BASED DETECTION OF FRAUDULENT ACQUIRER TRANSACTIONS

Examples described herein relate to apparatuses and methods of detecting fraudulent activity at an automated teller machine (ATM) using a machine learning model. A method includes receiving ATM activity data indicative of one or more withdrawal transactions at one or more ATMs using a transaction card, receiving transaction data and ATM data, ingesting the transaction data and the ATM data, analyzing the ingested transaction data and the ingested ATM data using a machine learning model, determining that the ingested transaction data and the ingested ATM data indicate a likelihood of fraudulent activity using the machine learning model, and performing, using the machine learning model, one or more remedial actions based on the determined likelihood of fraudulent activity.