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Imagine this: banks have been using AI and machine learning algorithms for decades to combat fraud. They’ve relied on systems built on rule-based approaches, models trained with historical data to detect anomalies—like strange spending patterns, purchases made far from home, or those unusually large withdrawals that make us all pause. It’s worked well, but only to a point.

But now, we have something new. A game changer: Generative AI.

Where traditional AI follows rules and retrained models, GenAI breaks free. It can detect subtle, complex, and even emerging patterns that older systems would miss. It doesn’t need rigid instructions because it learns in real time, evolving as it goes. It’s fast. It’s flexible. It’s fraud detection for a world where tactics change at the speed of light.

Let me give you an example. GenAI can sift through customer service chats, emails, and even social media posts, not just numbers on a screen. By understanding the context within these interactions, it can spot phishing attempts, account takeovers, and other fraudulent behaviors that slip through the cracks in our transactional data. GenAI understands language, not just transactions.

And it doesn’t stop there. While older AI systems require constant retraining when fraudsters change their game, GenAI adapts on the fly. It learns unsupervised, constantly evolving without needing us to tell it what to look for next. Think of it as a detective that’s always learning new tricks.

Beyond transactions, GenAI looks at the whole picture. It can analyze browsing habits, communication patterns, and personal information, building a richer, more accurate profile of a customer. This means fewer false positives, fewer blocked accounts from innocent purchases, and more accurate fraud detection.

And let’s not forget the tedious work of fraud investigations. Once an incident is flagged, it’s not over. Someone needs to dig through the evidence, analyze the patterns, write the reports. But with GenAI, much of that can be automated. It can summarize fraudulent behaviors, generate reports, and assist investigators, speeding up the entire process.

Now, we’ve all heard about the “black box” problem, right? AI making decisions we can’t explain. In banking, where trust is critical, that’s a big issue. But GenAI offers more transparency. It can be designed with explainability in mind, ensuring that we know exactly how it arrived at its decisions—critical for compliance, audits, and building trust in a highly regulated industry.

But what’s next? Agentic AI—the next evolutionary leap. While GenAI enhances human capabilities, agentic AI goes beyond. Imagine an AI that not only learns and adapts but also makes decisions on its own. It autonomously orchestrates actions, responds to emerging fraud threats in real time, and preempts fraudsters before they even attempt to strike.

Agentic AI could autonomously navigate entire fraud detection processes—setting strategies, adjusting fraud prevention measures, and making real-time decisions with minimal human oversight. It wouldn’t just detect fraud, it would counteract it, using vast amounts of data to make complex decisions faster than any human team could, while continuously optimizing for the evolving tactics of cybercriminals.

This is where we’re heading—a world where AI not only supports us but takes on a more active, decision-making role. GenAI has set the foundation, but agentic AI will usher in an era where fraud detection is proactive, predictive, and autonomously capable of safeguarding financial systems at a scale never seen before.

So, while traditional AI has been effective in spotting the fraud we know, and GenAI is getting us ready for the fraud we don’t, agentic AI will ensure we’re prepared for an unpredictable future. The future of fraud detection isn’t just about catching up or staying ahead; it’s about having AI that can anticipate the game before it’s even played. Thank you.

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