Fraud is evolving—and fast, and the very tool fuelling that evolution is generative AI. The same technology powering ChatGPT and image generators is now helping fraudsters write phishing emails, clone voices, and impersonate bank employees with terrifying precision.
This isn’t a theoretical risk. It’s happening. Every day.
Go deeper with GlobalData
And banks and credit unions are squarely in fraudsters’ crosshairs.
In recent months, we’ve seen a sharp rise in AI-enabled scams targeting financial institutions.
In December, 2024 the FBI issued an alarming alert about deepfake schemes and AI-generated voices being used to fool family, friends, and financial institutions.
According to the Federal Trade Commission, Americans lost over $12.5 billion to fraud in 2024, which marks a significant increase of about 25% to 35% compared to 2023 —a record—and experts believe that AI played a major role in the increase. And Bloomberg reported last year that banks are being tested by new fraud techniques that combine social engineering with synthetic identities generated by AI.
US Tariffs are shifting - will you react or anticipate?
Don’t let policy changes catch you off guard. Stay proactive with real-time data and expert analysis.
By GlobalDataBanks: scrambling to keep up with fraudsters
Simply put, it’s getting harder to tell what’s real, and that’s exactly what bad actors want.
In my conversations with CTOs and CIOs at banks & credit unions across the country, I hear the same concern over and over: “We’re seeing attack types we’ve never faced before—deepfakes, voice scams, AI-driven synthetic IDs—and we’re scrambling to keep up.”
Many of these institutions are staffed by smart, experienced professionals. But they’re under-resourced, under-staffed, and increasingly under siege.
Larger banks have started pouring resources into AI detection systems. JPMorgan Chase is investing in proprietary models to flag suspicious behaviour in real-time. But smaller institutions? They often can’t afford to build in-house AI teams. A 2024 survey from the American Bankers Association found a startling unfamiliarity with AI.
“While roughly 97% of respondents polled on how familiar they were with AI said they were either very or somewhat familiar with the tech, only 74% had some understanding of the difference between the two main forms of AI,” per the ABA’s survey of 127 financial institution executives. “26% had no understanding whatsoever.”
That’s a vulnerability. Fraudsters know it.
They’re testing defences at the edges: Launching deepfake video scams on unsuspecting branch staff. Sending phishing texts that use perfectly mimicked language from internal memos. One credit union CTO I spoke with described a spoofed email so convincing, it fooled a veteran employee into initiating a wire transfer. The email wasn’t just written well. It was trained on years of past emails from the organisation—scraped, compiled, and mimicked by an AI.
A new battlefield-and the only playbook will not cut it
But here’s the good news: Banks can fight back using the same weapon that’s being used against them.
Generative AI isn’t just a threat. It’s a tool. In the right hands, it becomes a powerful shield.
AI-powered fraud detection platforms can now analyse transaction patterns in real-time, flag anomalies, and even simulate likely attack vectors before they happen. Behaviour-based models don’t just look for bad actors—they learn what “normal” look alike for a given account, user, or network. That means they’re better equipped to catch the outliers that traditional systems might miss.
Even small institutions can deploy AI through cloud-based tools. Vendors are now offering scalable, subscription-based platforms tailored for community banks and credit unions. Some even specialise in training models on local data, making them uniquely sensitive to the patterns and rhythms of smaller financial ecosystems. You don’t need a PhD in machine learning to get started—you just need the will to act.
The point isn’t to replace people. It’s to augment them. To empower your frontline staff, your compliance officers, and your fraud teams with better tools. With faster response times. With early warnings that give you a chance to act before the money disappears.
Of course, no system is perfect. Fraud is a moving target. But not deploying AI because you can’t afford perfection is like refusing to lock your doors because the thief might pick the lock anyway.
A moment of reckoning – and a moment of opportunity
Small banks and credit unions have always thrived by putting community first—by offering trust, relationships, and service that big banks can’t replicate. Protecting those relationships now means investing in the tools that can keep them safe.
Don’t wait until an AI-generated voice fools your teller. Or a synthetic identity opens an account and vanishes with a six-figure loan. Start exploring your options today.
In my experience, there are at least three main qualities to look for when your CTOs and CIOs are deciding which anti-fraud AI tool to employ:
AI-Native Threat Detection: Choose vendors that don’t just tack on AI—but build their fraud prevention solutions around real-time, AI-powered behavioural analytics, anomaly detection, and synthetic media recognition to identify deepfakes, voice clones, and evolving scam patterns.
Rapid Integration & Interoperability: Look for platforms that can be deployed quickly and integrate seamlessly with existing core banking systems, CRMs, and KYC tools. The ability to adapt fast is critical as fraud tactics evolve faster than ever.
Explainability & Human-in-the-Loop Oversight: The best vendors provide transparent AI models with clear audit trails and human verification checkpoints—giving compliance teams insight into why a transaction was flagged and ensuring regulators stay on your side.
AI in fraud management at banks enables real-time detection of suspicious transactions by analysing patterns and anomalies in vast datasets. Machine learning models continuously adapt to evolving fraud tactics, improving accuracy and reducing false positives.
The banking industry must look at AI-powered hybrid fraud detection solution that combines the strengths of rule-based engines with the adaptability of AI-driven models. At its core, the system identifies discrepancies between traditional rules (e.g., “3-in-30” transaction velocity checks) and AI predictions, using these gaps as a learning opportunity. When the AI model fails to flag a fraud that the rule engine catches, the discrepancy is logged, analysed, and used to synthetically generate new, realistic fraud-like transactions. These synthetic examples are then injected back into the training pipeline, allowing the AI to retrain and improve its generalisation over time.
The key innovation lies in its self-correcting feedback loop—an automated mechanism where the AI learns from its own mistakes without requiring constant human intervention. Through iterative training and synthetic data augmentation, the system demonstrates increasing accuracy, precision, and recall with each retraining cycle. This approach allows it to detect subtle and evolving fraud patterns that static rules often miss, making it an ideal foundation for a future-ready, intelligent fraud detection platform suitable for real-time banking environments.
Beyond that, train your teams; run tabletop simulations; ask hard questions: What would we do if a scammer spoofed our CEO’s voice? How would we know?
The fraudsters have swords. But we have shields. We just need to lift them.
AI isn’t just part of the problem. It can be the solution.
The time to act is now.
Anoop Gala is the Senior Vice President, Head of Financial Technology Services Infinite Computer Solutions
