Machine learning-based fraud prediction has become essential in organizations, with supervised learning being preferred due to its accuracy in recognizing known fraud patterns, even as unsupervised methods can identify previously unknown fraud cases.
While supervised ML models excel when they learn from existing fraud cases, they fail to adapt to emerging fraudulent schemes, allowing creative fraudsters to evade detection by exploiting gaps in model knowledge.
Organizations often neglect unsupervised learning models because of the belief that supervised models offer superior performance, but this can be a risky approach in the rapidly evolving landscape of fraud detection.
The adversarial nature of fraud means that organizations must be prepared to continuously evolve their detection methods, as fraudsters adapt and find new ways to bypass current defenses.
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