AI Deepfake Detection Startups Find Their First Big Market in Fraud Prevention
Deepfake detection startups are finding early customers in banks, insurers and identity providers seeking to stop AI-powered fraud and impersonation.
The market for detecting AI-generated fakes is taking shape around a clear commercial need: stopping fraud. A map of 42 venture-backed companies in the sector points to banks, insurers and identity-verification providers as early buyers, while partnerships and acquisitions are emerging as routes to scale.
The urgency is no longer theoretical. Fake identities and fabricated media can be used to impersonate customers, manipulate claims or pass off invented sources as real people. OpenAI recently identified seven fake journalists operating out of Iran, including a purported columnist whose work appeared in a small Florida newspaper. For businesses, the larger concern is how cheaply convincing audio, video and identities can now be produced.
Among the companies addressing that problem is Incode, which combines identity verification with detection of deepfakes and synthetic identities. It has more than 75 partners and customers, and says it serves eight of the 10 largest U.S. banks. Equifax and Experian both license its technology. Truepic takes a different angle, verifying photos and video used in insurance and lending claims; its technology is integrated into Verisk’s claims software.
Fraud prevention offers the clearest route to revenue. Since January 2025, the mapped companies have raised about $125 million across 26 deals. Roughly $74 million went to businesses focused on fake voices, identities and related fraud risks. Incode’s $37 million round was the largest. Strategic investment is also signaling interest from financial institutions: Capital One backed GetReal, while BNY backed Reality Defender in 2025.
The appeal for buyers is practical. Banks and insurers already spend heavily on identity checks, claims review and fraud investigations. Detection tools can strengthen those existing workflows rather than require customers to adopt an entirely new category of software. That gives specialist vendors a path to enterprise distribution, but also makes their success dependent on proving accuracy, speed and measurable reductions in fraud losses.
A newer generation of founders is targeting the same problem. Of 11 companies in the group founded since 2023, seven primarily serve banks, insurers or teams concerned with impersonation in calls and video meetings. Neural Defend offers real-time detection of manipulated audio and video for financial firms, while DeepXL sells a detection API to financial services companies and online platforms. Their founders bring experience from fields including cybersecurity, banking, law and engineering.
Distribution is increasingly built into the product, not sold separately. Substack has added a way for readers to scan posts using Pangram’s AI-text detector. Zoom incorporated Pindrop voice-fraud detection into its contact-center software, and FICO added Pindrop to its marketplace. Charm Security has also connected its AI fraud agents directly to Reality Defender’s detection tools, reducing the need for teams to switch between systems.
These integrations point to a likely competitive divide. Detection companies that can deliver reliable results through software already used by customers may gain an advantage over standalone tools. At the same time, platform vendors and established fraud providers can decide whether to partner, license technology or build capabilities themselves. For startups, partnerships can provide reach and credibility, but they may also leave them reliant on larger companies for customer access.
Consolidation is another plausible next step. Equifax already uses Incode within Kount, its acquired fraud platform, and has identified identity and fraud as potential acquisition areas. Smaller specialists serving identity and fraud teams could attract interest from credit bureaus, software providers and financial institutions seeking to fill product gaps quickly. Several startups, including IdentifAI, DeepTrust, Scam AI and AI or Not, may also need fresh funding as they move from product development to broader enterprise deployment.
The market’s growth will depend on whether detection can keep pace with increasingly sophisticated fakes. False positives can disrupt legitimate customers, while missed manipulations undermine trust in the tools themselves. Buyers will also weigh privacy, data handling and the challenge of applying detection consistently across voice, video, images and text.
For now, fraud prevention gives AI-fake detection a defined budget owner and a measurable business case. The next phase will test whether specialist startups can become durable infrastructure providers—or whether their technology will be absorbed into the broader identity, security and financial software platforms that already control customer relationships.
Frequently Asked Questions
Why is fraud prevention emerging as the first major market for deepfake detection?
Banks and insurers already spend on identity checks, claims reviews and fraud investigations, so detection tools can improve workflows they already use. That makes it easier to demonstrate a business case than selling deepfake detection as a completely new software category.
How are deepfakes being used to commit financial fraud?
Fraudsters can use synthetic identities or manipulated audio and video to impersonate customers, influence insurance or lending claims, and deceive staff during calls or video meetings. These tactics can make it harder for organizations to confirm who they are dealing with and whether submitted evidence is genuine.
What should banks and insurers evaluate before adopting a detection tool?
They need evidence that a tool is accurate, fast and effective at reducing fraud losses. It should also fit existing identity, claims or investigation workflows. A detector that produces unreliable results or slows reviews may create extra work rather than improve fraud prevention.
Why do partnerships and integrations matter so much to detection startups?
Integrations put detection into software customers already use, reducing the need to switch systems and helping vendors reach more buyers. They can also lend a startup credibility. However, dependence on a larger partner for distribution may limit the startup’s control over customer access.
Could large companies build or acquire deepfake detection capabilities instead of partnering?
Yes. Established fraud platforms and financial institutions can partner, license technology or develop their own tools. The article also points to acquisition as a plausible way for credit bureaus and software providers to fill product gaps, particularly in identity and fraud prevention.