{"id":161040,"date":"2026-09-07T12:48:41","date_gmt":"2026-09-07T09:48:41","guid":{"rendered":"https:\/\/ondato.com\/?p=161040"},"modified":"2026-09-07T12:55:12","modified_gmt":"2026-09-07T09:55:12","slug":"fraud-trends-in-identity-verification","status":"publish","type":"post","link":"https:\/\/ondato.com\/fr\/blog\/fraud-trends-in-identity-verification\/","title":{"rendered":"Emerging Fraud Trends in Identity Verification: What You Need to Know"},"content":{"rendered":"\n

Identity fraud is not a new phenomenon, but the tools behind it are changing fast. <\/p>\n\n\n\n

Generative AI is making it easier to create convincing fake identities, manipulate identity evidence, and attack remote verification processes at scale.<\/p>\n\n\n\n

This article analyzes the top five emerging fraud trends that are shaping identity verification and what businesses should be prepared for next.<\/p>\n\n\n\n

1. Synthetic Identities Are Becoming More Convincing<\/h3>\n\n\n\n

Synthetic identity fraud<\/a>\u00a0is a tricky one to battle, as it is not limited to completely invented people. Fraudsters can\u00a0combine genuine personal information with fabricated details, creating an identity that looks legitimate when individual elements are checked separately.<\/p>\n\n\n\n

As if that wasn\u2019t bad enough, generative AI adds another layer to the problem. The\u00a0UK government’s identity-checking guidance<\/a>\u00a0notes that\u00a0synthetic identities can be partly genuine, while fake attributes can also be created using AI.<\/p>\n\n\n\n

For identity verification, this means checking whether individual details appear valid may not be enough. Organizations also need to consider whether those details belong to a consistent, credible identity and whether there are signs that the identity has been assembled from real and fabricated information.<\/p>\n\n\n\n

2. AI Is Making Fraudulent Identity Documents Easier to Create<\/h3>\n\n\n\n

Manipulated or\u00a0fake identity documents<\/a>\u00a0are not new, but today they’re becoming much easier to create. The technology available to fraudsters and the expertise needed\u00a0to create convincing identity evidence have changed dramatically.\u00a0<\/p>\n\n\n\n

For example, the UK’s\u00a0National Document Fraud Unit<\/a>\u00a0distinguishes between counterfeit documents created from scratch and genuine documents that have been forged by altering details, pages, or photographs.\u00a0For remote identity verification, this means document checks need to establish more than whether the information presented looks plausible.<\/p>\n\n\n\n

This makes\u00a0document verification<\/a>\u00a0one part of the verification decision rather than proof on its own. The document needs to be assessed alongside the person presenting it and other available identity signals.<\/p>\n\n\n\n

3. Deepfakes Are Putting Biometric Verification Under Pressure<\/h3>\n\n\n\n

Fraudsters also use another way to\u00a0imitate the person whose identity they are trying to claim \u2013 and that\u2019s via\u00a0deepfakes<\/a>. Images, video, and audio can all be manipulated to make an impersonation more convincing during a remote verification process.<\/p>\n\n\n\n

For example, the\u00a0UK National Assessment Centre’s fraud assessment<\/a>\u00a0says criminals are now using generative AI technologies to manufacture deepfakes and clone voices. The study also notes that AI-generated text, audio, images, and video are being used to support fraud.<\/p>\n\n\n\n

That\u2019s why, for\u00a0biometric verification<\/a>, the challenge is not just whether a face matches an identity document, but also to determine whether the biometric input comes from a\u00a0real person present during verification<\/em>. To determine the latter, organizations use\u00a0liveness detection<\/a>.\u00a0<\/p>\n\n\n\n

4. Injection Attacks Are Targeting the Capture Process<\/h3>\n\n\n\n
\"Normal<\/figure>\n\n\n\n

Not every biometric attack needs to fool a camera. Injection attacks target the verification process itself by feeding manipulated biometric data directly into the system rather than presenting it physically to the capture device.<\/p>\n\n\n\n

For example,\u00a0ENISA’s research<\/a>\u00a0on remote identity proofing\u00a0identifies biometric injection attacks as distinct from presentation attacks, where\u00a0artificial biometric data can be injected directly into the recognition system to bypass controls applied during capture.<\/p>\n\n\n\n

This means that\u00a0a system can detect a fake face shown to a camera and still be vulnerable to manipulated input injected elsewhere in the verification flow. Therefore,\u00a0identity verification<\/a>\u00a0needs to consider both the biometric itself and the integrity of the capture process.<\/p>\n\n\n\n

5. Identity Fraud Is Becoming Easier to Automate and Scale<\/h3>\n\n\n\n

Apart from improving the quality of individual fraud attempts, AI can also reduce the effort required to repeat them. Fraudsters can generate identity material, manipulate biometric data, and automate impersonation across a much larger number of attempts.<\/p>\n\n\n\n

For example, a\u00a0UK government consultation on digital identity<\/a>\u00a0warns that AI can be used to generate convincing fake identities, manipulate biometric data, and\u00a0automate impersonation at scale.<\/p>\n\n\n\n

For identity verification,\u00a0patterns across attempts become increasingly important. One application may appear plausible in isolation, while repeated identities, devices, behaviors, or other signals can reveal a broader fraud operation.\u00a0That\u2019s why\u00a0identity spoofing<\/a>\u00a0(impersonation) needs to be considered across the verification journey rather than through one check alone.<\/p>\n\n\n\n