Bad actors are skilled at using GenAI as a weapon: Here’s how banks can maintain their defenses

Generative AI is turbo-charging the financial services industry, but it is unfortunately doing the same for bad actors looking to skim off the top by exploiting security gaps with phishing attempts and impersonation schemes.

Small and regional banks, as well as credit unions, are becoming targets of choice as fraudsters weaponize AI to scale their deception with newfound realism and speed.

AI is in fact, reshaping everything, and this includes the economics of financial crime. Sophisticated scams are now cheaper to create, easier to deploy and far more convincing than schemes of the past.

Fortunately, there are steps banks can take to ensure that their defenses keep pace with the constant and evolving threat.

‘Credibility Engineering’

Fraud is growing rapidly, as AI enables impersonation and phishing schemes to scale, become more credible, and reach wider audiences.

A critical enabler of this could be called credibility engineering. Attackers no longer rely on a single touchpoint. Instead, they orchestrate multi-channel interactions to reinforce legitimacy. 

A spoofed phone call, for example, could be followed by a confirmation message on WhatsApp or an email that appears to originate from a trusted institution. Caller IDs are manipulated, digital footprints are fabricated, and each interaction is designed to reduce skepticism and accelerate compliance.

Scams like this are no longer a back-burner risk for banks. In 2025, roughly two-thirds of financial institutions reported an increase in fraud. At the same time, the nature of those attacks is shifting, with organized crime rings driving the majority of fraud attempts.

That shift is being accelerated by generative AI, turned fraud into an operational risk, not just a security concern. Fraudsters are not held back any longer by language barriers, technical skills, or scale. They can quickly produce convincing identities, voices, and communications on demand and deploying them across multiple channels simultaneously. The result is fewer obvious red flags and a higher likelihood that fraudulent transactions appear legitimate to both customers and institutions.

For smaller financial institutions, defending against these well-armed bad actors can be especially challenging. Community banks and credit unions often operate with limited resources, meaning they can seldom match the technological investments made by global, money-center banks. 

Many smaller banks are already straining to fend off cyber-attacks that range from deepfake videos targeting internal staff to highly personalized phishing campaigns trained on historical communications. Fraudsters are aware of these gaps and have learned to probe the edges of defensive systems to find weak links.

But the technology that functions like a wind in the sails for fraudsters is also enabling a powerful response from banks. That’s because AI is not just a threat, but also the most effective defensive weapon.

Financial institutions are increasingly deploying AI-driven fraud detection systems that can quickly establish a baseline of “normal” activity for each customer, account, or network. When anomalies in transaction patterns, log-in behavior or communication signals occur, the system can flag or block activity instantly. 

Hybrid Models

One of the most promising developments for smaller banks looking to strengthen their perimeter is the emergence of hybrid fraud detection models. These systems combine traditional rule-based engines with adaptive machine learning. 

When discrepancies arise, for example, a rule that catches a fraud attempt that the AI model misses, the system logs the gap and uses it to generate synthetic training data. This self-correcting feedback loop enables continuous improvement. It enables AI to adapt to evolving tactics without the IT team having to constantly intervene manually.

Fortunately, sophisticated capabilities like these are no longer the sole purview of larger banks. Cloud platforms and subscription models are making AI-powered defenses accessible to community banks and credit unions. Some programs can even be tailored to local market dynamics, training models on regional data to improve accuracy. 

When CTOs and CIOs are comparison shopping for AI-powered products to shore up defenses, they should be looking for:

• AI-Native threat detection
• Rapid integration and interoperability
• Explainability and ‘Human-in-the-Loop’ oversight

These are the basic ingredients that can give smaller banks the edge that used to be reserved for much larger institutions.

The Human Factor

Technology on its own will never be enough when an organization is facing risks that are fast-evolving and multifaceted. That’s why banks must also invest in training, simulation, and preparedness. Employees should be equipped to identify deepfake scenarios, while tabletop exercises should be conducted regularly to test responses to complex fraud incidents. 

Consumers and bank customers also have a role to play when it comes to constant vigilance. They must take steps to secure accounts, notify financial institutions, preserve communications as evidence, and report incidents to authorities. 

Thanks to the emergence of powerful new AI models, the financial sector has reached a moment of reckoning. But it is also a time of opportunity. The institutions that future-proof their operations will be those that embrace AI not just as a defensive strategy, but as a strategic edge.

Community banks and credit unions have long built their businesses on trust and relationships. Protecting that trust now means investing in tools that can safeguard customers in a time where reality itself can be convincingly faked.

Fraudsters may have sharpened their tools, but the industry is far from defenseless. With the right combination of technology, training, and awareness, banks can turn AI into a shield to protect against those who use it as a weapon.

A quote or advice from the author: One of the most promising developments for smaller banks looking to strengthen their perimeter is the emergence of hybrid fraud detection models. These systems combine traditional rule-based engines with adaptive machine learning.When discrepancies arise, for example, a rule that catches a fraud attempt that the AI model misses, the system logs the gap and uses it to generate synthetic training data. This self-correcting feedback loop enables continuous improvement. It enables AI to adapt to evolving tactics without the IT team having to constantly intervene manually.

Stay Ahead of the Financial Curve with Our Latest Fintech News Updates!