Executive Summary

Sophisticated AI-generated and synthetic media can bypass traditional banking security protocols. As a result, banking deepfake fraud is an escalating threat, with tens of billions of dollars at risk. Generative AI is increasing the risk of deepfake-related losses. Deloitte’s Center for Financial Services projects that generative AI could cause fraud losses of $40 billion in the U.S. by 2027 — up from $12.3 billion four years earlier. The average loss per financial sector company is more than $600,000, with 23% of financial services organizations reporting losses of more than $1 million due to deepfake fraud, a 2024 Regula survey found. 

Attack Vectors

Voice banking bypass

Voice-based authentication was once heralded as a fraud prevention tool for banks, but today voice banking fraud is a primary attack vector for deepfake attacks, with criminals using synthetic voices to trick KYC systems. Recent examples demonstrate this vulnerability. A Wall Street Journal reporter successfully cloned her own voice with AI and evaded her bank’s security measures. Meanwhile, University of Waterloo researchers developed a method to bypass voice authentication with up to 99% success in just six attempts.

Video KYC fraud

By fooling video verification systems with fake personas during onboarding, bad actors can enable financial crimes such as loan fraud, investment scams, insurance fraud and money laundering. These KYC deepfakes can hijack legitimate accounts and use social engineering to access sensitive data, putting the integrity of systems at risk.

CEO Impersonation

Deepfake impersonations of executives often involve urgent requests for money transfers or access to sensitive information. These attacks exploit trust in decision-making hierarchies and evade traditional security through advanced social engineering.

Real Cases: The Cost of Deepfake Deception

The financial impact of deepfake fraud is substantial and growing rapidly. Banks that use voice authentication without additional measures allow fraudsters with voice samples and stolen personal information to enable transfers, cashouts and other transactions.

$35 million wire fraud: In 2020, fraudsters used AI voice-cloning technology to impersonate a company director and trick a Hong Kong branch manager of a Japanese firm into transferring $35 million, supported by follow-up emails that appeared legitimate. The case highlights the growing threat of deepfake audio in financial crime and the urgency for better fraud detection and authentication methods.

Account takeover ring: In April 2025, Hong Kong police intercepted a fraud network using financial services deepfakes to open bank accounts. Scammers used AI to merge their own faces onto lost IDs. Losses were in excess of $193 million. In early 2024, fraudsters used AI-generated video to impersonate a company's CFO during a video conference call. An employee who thought this was an authentic request enabled the account takeover by transferring $25 million to the scammers.

According to research from cybersecurity firm Proofpoint, 99% of customer organizations it monitored in 2024 were targeted for account takeovers —  and of those, 62% experienced at least one successful takeover. As deepfake tools become more accessible, the potential for voice-based authentication workarounds continues to grow.

Protection framework

Multi-Modal Verification

Effective banking authentication now requires multiple verification layers beyond traditional voice recognition. Financial institutions and insurance companies are implementing multimodal verification methods, recognizing that biometric information — while crucial — is insufficient to protect against AI fraud threats. Biometric systems don’t become obsolete when bolstered by AI voice detection, public customer data verification and other measures.

Real-Time Detection

Without the ability to detect threats in real time, enterprises face substantial risks of reputational damage, data breaches and financial fraud. Audio detection technology rapidly identifies synthetic and manipulated speech and voice clones of executives, employees, and customers — while a conversation or meeting is taking place. Meanwhile, real-time video detection tools detect synthetic faces, manipulated movements and other telltale signs of video manipulation across crucial applications like web conferencing platforms and authentication workflows, helping enterprises maintain trust in their video communications. 

Staff Training

Employee education is critical, as human oversight serves as the final defense against sophisticated deepfake attacks. Bank and financial services firms should learn to recognize potential deepfakes of high-net-worth clients requesting fund transfers, while accounting staff should be trained to verify executive requests through alternative channels. 

Quick Start Guide

Here are three immediate steps institutions can take:

1. Callback verification for all high-value transaction requests.
2. Multi-factor authentication combining biometric and behavioral verification methods.
3. Staff training on deepfake recognition, verification protocols and incident response plans, including procedures for detecting, containing and recovering from deepfake attacks. 

Resources

• FS-ISAC Deepfake Taxonomy
• CISA Cybersecurity Performance Goals
• DHS AI Safety Guidelines

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