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Deepfake Risk Calculator

Score synthetic media and voice-clone fraud risk from public exposure, verification workflow, channel and transaction value.

About Deepfake Risk Calculator

Cloning quality is no longer the constraint — a few minutes of public audio is enough — so the risk is almost entirely a process question: can a convincing voice or face alone authorise something consequential. Out-of-band verification to a pre-agreed number with a shared code word is the control that works, because it does not depend on anyone detecting the fake. Dual control and training reduce the residual, and detection tooling is a supplement rather than a defence. The control that stops synthetic-media fraud is the same one that stopped its telephone predecessor: never act on an instruction verified only through the channel it arrived on.

Formula

Deepfake Risk

risk = cloneFeasibility × controlStrength ÷ 10, where cloneFeasibility = mean(public media footprint, channel weakness) and controlStrength is the residual weakness after verification, dual control and training.

cloneFeasibility
How easily a convincing clone can be produced and delivered
controlStrength
Residual weakness of the approval process
expectedLoss
attempts × success probability × transaction value

Worked example

With the default values below, Deepfake Risk Calculator returns Deepfake Fraud Risk of Medium. Change any input above and every figure here is recalculated in your browser.

Inputs used

Public audio and video of key peopleone of Almost none — 2, Occasional conference talks — 5, Regular media and earnings calls — 8, Constant public video presence — 10
Regular media and earnings calls — 8
Channel used for approvalsone of In person or signed workflow only — 2, Email plus phone confirmation — 5, Video call — 7, Voice call or voice note — 9, Chat message — 8
Video call — 7
Out-of-band verification for instructionsone of Callback to a known number plus a code word — 1, Callback to a known number — 3, Reply on the same channel — 8, None — recipient judgement — 10
Reply on the same channel — 8
Value of a single approvable transactionaccepts 0–1,000,000,000 currency
500,000 currency
Dual authorisation on payments and changes
Yes
Staff trained on synthetic-media fraudaccepts 0–100 %
40 %
Synthetic-media detection toolingone of Liveness plus provenance checks in the workflow — 3, Manual review of suspicious media — 7, None — 9
Manual review of suspicious media — 7
Impersonation attempts expected per yearaccepts 0–10,000 attempts/year
6 attempts/year

Results

Deepfake Fraud Risk
Medium
Risk Score
3.88 / 10
Probability One Attempt Succeeds
38.8%
Expected Annual Loss
$1,164,375
Voice or Face Clone Feasibility
7.50 / 10
Highest-Value Control
Require callback to a pre-agreed number plus a code word for any instruction to move money or change details

Step by step

  1. 1Values used
  2. 2Deepfake Risk
  3. 3Deepfake Fraud Risk= Medium
  4. 4Risk Score= 3.88 / 10
  5. 5Probability One Attempt Succeeds= 38.8
  6. 6Expected Annual Loss= 1,164,375
  7. 7Voice or Face Clone Feasibility= 7.50 / 10
  8. 8Highest-Value Control= Require callback to a pre-agreed number plus a code word for any instruction to move money or change details

Assumptions and limits

  • The result is derived only from the 8 inputs listed above. Anything not among them is not part of the calculation.
  • In particular, this calculator does not take inflation, tax or fees or charges into account.
  • Figures are rounded for display to 2 decimal places, so totals built from several rounded parts can differ slightly from the unrounded arithmetic.

How these calculators are built and tested is described on the Methodology page. If a figure looks wrong, please report it.

Frequently Asked Questions

How is Deepfake Risk calculated?

risk = cloneFeasibility × controlStrength ÷ 10, where cloneFeasibility = mean(public media footprint, channel weakness) and controlStrength is the residual weakness after verification, dual control and training. Cloning quality is no longer the constraint — a few minutes of public audio is enough — so the risk is almost entirely a process question: can a convincing voice or face alone authorise something consequential. Out-of-band verification to a pre-agreed number with a shared code word is the control that works, because it does not depend on anyone detecting the fake. Dual control and training reduce the residual, and detection tooling is a supplement rather than a defence.

Why does Deepfake Risk matter?

The control that stops synthetic-media fraud is the same one that stopped its telephone predecessor: never act on an instruction verified only through the channel it arrived on.

What values do I need to enter?

This calculator takes 8 inputs: Public audio and video of key people, Channel used for approvals, Out-of-band verification for instructions, Value of a single approvable transaction, Dual authorisation on payments and changes, Staff trained on synthetic-media fraud, Synthetic-media detection tooling, Impersonation attempts expected per year. The pre-filled defaults are a realistic starting point — replace them with figures from your own environment for a result you can act on.

Can detection tools reliably identify deepfakes?

Not dependably in a live call, and generation improves faster than detection. Treat detection as a useful signal for retrospective investigation, and put the reliance on process controls that do not require judging authenticity.

Why does public exposure matter if anyone's voice can be cloned?

Because quality scales with available material. A few seconds produces something passable on a bad phone line; hours of earnings-call audio produces a clone that survives a video conference with a suspicious CFO.

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