The Cost of Deepfake Fraud for Enterprises: Which Costs to Include

Which costs should enterprises include when assessing deepfake fraud?

Count every cost the incident creates, not only the money that left the account. A deepfake fraud usually triggers an investigation, legal review, possible regulatory reports, attempts to recover funds, customer contact and remediation, and changes to controls afterwards. Each of those consumes staff time and sometimes outside fees, and most of them are booked to departments other than the one that lost the money.

The table lists the categories to include. Use it to gather costs from past incidents and to estimate the exposure of a plausible future one.

How much money is lost to deepfake fraud?

No official statistic isolates deepfake losses yet, because most reporting systems record the fraud type (business email compromise, identity fraud, investment scams) rather than the tool used. The available figures show the size of the categories deepfakes feed into. The FBI's Internet Crime Complaint Center recorded a record $16.6 billion in reported losses for 2024 across 859,532 complaints, a 33 percent increase on 2023, with business email compromise alone accounting for $2.77 billion.

Forecasts point upward. Deloitte's Center for Financial Services estimated in May 2024 that generative AI could take US fraud losses from $12.3 billion in 2023 to about $40 billion by 2027, a compound annual growth rate of 32 percent. FinCEN's alert FIN-2024-Alert004 of November 2024 reported a rise in suspicious activity reports describing deepfake media used against financial institutions.

Single incidents can be large. In early 2024, an employee in the Hong Kong office of engineering firm Arup joined a video call in which the CFO and colleagues were deepfakes, and made 15 transfers totalling HK$200 million, about US$25 million.

The hidden costs that incident totals miss

Investigation time is the largest hidden cost in many incidents. Analysts have to establish what happened, whether the media was synthetic, which accounts and people were involved, and what evidence will stand up with law enforcement, insurers or a court. Without forensic tooling, deciding whether a voice note or video is synthetic can take days and outside experts.

Customer and reputational costs follow when a customer's identity was used, when a fake executive video circulates publicly, or when a synthetic account was used to defraud others. Those costs land months later and are rarely linked back to the original incident in reporting.

Regulatory costs depend on sector. Financial institutions may need to file suspicious activity reports, and FinCEN asks filers to use the key term FIN-2024-DEEPFAKEFRAUD for related activity. Each report means analyst and compliance time.

Staff time is the thread through all of these. Ask each team involved in a past incident to estimate the hours it spent, multiply by loaded hourly cost, and add outside fees. The total is usually a surprise.

How to estimate your own exposure

Work from scenarios rather than averages. Pick three plausible incidents for your business, for example a synthetic identity used to open accounts at scale, a cloned executive voice authorising a payment, and a manipulated claim photo. For each, estimate frequency per year and cost per incident using the categories above, with low and high values.

Ground the frequencies in your own data where possible: past cases, near misses, and fraud that was caught late. Where you have none, use public evidence to set a range and be explicit that it is an estimate. Repeat the exercise each year, since attack volume and tool quality change quickly.

Present the result as expected annual cost (frequency times cost per incident, summed across scenarios) alongside the single worst plausible incident. Finance teams use the first for budgeting and boards use the second to judge whether the risk is tolerable. Keep the working visible, with each figure tied to its source, so the estimate can be challenged and improved rather than accepted or dismissed whole.

Where detection reduces these costs and where it does not

Detection cuts cost in two places. Before the loss, it flags synthetic media in onboarding, payments or claims so the transaction can be stopped or reviewed. After the loss, it shortens investigation by establishing quickly whether media was manipulated and producing evidence others can rely on. Fast checks also limit friction costs, because legitimate customers are not kept waiting.

Detection does not remove the need for process controls. The Arup transfers were authorised by a person on a call; callback verification on a known number and dual approval for unusual payments would have added independent checks. Detection is one signal, and payment and approval policies decide what happens next.

How Neural Defend lowers the cost of deepfake incidents

NeuroDetect is built for the investigation side. Analysts upload authorized images, audio, video or documents into a case, review explainability heatmaps and attention views down to regions, frames and audio segments, get an LLM-assisted plain-language summary linked to the evidence, and download a forensic report covering observations, uncertainty, provenance and model context. The analyst makes the final call, with a record that legal, insurers and law enforcement can follow.

For prevention, NeuroVerify screens face images, video and audio in one API call with six signals, completing eligible image requests in under 200 ms at p95 under defined test conditions, and NeuroCall, in Beta, addresses voice calls. Neural Defend ranked first of 36 systems on the public VendorBench-100 image benchmark, with an MCC of 0.876; one of its authors also co-authored our ARCAS 1B paper, and the corpus is public.