How ML Models, Real-Time Analytics Can Mitigate APP Fraud

Machine learning helps financial institutions detect authorized push payment fraud by analyzing large transaction datasets faster. These AI models operate in near real-time and can distinguish legitimate transactions from fraudulent ones by spotting anomalies, said Jennifer Pitt, senior analyst at Javelin Strategy & Research.

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“These machine learning models learn from historical data. They look at legitimate transactions versus fraud transactions, not only from the consumer but across the institution, and machine learning can essentially train models to determine or detect which transactions are legitimate versus fraud by detecting subtle anomalies rather than traditional, larger anomalies that humans would be able to detect,” Pitt said.

But for AI-powered fraud detection to work, real-time monitoring is critical. Unfortunately, many financial institutions still review transactions days later, missing opportunities to stop fraud before losses occur. “We need real-time monitoring, actually in the moment, real-time monitoring analytics that happen in the background,” Pitt said.

AI models must adapt to new fraud trends, continuously improving accuracy while reducing false positives and negatives. Risk scoring in real time also plays a crucial role in identifying suspicious transactions and acting quickly, she said.

In this video interview with Information Security Media Group, Pitt also discussed:

• The role of generative adversarial networks for training fraud detection models;
• Why real-time analytics and adaptability are critical for stopping APP fraud;
• How financial institutions can balance AI-driven fraud prevention with compliance requirements.

Pitt, a senior analyst at Javelin Strategy & Research, analyzes consumer data and provides fraud prevention and cybersecurity recommendations. Her background includes working in law enforcement and banking and conducting and overseeing complex fraud and money laundering investigations.