Three types of generative AI fraud and how to stop them

Generative AI is transforming our world—and fraudsters are taking note. The same technology used to automate tasks and filter spam emails is also fueling a surge in sophisticated scams. With the help of generative AI, fraudsters can scale their attacks faster and even evade traditional detection systems.

According to DataDome’s 2025 Global Bot Security Report, 97% of websites are vulnerable to unwanted bots, agentic AI, and LLM crawlers. Even more concerning, only 2.8% of sites are fully protected, down from 8.4% in 2024. 

The gap is widening between attackers who use AI to automate and personalize fraud, and organizations still relying on static, rule-based fraud defenses. As generative AI becomes more accessible, organizations need to learn how to fight back—and quickly. 

What is generative AI fraud, and how is it changing the fraud landscape?

Generative AI fraud uses tools like deepfakes, voice cloning, and large language models (LLMs) to mimic real people, create fake identities, and manipulate digital systems in real time. From impersonating executives to creating synthetic borrowers, these AI-driven tactics are making fraud more convincing and harder to catch than ever before. 

Unlike traditional fraud, which requires manual effort, AI-powered attacks are often automated, adaptive, and personalized. Large language models can craft convincing customer support messages or loan applications in seconds, while deepfake technology can spoof facial recognition systems or even simulate live video calls. Fraudsters can now generate thousands of believable attempts in less time than it took to research and build just one phishing campaign. 

To counter this, some organizations are leveraging machine learning-based fraud detection models that use dynamic anomaly detection to identify fraud and scams while they're in progress. These advanced systems analyze patterns across data sources to predict risk as it happens—not after the damage is done.

Types of GenAI-powered fraud (with examples)

Generative AI has lowered the barrier to entry for sophisticated fraud attacks. What once required technical skill or insider access can now be done with off-the-shelf tools and a few lines of code. Here are the most common types of AI-powered fraud affecting financial institutions today.

Deepfake fraud

Fraudsters use AI-generated videos and images to impersonate real people, often during identity verification, video-based onboarding, or even job interviews. In one recent case, a financial employee in Hong Kong was tricked into transferring $25 million after deepfake scammers posed as the company’s CFO during a video call.

Voice scams

With just a few seconds of recorded speech, AI can clone someone’s voice to trick friends, family, or employees into transferring money or revealing sensitive information. Voice scams have surged as attackers use cloned audio to impersonate executives or relatives in distress. For example, a scammer might pretend to be a grandchild in need of bail money—and even spoof the phone number to make it look legitimate. 

Synthetic identity fraud

Generative AI can create fake but highly believable identities—complete with photos, documents, and digital histories—to open accounts or apply for credit. These “synthetic” personas blend real and fabricated data, making them difficult to detect with traditional methods.

Why traditional fraud prevention isn’t enough

Traditional fraud prevention tools were built for a different era—one where attacks were slower and easier to recognize. Rule-based systems, static identity checks, and after-the-fact reviews used to be enough to catch most fraud. 

But today’s AI-driven threats move fast and adapt too easily for those defenses to keep up. Generative AI enables fraudsters to personalize attacks, automatically adjusting messages, voices, and visuals to match each target. These systems can test thousands of variations in seconds, quickly learning which strategies will bypass filters or fool human reviewers. What used to take days or weeks can now happen in minutes.

Even more challenging, AI-generated fraud often looks authentic because it can blend real data with fabricated content. Traditional tools that rely on fixed parameters or static blacklists can’t keep pace with a system that constantly learns and evolves. Additionally, manual reviewers can easily miss AI-generated fake driver's licenses and superimposed AI-generated images during selfie verification.

For fintechs, banks, and crypto platforms, the result is clear: fraud prevention stacks need to be upgraded for the modern era. Financial service providers need to incorporate new, diverse data sources and expertly finesse fraud models to stay ahead of evolving threats, including generative AI fraud.

How to detect and prevent generative AI fraud

As fraudsters become smarter, detection can’t rely on fixed rules or manual reviews. Stopping AI-driven scams requires the same level of speed, scale, and intelligence that powers the attacks themselves—and that’s where generative AI fraud detection and prevention comes in.

1. Real-time fraud detection

Traditional systems alert you to red flags after the fact—once a customer reports their account has been compromised, or once money has already been stolen. AI fraud detection tools can spot fraud as it’s happening in real-time by analyzing transactions, logins, and user behavior. Machine learning models can flag anomalies in milliseconds, before accounts are created or funds are moved, helping organizations prevent losses without creating user friction. 

2. Behavioral and device intelligence

AI analyzes how users behave and the devices they use to spot subtle signs of risk. Behavioral analytics can detect when typing speed, cursor movement, or navigation patterns differ from a user’s norm, while device intelligence helps identify cloned environments or suspicious IP changes. These combined signals help detect fraud that would otherwise slip through static rule-based systems.

3. Identity verification with AI

Identity verification has evolved far beyond document uploads. Fraud detection tools can spot generative AI fraud using biometric checks, facial recognition, and liveness testing to confirm a person is real—and not a deepfake or synthetic identity. These systems can also use machine learning to analyze microexpressions, lighting patterns, and even pixel irregularities to determine whether someone is using deepfake videos or fake documents. 

4. AI threat intelligence

Modern AI threat intelligence systems combine signals across institutions, using data from millions of interactions to identify new fraud attributes before they become widespread. By recognizing behaviors linked to previous attacks, organizations can proactively block threats—even when facing novel, AI-generated tactics.

Together, these tools create a layered approach to AI fraud prevention that adapts in real time, reduces false positives, and keeps digital experiences both secure and frictionless.

How Plaid helps stop GenAI fraud

Fighting AI-powered fraud requires more than faster alerts—it demands real-time intelligence built into every user interaction. Plaid brings that intelligence to the forefront with Plaid Protect, an AI-powered fraud intelligence platform with modular tools, such as Plaid Identity Verification, for a comprehensive assessment of fraud risk. 

These configurable tools allow organizations to optimize their fraud risk strategy and stop diverse threats like deepfakes, synthetic identities, and AI-driven account takeovers before they cause harm.

Plaid Protect: Tomorrow’s fraud threats, prevented today

Plaid Protect is a real-time fraud intelligence platform powered by the Plaid network spanning across 7,000+ connected apps to one billion devices. At its core is the Trust Index, an ML-powered fraud model trained on thousands of behavioral and device-level signals to evaluate risk from onboarding through transacting.

Protect provides insights you can’t get anywhere else:

• Fraud network intelligence: Determine the risk of fraud based on network interactions across one billion devices that identify high-risk devices or unusual connection patterns in real time.

• Bank account risk indicators: Assess fraud risk using aggregated historical insights into account history, transactions, and unusual activity patterns.

• Consortium and ATO reports: Surface fraud activity reported by other businesses and directly from consumers affected by credential theft.

Identity Verification: Trusted verification for the AI era

Plaid’s Identity Verification helps verify the identity of users and prevent sophisticated fraudsters by analyzing hundreds of different signals. It uses advanced, trusted identity verification tactics to block deepfake identities, including:

• Biometric liveness checks: Detects deepfakes and spoofed media by analyzing facial motion, lighting, and texture for authenticity.

• Age estimation: Flags major discrepancies between a user’s stated age, their ID document photo, and their selfie, offering an important indicator of potential identity misuse or impersonation. 

• Device signals: Identifies risky devices before onboarding is complete by detecting the use of VPNs, incognito browsers, or repeated sessions from the same device. 

Together, Protect and Identity Verification provide real-time fraud risk scoring, identity verification, and behavioral intelligence to help fintechs, banks, and crypto platforms protect their users with confidence.

What comes next?

As generative AI continues to evolve, so will the tactics fraudsters use to exploit it. Deepfakes will become more realistic, synthetic identities more complex, and AI-driven scams more personalized. Staying ahead requires tools that think and act in real time. 

By combining real-time data, machine learning, and network-wide intelligence, Plaid Protect and Identity Verification give companies the visibility and speed needed to fight back. Together, they help fintechs, banks, and crypto platforms strengthen trust, reduce friction, and protect users across the entire customer journey.

Learn how Plaid Protect can safeguard your users and stop tomorrow’s fraud today.