Abstract

Fraud detection is an ideal application of machine learning because traditional methods are often outpaced by the evolving tactics of fraudsters. Historically, organizations have relied on reputation lists, which filter fraud based on user metadata, and rules engines, which filter fraud based on transaction criteria. Both methods are static and require continuous maintenance by subject matter experts, resulting in detection algorithms that are perpetually a step behind fraudsters.

Machine learning offers a solution by enabling the identification of fraudulent activities based on patterns and trends that may not be immediately apparent to human analysts. This dynamic approach allows machine learning algorithms to adapt and recognize variations in behavior that signal fraud. Moreover, implementing a human-in-the-loop methodology can further optimize detection performance by combining the strengths of machine learning with human expertise, ensuring a more robust and responsive fraud detection system.