Real-Time Fraud Detection through Agentic Systems

Agentic Systems for Real-Time Fraud Detection investigates a real-time fraud detection approach that combines agentic systems with real-time data processing. Agentic systems are capable of autonomous, goal-directed activity and—when enabled to make decisions independently—they can react to suspicious activities quickly without human intervention. By detecting fraudulent transactions at first sight, the risk of financial loss induced by fraudulent transactions is minimized. The sections “Real-Time Data Processing” and “Fraud Detection Algorithms” identify challenges related to the real-time collection and streaming of data, outline techniques that support real-time data processing, and discuss methods for recognizing fraudulent transactions. The principal motivation for a real-time fraud detection approach is explained in “Background and Motivation.”

The demand for real-time fraud detection derives from the fact that the faster a fraudster is detected, the smaller the amount of money lost—hence, the greater the incentive for financial institutions to implement real-time fraud detection. Given that large financial institutions generate thousands (or even millions) of transactions daily, not all suspicious activities can be investigated by humans; therefore, automating the credit card risk management process becomes imperative. Service providers typically have access to cardholders' historical transaction details, ratings, and reports from various resources. When a new transaction is initiated, the service provider assigns a risk score, which helps the cardholder monitor the state of transactions in real time. Nevertheless, service providers’ decisions depend on how the fraud detection models are built—specifically, the methods of fraud and risk score calculations and the nature of the data on which the models are built, such as whether data are collected in a batch or streaming mode. An outline of these challenges is provided in “Future Directions.”