Deepfake-enabled fraud in banking and fintech has moved from an emerging risk to a daily operational reality. Since 2022, deepfake incidents and losses have grown by triple- and even quadruple-digit percentages. This is fundamentally changing how financial institutions think about identity, trust, and digital security. 

In 2026, deepfakes are expected to be embedded in most high-impact fraud scenarios: from onboarding and account takeover to payment authorisation and internal scams. As a result, banks and fintechs are being forced to adopt continuous, AI-driven, biometric and behavioural defenses as a baseline, rather than a competitive differentiator. 

The Growing Threat of Deepfake Fraud in Financial Services 

Deepfakes are AI-generated synthetic media that have become increasingly sophisticated, allowing fraudsters to create highly realistic impersonations of executives, employees, and customers. These artificial reproductions can bypass traditional security measures and authentication systems that financial institutions have long depended on for verifying identities and authorising transactions. 

The Financial Services Information Sharing and Analysis Center reports that deepfake tools have become remarkably easy to use: 

• Voice cloning requires only 20-30 seconds of audio recording 

• Convincing video deepfakes can be produced in approximately 45 minutes using free software 

This accessibility has effectively democratized fraud capabilities, putting powerful impersonation tools in the hands of a broader range of malicious actors. 

Both fintech companies and traditional banks have become major targets for AI-powered fraud schemes. These attacks exploit the trust-based relationships that underpin financial transactions and institutional decision-making. 

Research from Deloitte reveals the scale of the problem: 

• More than 40% of financial professionals have directly encountered deepfakes used in fraud attempts 

• 90% of survey respondents report that fraudsters are actively using generative AI in their operations 

Deepfake fraud has moved beyond theoretical risk to become a source of measurable financial losses. During the first half of 2025 alone, deepfake-related fraud losses exceeded $410 million, and some incidents now exceed $680,000 per event. 

Further industry projections estimate that generative AI-enabled fraud across the financial sector could reach approximately $40 billion annually by 2027. These figures highlight the urgent need for financial institutions to enhance their defenses through improved detection capabilities, stronger authentication frameworks, and comprehensive risk management strategies specifically designed to counter AI-enabled threats. 

The Evolving Threat Landscape: What Financial Institutions Face in 2026 

The financial sector is facing a new era of cybercrime. As 2026 approaches, AI-driven attacks are becoming more sophisticated, adaptive, and focused on core banking operations. Deepfake-enabled impersonation and account takeover (ATO) attempts are expected to move from rare events to daily challenges. 

Fraudsters now have tools to collect customer data, generate realistic deepfake audio, and test multiple attack methods simultaneously. Industry research predicts that AI-enabled fraud will reach $40 billion annually by 2027, up from $12.3 billion in 2023.  

Deepfake technology is expected to represent a rapidly growing portion of these losses. Impersonation attacks using deepfake technology are expected to become daily occurrences for many financial institutions. Predicted scenarios include: 

• Fraudsters using synthetic executive voices to authorise transfers. 

• Fake customer videos used to bypass remote identity-verification processes. 

• AI-generated personas that gradually build trust before executing financial fraud. 

Account takeover (ATO) represents one of the fastest-growing fraud categories facing financial institutions, with both attack frequency and associated financial losses demonstrating sustained double-digit growth rates. ATO occurs when unauthorised actors gain control of legitimate customer accounts. Deepfake technology has fundamentally altered the threat landscape by enabling fraudsters to generate highly convincing authentication credentials: voice samples, video verification, and biometric data—that make fraudulent access attempts increasingly difficult to distinguish from legitimate customer activity. 

Why traditional defenses fall short: 

These attacks target human trust mechanisms rather than exploiting purely technical vulnerabilities. As a result, conventional security controls, including static authentication protocols and rules-based fraud detection systems, prove insufficient when deployed in isolation. 

Organisations that rely on single-point authentication or purely automated detection systems face significant exposure to this evolving threat category. 

The threat posed by deepfakes extends beyond immediate financial losses to a more fundamental challenge: erosion of trust in the financial system itself. Addressing this requires a comprehensive security strategy that integrates advanced detection technologies with organisational culture change—fostering vigilance and critical evaluation of digital communications at all levels. 

Regulatory Pressure and AI Advancement Are Reshaping Requirements 

Although the regulatory landscape is shifting fast, AI adoption is accelerating even faster. The EU is rapidly updating its regulatory framework to address the growing use of AI in financial services.  

Financial institutions are navigating two simultaneous pressures: meeting stringent regulatory requirements under frameworks like the EU AI Act and DORA, while rapidly scaling AI across fraud detection, customer onboarding, and risk assessment. 

Regulatory requirements now mandate: 

• Privacy safeguards — Comprehensive data protection measures aligned with evolving AI-specific regulations 

• Security controls — Enhanced operational resilience standards for AI-powered systems 

• Explainability frameworks — Transparent decision-making processes that enable regulatory scrutiny and customer accountability 

Regulatory pressures from frameworks like the EU AI Act and DORA, combined with rapid AI adoption in fraud detection, customer onboarding, and risk assessment, are forcing financial institutions to integrate advanced privacy, security, and explainability requirements into their core operations.  

This dual dynamic creates competitive differentiation for those who can scale AI innovation while staying compliant. 

Organisations that stay ahead of these threats will differentiate themselves through three key capabilities: 

•  Cloud-Native AI Fraud Detection 

Platforms that provide transparency into how decisions are made, supporting both operational effectiveness and regulatory compliance requirements. 

• Biometric and Liveness Detection 

Technologies that meet evolving regulatory standards while maintaining smooth, friction-free user experiences. 

• Adaptive Security Systems 

Solutions that balance enhanced security with customer convenience, recognising that excessive friction in legitimate transactions can harm customer satisfaction and retention. 

The combination of advanced technology and user-centred design will determine institutional resilience and competitive position in an increasingly complex fraud landscape. Success will require solutions that are simultaneously secure, compliant, and easy to use. 

Redefining Trust in Digital Financial Services 

In 2026, both consumers and businesses are operating in a landscape increasingly defined by scepticism. The proliferation of sophisticated fraud techniques has fundamentally altered how people view digital interactions with financial institutions. 

The strategic implication is clear: trust in digital banking can no longer be established through a one-time verification and then assumed to hold indefinitely. Financial institutions must implement continuous authentication models that validate identity and legitimacy throughout the entire customer relationship. 

In 2026, trust requires continuous validation across: 

✓ Every customer interaction 

✓ Each transaction 

✓ All communication channels 

Deepfake technology has forced financial institutions to completely rethink their approach to identity verification, authentication protocols, and fraud prevention strategies.