Training AI Agents Like Behavioral Scientists to Excel at Preventing Scams and Fraud

As scams become more advanced and personalized, the tactics used to manipulate individuals are increasingly rooted in behavioral psychology. What once required blunt deception now relies on nuance: fraudsters exploit victims' fears, biases, and emotional vulnerabilities with surgical precision. 

With fraudsters now equipped with generative AI tools and attacking with psychologically driven tactics, it's not enough for banks to rely solely on traditional fraud detection systems. AI agents need to do more than flag suspicious transactions to keep up. They must be trained to understand people (both victims and scammers) and have the capabilities to deliver personalized insights, warnings, and conversations to help customers recognize and break free from a scammer's influence.

Some of the world's largest financial institutions are beginning to realize that this training is necessary. In-house behavioral science teams like those led by Elizabeth Huppert, PhD, from JPMorganChase are now working hand-in-hand with fraud operations teams. The goal: to design technically accurate and psychologically effective intervention strategies. 

At Charm Security, after years of analyzing transactional data and behavioral patterns, we’ve learned that warning people that they're being scammed is very different from convincing them of it. The main challenge is moving from detection to effective prevention to “Break the Scam Spell.”

This shift reflects a broader truth: modern scam prevention is as much a psychological challenge as a technological one. Fraudsters know how to apply social engineering to induce urgency and exploit confirmation bias. If banks want to stay ahead of today’s fraudsters, their AI tools must do more than transform back-office functions. They must be able to detect anomalies and respond with empathy, clarity, and personalized engagement.

To achieve this goal, we must train AI models to think like behavioral scientists. In practice, this means simulating scam scenarios, reverse-engineering past incidents to understand emotional triggers, training AI with billions of scam identifiers, and clustering users by behavioral risk profiles, similar to how credit risk is modeled. Some banks have begun exploring real-time conversational interfaces that interact with customers during transactions. Instead of simply blocking a suspicious payment, these tools initiate a dialogue, explaining the risk and giving the customer a chance to reconsider. Early results suggest this approach significantly improves both scam prevention and customer satisfaction while mitigating reputational risk and lowering the false positive rates in certain cases.

Of course, not all interventions are created equal. Poorly executed friction, like generic prompts, scripted questions, or robotic messaging, can erode trust. Worse, it can cause vulnerable users to disengage entirely or allow scammers to take advantage of the predictable scripts to manipulate their victims. This is where psychology matters most. A well-trained AI agent should know how to de-escalate, listen, and guide a user back to safety without shame or confusion.

Ultimately, the future of scam prevention will depend on advanced AI models constantly trained on the latest scam trends and human vulnerabilities, increasing their ability to detect and intervene in real time. Banks that treat customer protection as a human challenge, compared to a compliance one, will be best positioned to lead. Training AI agents like behavioral scientists may sound unconventional, but in today’s threat landscape, it’s one of the most effective moves a bank can make.