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Fraud Detection Savings Calculator

Work out fraud detection savings instantly with clear inputs, formula shown and shareable results.

About Fraud Detection Savings Calculator

Fraud exposure is transaction volume times fraud rate times average loss, and a detection system converts a share of that into prevented loss. The residual is what still gets through. What this calculation deliberately omits is the cost of false positives — declined legitimate transactions and manual review effort — which often exceeds the marginal fraud saved once detection is already above 80 percent.

Formula

Fraud economics

fraudulent = transactions x fraud rate; exposure = fraudulent x average loss; prevented = exposure x detection rate

Worked example

With the default values below, Fraud Detection Savings Calculator returns Annual loss prevented of $1,082,400.00. Change any input above and every figure here is recalculated in your browser.

Inputs used

Transactions per yearminimum 1
4,000,000
Fraud rateaccepts 0–100 %
0.15 %
Average loss per fraudulent transactionminimum 0
220
Share of fraud detected and stoppedaccepts 0–100 %
82 %

Results

Annual loss prevented
$1,082,400.00
Fraudulent transactions per year
6,000
Gross fraud exposure
$1,320,000.00
Residual loss after detection
$237,600.00

Step by step

  1. 1Values used
  2. 2Fraud economics
  3. 3Annual loss prevented= 1,082,400.00
  4. 4Fraudulent transactions per year= 6,000
  5. 5Gross fraud exposure= 1,320,000.00
  6. 6Residual loss after detection= 237,600.00

Assumptions and limits

  • The result is derived only from the 4 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

Why can pushing detection higher lose money?

Raising recall on a rare class costs precision fast. Each extra percentage point of fraud caught can decline hundreds of good transactions, and lost customer lifetime value dwarfs the recovered amount.

What else belongs in the business case?

Chargeback fees, manual review labour, and the cost of customer friction such as step-up authentication. Prevented loss alone overstates the net benefit.

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