How Deepfake Fraud in Banking Poses Risks for Financial Institutions

The risk of deepfake fraud has shifted from a theoretical concern to a real, immediate threat to the banking sector’s digital onboarding and account access. Today, identity thieves do not need stolen passwords or forged documents. They simply use AI-generated faces that are so realistic they can fool identity verification systems. These kinds of attacks target remote verification methods, which rely on face recognition, liveness test, and automatic verification, and thus create fast-changing risks for digital banking. The risk is already present, and as banks and other financial institutions expand their digital offerings, the risk of fraud is also rising.

According to the Signicat report called Battle Against AI-Driven Identity Fraud 2024, the percentage of fraud cases using AI in the form of deepfakes has been at an approximate level of 6.5%, which is a drastic rise of over 2,000% in the last three years. This alarming trend does not just reveal that there is an increasing number of cases of fraud, but it also indicates that the criminals’ tactics are getting more advanced since they are taking advantage of the flaws in the digital verification processes.

To protect customers and secure digital operations, financial institutions need to understand how deepfake fraud operates and its effects. It is mandatory to enhance identity verification and fraud prevention to not only keep pace with this new threat but also ensure uninterrupted digital banking services.

What Banks Need to Know About Deepfake Attacks

Deepfakes are media that are AI-generated, usually images or videos, and that are impersonating human beings in a very convincing way. However, the deepfakes’ very initial version was very awkward and easy to detect, but the recent developments have brought them to a state where they are completely indistinguishable from the real human images and videos. 

A 2025 industry analysis projects that the total volume of deepfake content could reach 8 million pieces online globally , up from just 500,000 in 2023. Fraudsters are now applying this capability to financial systems by creating synthetic profiles or manipulated content that is engineered to deceive automated identity checks.

Fraudsters are taking advantage of deepfake technology to produce completely synthetic representations of identities that can easily surpass the automated identity verification process, or they can even resort to AI-powered presentation attacks that are specifically aimed at deceiving liveness detection systems. The use of these methods allows criminals to conduct activities such as opening fake accounts, filing fake loan applications, or, in general, getting unauthorized access while never having to interact with a human being.

Deepfake fraud in the case of digital banking might not be explicitly seen, but its implications are drastic. The use of AI-generated impersonations could penetrate through remote onboarding, get past identity checks, and even lead to the submission of fake loan applications or unauthorized access to accounts. As deepfake fraud becomes integrated into broader attack kits, banks and FinTechs must understand that this threat is fundamentally about identity deception, not just clever visuals.

Why Traditional KYC Fails Against Deepfake Fraud in Banking

Most legacy identity verification methods were designed for human fraudsters relying on stolen information, not AI‑generated threats. Common weaknesses include:

• Static Verification Only: Comparing a submitted photo ID or a single selfie does not verify whether the identity is genuine or live, allowing deepfake images or videos to slip through.
• Absence of Multi-Signal Verification: When systems fail to incorporate document authenticity, biometric consistency, and time-based live cues, they cannot detect very faint manipulations.
• Reactive Threat Models: Financial institutions typically adjust their rules based on previous fraud types such as phishing or card skimming, which, however, do not provide the desired protection against highly sophisticated fake identities.
• Training Gaps: Numerous companies do not think the AI-based fraud to the extent it reaches, and a considerable percentage of the security teams do not have even basic knowledge of new forms of attack.

Consequently, deepfakes have the potential to pass through traditional onboarding authentication measures without being noticed or to cause low suspicion, which puts a premium on user experience rather than layered security.

The Real Financial Impact of Deepfake Fraud in Banking

Deepfakes are no longer just a tale from a fantasy world, yet heavy financial consequences accompany them. Industry studies found that the financial services firms  incurred average losses of more than $600,000 per company due to deepfake-related fraud.

• The losses from identity fraud caused by AI-powered attacks have increased more than twofold within just a few years, which is a clear indication of how fast the attackers have implemented and upgraded their methods.
• Companies that are ineffective in modernizing fraud defense are likely to witness a spike in costs, as a portion of it will undoubtedly go on damages and corrective efforts.

These figures reinforce that deepfake banking fraud is not an abstract future threat; it’s a current and costly problem.

How High‑Assurance KYC Protects Banks from Deepfake Fraud

In order to successfully defend against new types of fraud, such as deepfakes, financial institutions have to not only rely on one-time authentication methods but also apply multi-layered, high-assurance identity verification. Among the main components are:

Document Verification

Authenticity checks powered by AI perform detailed investigations of identity documents to detect  any signs of forgery, tampering, or the establishment of fictitious identities. The verification technologies of today are capable of identifying the real issuance through the examination of holograms, fonts, watermarks, and microprinting.

Face Matching

The matching of a live person’s facial traits to those on official IDs acts as a secondary authentication layer. Superior biometric comparison speeds are able to tell the difference between authentic faces from those that have been digitally manipulated.

Liveness Detection

Liveness checks do not depend on static pictures but rather consider movements, depth, and time signals to ensure that the real human being is in the spot where the capturing is done. Thus, the possibility of the system being deceived by spoofing and presentation attacks is lowered.

Multi‑Signal and Risk Analytics

By correlating device data, session context, geo‑location signals, and behavior patterns, banks can assess whether a user’s access attempt fits expected norms and flag anomalies for additional scrutiny.

These combined layers make it significantly harder for deepfake fraud to succeed without introducing friction for legitimate users.

Regulatory Requirements for Deepfake Fraud Prevention in Banking

Non-compliance not only increases regulatory risk but also weakens customer trust in digital banking channels. For example:

• FATF (Financial Action Task Force) emphasizes customer due diligence and risk‑based approaches to identity verification.
• NIST (National Institute of Standards and Technology) provides biometric and presentation attack detection standards, pushing financial services toward stronger ‘liveness’ and anti‑spoofing checks.

Failure to meet these evolving expectations may result in regulatory scrutiny, penalties, and increased audit requirements.

Practical Steps Banks Can Take to Prevent Deepfake Fraud

• Banks that want to be one step ahead of the deepfake threats should take the following actions into consideration:
• Deploy Layered Verification: The combination of document checks, biometrics, and liveness will give rise to a stronger assurance.
• Adaptive Risk-Based Authentication: Adjust the friction depending on the levels of transaction risk; that is, higher risk requires stronger checks.
• Continuous Monitoring: Implement real-time analytics to find out the unusual behavior or suspicious access patterns.
• Employee Training and Awareness: Provide teams with information about synthetic fraud trends and detection signals.
• Incident Response Plans: Create well-defined workflows for fraud event investigation and suspected remediation.

Integrating these practices positions banks to respond to both current and emerging fraud vectors.

How Facia Empowers Financial Institutions to Combat Deepfake Fraud

Deepfake fraud in banking has turned from a remote what-if scenario to a fact that is already changing the fraud landscape. With incidents rising sharply and average losses mounting, financial institutions must embrace high‑assurance identity verification as a core defense. By combining smart document verification, strong face matching, and certified liveness detection, banks’ exclusive sectors can repel deepfake-enabled fraud without sacrificing user experience.

Facia provides these layered protections natively, enabling banks to safeguard remote onboarding, meet regulatory expectations, and reduce exposure to deepfake‑related fraud attempts.

Learn more about Facia’s solutions to see how Financial institutions can protect themselves against rising deepfake fraud.