Despite the hype, generative AI doesn’t necessarily make fraud more sophisticated. Instead, what we're seeing is that AI is making existing fraud schemes more efficient and cheaper to execute at scale.

Another common misconception is that generative AI is a good tool to fight generative AI, fighting fire with fire. And while generative AI is a great tool to perpetuate fraud faster, it doesn’t have a practical place in fraud prevention. To combat fraud, credit unions need a robust strategy that should include things like machine learning models to better identify fraud alongside other solutions and data sources that help identify who is a real customer and who is a bad actor (or a fake one for that matter).

Credit unions' distinct fraud prevention needs are shaped by their commitment to member relationships and limited resources to put toward continuous updates to technology infrastructure. For example, credit unions historically focused on in-office member experiences, but as members' needs evolved to demand more online and mobile offerings, onboarding funnels moved to digital channels. And digital onboarding fraud prevention processes can be highly manual, time-consuming and rigid.

Over time, credit unions did build models to automate this process, but those models didn't evolve as quickly as fraudsters changed their tactics.

For the past few years, there has been a lot of promise in AI’s ability to fight fraud. While 2025 could be the year this will translate to real applications of machine learning to help credit unions navigate new risks, there still seems to be a misconception that by simply implementing machine learning and AI, institutions should be better equipped to detect and prevent all forms of fraud – including the sophisticated AI-driven scams on the rise.

Instead, credit unions need to see AI for what it is: An automation tool rather than a precision tool. It's true that AI models have applications in fraud prevention, like recognizing and explaining patterns in a dataset, and can help create synthetic data to train machine learning models, but on its own AI is not a magic bullet.

One thing we’ve heard from our clients is that there is often a lack of clarity on what it actually means when a product claims to use AI, which can lead to confusion and skepticism among decision-makers at credit unions. While AI and machine learning models can help analyze vast amounts of identity and transaction data to spot patterns and anomalies that may indicate fraud, these models are only as good as the data fed to them. Further, those insights alone aren’t valuable if they aren’t actionable in nature, meaning they flag an anomaly and then provide insight into how to contain it.

Before rushing to implement AI solutions, credit unions need to focus on strengthening their fundamental controls. Think about fraud prevention like building a house – you need a solid foundation before you start adding sophisticated security systems. This means implementing robust step-up authentication, enhancing document verification protocols and deploying real-time transaction monitoring. These foundational elements might not grab headlines like AI does, but they've proven to be effective at detecting and preventing fraud in real-time.

The challenges facing credit unions in 2025 are significant, but they're not insurmountable. By focusing on fundamentals while strategically incorporating new technologies, credit unions can protect both their institutions and their members. The key is to stay focused on what actually works, rather than getting caught up in the hype of the latest technology trends.