How to Win the AI vs. AI Battle on Fraud

The financial services sector is undergoing a pivotal shift in its approach to combat fraud. AI-enabled fraud is on the rise, and losses to these sophisticated attacks are expected to reach $40 billion annually by 2027, up from $12.3 billion four years ago. Despite its industry potential, AI allows bad actors to act more quickly than ever before, intruding critical systems in under an hour, narrowing the window of opportunity for security teams to identify and defend against threats.

Security teams face a dual challenge: outsmarting increasingly sophisticated fraudsters while ensuring that protection never feels like a burden on customers. While traditional methods – like repetitive CAPTCHAs and manual verification calls – create unnecessary friction, AI-driven defense systems strike the balance of neutralizing risk while keeping the customer experience fluid. 

To win this battle, financial institutions must move beyond reactive, manual safeguards and embrace an orchestrated AI strategy that integrates behavioral biometrics, deepfake detection, and transparent governance. 

Modern Authentication Strategies

For many years, Multi-Factor Authentication (MFA) was the gold standard. However, research by Alloy confirms that device authentication using static credentials (e.g., a PIN, security question, or one-time code) is easily bypassed by Agentic AI tools, rendering many of these traditional static credentials obsolete. 

To win the battle, institutions are moving from “point-in-time” authentication to AI-powered behavioral biometrics authentication with invisible, real-time data collection via CAPTCHAs, including: 

• Dynamic Parameter Analysis: Systems now collect and analyze thousands of behavioral parameters simultaneously.
• Keystroke Dynamics: AI evaluates typing rhythms to distinguish the unique “digital fingerprint” of a human from a bot.
• Interaction Patterns: Monitoring scroll patterns and mouse movements allows for seamless verification without interrupting the customer experience.

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By integrating AI-powered biometrics with traditional verification processes in a hybrid model, banks can automate frontline defenses and simultaneously improve CX. Context-aware models reduce the false-rejection rate, preventing the frustration caused by blocked legitimate transactions. These biometric systems act as “first responders,” addressing threats instantly and providing rapid protection that builds customer trust. Said simply, banks know their customers increasingly more and thus prevent false positives and increasingly protect their clients.

Defending Against the Deepfake

Deepfakes are perhaps the most difficult fraud threat to counter. According to Experian’s 2026 Fraud Forecast, voice cloning can now be used to compromise a bank phone call with as little as 20 seconds of audio. Sometimes, deepfakes bypass MFA, using cloned voices or images for fraudulent biometric authentication. Though this threat is growing, it is important to note that deepfakes and MFA bypass are not always interchangeable. Instead, deepfakes are a sophisticated tool that targets specific biometric layers within an authentication portfolio. 

To counter this, financial leaders must adopt an AI-powered, human-centered approach. Rather than relying on a single tool or practice, orchestrated AI strategies seamlessly coordinate multiple technologies, human experts, and processes via “liveness” checks. For example, AI agents can autonomously check for deepfake behavior in video or audio. Decisions on how to defend against deepfakes are made instantly based on the threat level of sophistication. This is often done via Risk-Based Orchestration, where these systems may determine a low-risk scenario and present a step-up challenge during authentication. If a potentially fraudulent situation is deemed high-risk, the orchestration layer immediately triggers a human-in-the-loop (HITL) protocol, passing a structured summary of the threat to a human expert.  As these protocols continue to evolve, a feedback loop is established between AI and humans, with a flag identified and fed back into the AI model as a part of active learning.  This hybrid approach takes the best of all capabilities – technology and human-based – to create the ultimate defense mechanisms against fraud. 

Modern Regulations Require Modern Governance 

Modernizing fraud prevention strategies also requires staying up to date with modern regulations. For example, under both the EU AI Act and U.S. fair lending protections administered by the Consumer Financial Protection Bureau (CFPB), AI models used for credit decisions are subject to strict standards of transparency and accountability. The EU AI Act introduces some of the world’s most rigorous transparency requirements for high-risk AI systems, mandating clear documentation, explainability, risk management, and human oversight to ensure that automated decisions are understandable and subject to scrutiny. Similarly, the CFPB requires that financial institutions provide consumers with specific, legally required explanations for adverse actions, such as why they were denied a loan or why their credit was affected. Regulations like these move the industry toward a “glass-box” standard, where human oversight and Responsible AI frameworks ensure the speed of automation does not introduce systemic bias. As regulations continue to evolve, maintaining full auditability and HITL governance remains a cornerstone of consumer protection and regulatory compliance.

Security as the Profit Center 

The ability to protect customers is a financial institution’s most valuable brand asset. By orchestrating AI with people-powered fraud detection approaches, financial institutions can detect fraudulent behavior, verify identity, and operate more safely and efficiently. This “fight fire with fire” approach doesn’t just stop fraudsters; it creates a secure, frictionless, governed environment that makes your institutions the safest harbor for customer assets and reduces false positives. This builds trust and loyalty, which ultimately is at the heart of every financial institution’s goal. 

About TP

TP is a global partner that operates where people, processes, and AI meet.

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