A financial institution receives a new account application during tax season, when teams are handling more applications and document reviews than usual. The file appears normal: there is a valid‑looking IDs and standard supporting information and documents. At first glance, nothing seems out of place. But the submission is fraudulent, built on documents manipulated or generated by AI, designed to impersonate a legitimate customer and open an account, access financial services, or move funds under a false identity.

How does a submission like this make it through in the first place? The answer lies in the tools fraudsters now use. AI makes it easy to produce fake IDs, supporting documents, and application materials that appear consistent across the file. Documents that might once have raised immediate concerns can now look credible enough to delay suspicion, giving fraudsters a greater chance of impersonating legitimate customers and gaining access to accounts or funds. Consumers are also seeing the effects of AI-generated scams as well: in a 2025 McAfee tax season survey, 55% of respondents said scam attempts look more realistic than in previous years, and 87% said they worry AI is making them harder to identify.

For financial institutions, the challenge is no longer just spotting individual red flags. AI-enabled fraud evolves too quickly for manual review alone to keep up. Staying ahead requires detection systems that are regularly updated to keep pace with evolving generative engines. Here are four ways organizations can strengthen verification and stay resilient against AI fraud.

1) AI detection should be part of identity verification

Financial institutions should strengthen identity verification by using detection systems at the points where identity is being established or confirmed. That includes identifying AI-generated impersonation fraud on identity documentation and using AI-generated and deepfake content detection to assess whether images, video, or audio, are authentic or AI-created. Because these models return clear confidence scores, they can give teams more actionable signals when reviewing identity-related content that may otherwise appear legitimate.

2) Secure communications channels to detect impersonation fraud

Financial institutions should apply stronger detection measures with requests involving wire transfers, payment changes, or other transfers of funds. Additional review steps for phone- or video-based requests, checks to confirm the requester is authorized, and escalation paths for unusual payment instructions can help teams assess these interactions more carefully before action is taken. Building these measures into communication workflows can reduce the risk of impersonation and help prevent malicious actions like wire fraud.

3) Validate document submissions before they move further into review

Tax season can put more pressure on document-heavy processes, making it easier for incomplete, low-quality, or suspicious submissions to slip through manual review processes. Institutions can strengthen review by validating that documents are submitted correctly, including whether they are legible, and by automating review through the extraction of key metadata and tagging content at scale. Tools like Hive’s Vision Language Model, a prompt-based content tagging model, and AutoML, a no-code tool for building custom image classification models, can help teams tailor document validation to their own standards.

4) Screen for voice cloning and AI-enabled vishing attempts

Voice should be treated as a growing fraud vector, especially when a call is used to make a false request sound routine. Financial institutions should screen for voice cloning and flag malicious vishing schemes in areas where voice is used for identity checks, account verification, or transaction approval. The more realistic cloned voices become, the easier it is for a fraudulent call to sound credible, which makes early detection more important.

Preparing for a More Convincing Fraud Landscape

Peak periods like tax season reveal where customer communications, review processes, and service channels are most vulnerable to AI manipulation. Institutions that use these moments to identify weak spots and build more adaptive systems will be better prepared not only for seasonal fraud spikes, but for a landscape where deception is becoming more common year-round.

Hive helps financial services companies proactively detect potential instances of AI-enabled identity and document fraud, as well as streamline workflows. Its enterprise-grade, best-in-class AI models are designed to help institutions strengthen fraud detection and improve efficiency across high-volume review processes. Contact us today to learn more.