Fraud is no longer the work of isolated bad actors; it has evolved into a sophisticated, organized industry.
According to Federal Bureau of Investigation data, U.S. losses from cyber-enabled fraud reached almost $20.9 billion in 2025, up from just $4.2 billion in 2020. The FBI received more than one million fraud claims last year, with victims skewing heavily toward those over 60 years old.
The top three most common schemes, according to the Federal Reserve, are debit card fraud, check fraud and nonbank payments apps fraud. Automated Clearing House, faster payments and credit card fraud are also high on the list.
The numbers are alarming on their own, but what’s more alarming is how fast they’re climbing. Artificial intelligence (AI) didn’t create financial fraud, but it has supercharged it.
Today’s fraudsters are increasingly using AI to enhance and scale a wide range of attacks. From generating convincing deepfakes and manipulating documents to powering sophisticated phishing campaigns, impersonation attempts and account takeover schemes, AI is making fraud faster, more personalized and more difficult to detect. It is also being used to support business email compromise, synthetic identity fraud, investment scams and other forms of social engineering, enabling fraudsters to operate with greater speed, precision and scale than ever before.
So, what are financial institutions doing about it?
The good news is — quite a lot. Cybersecurity spending continues to increase. Many large financial institutions and some forward-thinking community banks have started leveraging AI for things like Bank Secrecy Act (BSA) support, check fraud image analysis, behavioral analytics, biometric verification, anomaly detection and document review.
Financial institutions are also investing in customer education — particularly for older customers who are more vulnerable to scams — and training staff to recognize and respond to increasingly sophisticated fraud tactics.
However, adoption remains uneven. While some institutions are embracing AI as a core part of their fraud strategy, others are still in the early stages of implementation. Legacy architecture remains a big barrier, as many core systems were not designed to integrate with modern AI tools.
There is also the challenge of trust and oversight, with institutions seeking greater transparency into how AI models arrive at decisions and how those decisions can be governed. As a result, a gap exists between what today’s technology can do and what many organizations are currently deploying.
Closing that gap isn’t a matter of buying more technology. In fact, adding another tool can sometimes make the problem worse. The concept of alert fatigue has emerged as a real operational challenge. When fraud detection systems generate too many false positives, analysts become overwhelmed and begin to lose trust in the alerts themselves, allowing fraud to slip through the cracks.
The goal should be to deploy the right technology. When evaluating fraud prevention vendors, three criteria should drive the conversation:
- Governance.
- Flexibility.
- Experience.
Financial institutions need solutions that are explainable, auditable and aligned with their risk management frameworks, while remaining flexible enough to adapt as fraud tactics evolve.
It’s also important to choose reputable, experienced partners with a deep understanding of the financial services industry and a proven track record of helping institutions combat evolving fraud threats.
Looking ahead, the direction is clear: there needs to be more data sharing between institutions, greater standardization in how that data is exchanged, expanded use of third-party verification services and continued investment in AI-driven detection. But one thing won’t disappear anytime soon — the human in the loop. No model catches everything, and seasoned fraud examiners bring their unique experience and intuition that no algorithm has fully replicated.
Fraudsters are already using AI to operate faster, smarter and at greater scale — so financial institutions must do the same. The challenge is not whether to adopt AI, but how to adopt it responsibly with the right governance, the right partners and the right balance between automation and human oversight.
Previous