AI-powered fraud is moving faster than the companies trying to stop it. Deepfake videos that fool live identity checks. Fake people with perfect credit histories. Bots that learn how fraud detection works and then dodge it. These are not future problems. They are happening right now.
We asked five industry leaders one simple question: what is the biggest AI-powered fraud threat fintech companies are not prepared for in 2026? Their answers point to a common theme. The tools fintechs built to catch fraud were designed for a different era. And the attackers have already moved on.
Deepfake injection is breaking identity verification
The most visible threat is deepfake video being injected directly into live KYC (Know Your Customer) verification sessions. This is not someone holding a photo up to a webcam. Attackers now stream AI-generated faces through virtual cameras or intercept the data feed between an app and its verification provider. The system sees what looks like a real person in a real video call. It is not.
The numbers back this up. Sumsub recorded a 1,100% increase in deepfake fraud in the US in Q1 2025 compared to the same period in 2024. Entrust found that a deepfake identity attack happens on average every five minutes. Underground services sell the full workflow for less than the cost of a streaming subscription. Generate a face, create a fake ID, pass verification. Done.
Gartner predicted in early 2024 that by 2026, 30% of enterprises will no longer consider facial biometrics reliable as a standalone verification method. That timeline is looking generous.
“The most important AI-based fraud threat that fintech companies will face by the year 2026, which companies have not prepared themselves for, is the industrialisation of real-time deepfake injection into live KYC video verification. Although many companies have spent the last several years hardening their static biometric check process, companies are not currently ready for attackers who can now stream high fidelity AI generated personas into their web browser’s camera input. The “substitution” of the original, trusted liveness detection systems by mimicking the exact micro-expressions and eye movements of a legitimate user, poses a significant risk to the integrity of visual verification methods.
There has also been a shift from traditional fraud, which comprised of static stolen credentials for victimized users, to synthetic identities (i.e. created fraudsters) that do not have legitimate victim users to report the fraud. According to research by Gartner, they expect that by the year 2026 facial biometrics will cease to be standalone user verification methods because of the accessibility of deepfakes as well as the capability of being successfully generated at human-like quality. The biggest risk to enterprise systems as a result of this type of attack, is the ability for entitites to automate the types of network attacks being used to create accounts automatically. A single botnet can fill thousands of accounts at once and can create work in a way that is impossible to distinguish between human vs high resolution generative model accounts in real-time by any manual/evaluative review teams.
Enterprise architect’s primary challenge is that the visual trust layer is irreparably broken. Moving to a zero-trust identity model will require significant organisation-wide changes to how end-user identification occurs and will require changing the way in which we evaluate user device telemetry signals and behavioural signals rather than what is seen on the visual trust layer on a physical or, in this case, electronic screen. Leadership teams remain challenged with how to begin to implement these significant changes across the organisation. as visual trust has been seen as the most vulnerable area of risk in the fintech ecosystem.”
- Sudhanshu Dubey, Delivery Manager and Enterprise Solutions Architect, Errna
Synthetic identities that play the long game
Deepfakes get the headlines, but synthetic identity fraud might be the harder problem to solve. This is where fraudsters blend real stolen data with fake details to build entirely new people. These fake identities open accounts, make small transactions, build credit over months, and then cash out big.
McKinsey calls synthetic identity fraud the fastest-growing type of financial crime. TransUnion reported $3.3 billion in US lender exposure to suspected synthetic identities by mid-2025. The Federal Reserve Bank of Boston confirmed that generative AI is speeding up the process, making these identities harder to spot and faster to create.
The core issue is that nobody reports the fraud. With traditional identity theft, the real person notices and flags it. With synthetic identities, there is no real person. The identity was built from scratch. Legacy KYC systems confirm that an identity exists. They do not confirm that it is real.
“The biggest threat we face today is synthetic identity fraud. These fraudsters can create profiles that look legitimate and pass KYC checks. They then build trust over months by engaging in low-risk activities. By the time they make high-value transfers, the account has already earned favorable limits.
One key issue is that teams tend to over-invest in point-in-time checks. At the same time, they under-invest in continuous verification. It is important to link risk to behavior over time and treat onboarding as just the beginning. Look out for subtle signals like device rotation, unrealistic income patterns and consistent documentation.”
- Christopher Pappas, Founder, eLearning Industry Inc
This problem is showing up across all kinds of platforms, not just banks and lenders. Any company running cross-border payments and identity checks is a target.
“One of the growing risks across fintech today is the rise of synthetic freelancer profiles. We operate across multiple countries, and the quality of forged identities seen across the industry has changed significantly in the last 12 months.
With AI tools, it has become possible to generate documents that look perfectly legitimate (IDs, invoices, proof of address) all consistent and professionally structured within minutes.
The old playbook was to catch bad documents at sign-up. That is no longer sufficient when documents appear flawless. What matters today is what happens after onboarding: how someone invoices, how often bank details change, whether activity patterns reflect real freelance work.
If compliance stops at the front door, fraud can still walk straight through it.”
Hasan Can Soygök, Founder, Remotify.co
Attackers are reverse-engineering fraud detection
There is another layer to this that gets less attention. Attackers are not just trying to fool identity checks. They are studying the fraud detection models themselves. They run small test transactions to figure out what gets flagged and what does not. Once they understand the rules, they craft activity that stays just inside the safe zone.
This is called adversarial manipulation, and it is cheap to do at scale with automation. ISACA warned in 2025 that this is a growing but overlooked threat in financial services. The problem is that most fraud models are trained on historical data. They know what past fraud looked like. They do not know what fraud looks like when it is specifically designed to avoid them.
“The biggest AI-powered threat in 2026 is the manipulation of fraud models by attackers. They will run many small, low-value tests to learn how the model works and then craft transactions that avoid detection. It’s similar to lock-picking for fraud detection systems, and it can be done cheaply with automation. Fintechs must assume their models are being studied and build defenses accordingly.
To defend against this, fintechs should rate-limit retries and treat repeated near-miss events as suspicious. They can add dynamic thresholds that adjust based on context, rather than fixed rules. Monitoring probing behavior, like small transfers to many recipients, is essential. Keeping a human feedback loop that can quickly adapt features and policies is key for resilience and fast response.”
Sahil Kakkar, CEO / Founder, RankWatch
AI fraud that looks like a real customer
The most concerning development might be the one that is hardest to detect. AI systems can now simulate entire customer journeys. Not just a fake document or a single fraudulent transaction, but months of realistic financial behaviour. Transaction histories, digital footprints, engagement patterns across multiple channels. All generated to look like a real person living a real financial life.
Experian’s 2026 Fraud Forecast flagged agentic AI as a top threat, warning that AI-powered bots will carry out complex scams without a human behind the keyboard. Deloitte projects that GenAI-enabled fraud losses will jump from $12.3 billion in 2023 to $40 billion by 2027. And only around 22-25% of financial institutions have implemented adequate AI-based fraud prevention tools to fight back.
“The graver risk isn’t simply synthetic identity fraud by itself, but the existence of AI systems that are able to model plausible financial behavior not in just one dimension, but over time. This means in 2026 we will face fraud that does not resemble a fake document, but is a real customer’s journey with history of transactions, a digital footprint and engagement patterns that look genuine across channels.
What makes this so dangerous is that a large number of fintech risk models are trained on historical fraud signatures. Agentic AI can respond, intuit what detection looks like in real-time and adjust behaviour to remain within a detectable spectrum of usage. Where fraud has become adaptive rather than a one off, rule base defence cannot keep up.
The most vulnerable fintechs will be those that are too dependent on automation but lack robust anomaly detection throughout behavioral, network and contextual data. The next iteration of resilience will be introduced via layered intelligence: machine detection combined with human judgement and cross ecosystem verification that does not debind the concept of clean looking data with being low risk.”
Mada Seghete, Co-founder, CEO and Marketing, Upside.tech
Where this leaves fintechs
The pattern across all five responses is clear. Every single defence can be beaten in isolation. Deepfakes beat biometric checks. Synthetic identities beat document verification. Adversarial probing beats ML models. Agentic AI beats behavioural baselines by mimicking real patterns over time.
The answer is not one better tool. It is layers. Continuous behavioural monitoring instead of one-time checks at onboarding. Device telemetry and session signals alongside visual verification. Dynamic risk thresholds that adapt instead of static rules that attackers can learn. Human review teams plugged into the loop where machines fall short. Cross-platform data sharing so fraud caught at one company does not just walk over to the next.
Fintech companies still treating identity verification as a checkbox at sign-up are the ones most exposed. The attackers have moved to AI. The defences need to catch up. Fast.