How to Build a Business Case for Deepfake Detection (with an ROI Method)

Start with the exposure: what deepfakes cost organisations like yours

A business case stands on evidence that the risk is real for you. Start with internal data: confirmed or suspected synthetic identity cases, account takeovers through impersonation, claims with manipulated photos, and the losses and staff hours attached to each. If your teams have not been tagging deepfake cases, a short review of recent fraud files often finds some.

Then add public evidence that shows the trend. FinCEN issued alert FIN-2024-Alert004 in November 2024 after seeing a rise in suspicious activity reports describing deepfake media, especially fake identity documents used to get past verification, and it lists a photo or video flagged by deepfake detection software among its red flags. The FBI's 2024 Internet Crime Report recorded a record $16.6 billion in reported losses across 859,532 complaints. Deloitte's Center for Financial Services projected in May 2024 that generative AI could lift US fraud losses from $12.3 billion in 2023 to about $40 billion by 2027.

Use market figures to explain direction, not to size your own loss. A board will trust a smaller number from your own files over a larger industry estimate.

How should organizations calculate deepfake detection ROI?

Use a plain formula. ROI equals avoided losses plus efficiency savings minus total cost, divided by total cost. Avoided losses are the fraud you expect detection to stop that current controls miss. Efficiency savings come from faster or fewer manual reviews, fewer escalations and less investigation time. Total cost is licence or usage fees plus integration, reviewer time for new flags, evaluation and governance.

The hard input is avoided loss, because undetected fakes leave no record. Estimate it two ways and compare. Top down, take losses from case types deepfakes enable and apply the share your pilot shows detection would have caught. Bottom up, count fakes the detector flags in a shadow-mode pilot that your existing controls passed, and multiply by your average loss per case.

Show a range: low, expected and high. If the case only works in the high scenario, say so and propose a smaller scope.

How should KYC teams measure operational value?

KYC value shows up in the queue as much as in fraud losses. Measure a baseline before the pilot and the same figures during it: share of applications sent to manual review, minutes per review, time from submission to decision, abandonment rate at the verification step, and confirmed fraud per 1,000 approved accounts. A good detector should cut review time on clear cases and raise the catch rate on synthetic ones without pushing abandonment up.

Also count the work you avoid later: account closures, mule investigations, suspicious activity reports, chargebacks and customer remediation tied to accounts that should never have opened. These costs often sit in other teams' budgets, which is why business cases undercount them.

Which costs belong in the business case

Include everything that will appear on someone's budget, or the case will be challenged later. Usage or licence fees at realistic volume, integration engineering, reviewer time for new flags including false positives, ongoing evaluation on your own media, monitoring, governance and vendor management all count. For self-hosted deployments add infrastructure and upgrade work.

Plan for growth in the numbers. Volume rises, new channels such as video calls or voice get added, and the tool needs to scale without a new procurement. Price two or three volume tiers so the board sees how cost moves with the business.

How should a champion present the investment to executives?

Keep it to one page with a decision at the end. Executives want to know the size of the risk, what doing nothing costs, what the proposal costs, how sure you are, and what they are being asked to approve. Lead with your own incidents, then the public trend, then the options considered, including building in-house and doing nothing.

Ask for a pilot, not a programme. A time-boxed shadow-mode pilot with success measures agreed in advance, such as catch rate on confirmed fakes, false positive rate and review minutes, lets the board approve a small sum now and a larger one on evidence. Name the date when the team will return with a scale or stop recommendation.

• The risk: your incidents and losses, then the public trend.
• The cost of doing nothing: expected losses and workload over 12 months.
• The proposal: scope, vendor, deployment and total cost.
• The evidence: benchmark results and what the pilot will prove.
• The decision: pilot budget, success measures and a review date.

Where Neural Defend fits in the business case

For onboarding and KYC, NeuroVerify checks a face image, video or audio file in one API call and returns six signals with a unified risk score and signal-level evidence, in under 200 ms at p95 for eligible image requests under defined test conditions. That speed keeps detection inside the journey, which protects conversion. Integration takes two lines of code and can be live in under 24 hours, so a pilot can start quickly. For fraud and investigation teams, NeuroDetect turns flagged media into a case with explainability views and a downloadable forensic report, which shortens investigation time and supports SAR and legal work.

On evidence, Neural Defend ranked first of 36 systems on the public VendorBench-100 benchmark, with an MCC of 0.876 against 0.646 for the next-best vendor. One VendorBench author also co-authored our ARCAS 1B paper, and the benchmark covers still images only, so the pilot on your own media should carry the decision.