Calculating Fraud-Prevention ROI

Fraud prevention feels obviously worthwhile — until finance asks, "what's the return?" If your answer is "it stops fraud," that's not a number. And the honest truth is that a fraud filter has *costs* too: the subscription, the review labor, and the revenue lost when it blocks real customers. A tool can easily be net-negative if it over-blocks.

This is a simple ROI model you can run in a spreadsheet to know whether your fraud prevention is actually earning its keep — and to size the opportunity before you buy.

The core equation

At its heart:

ROI = (Losses avoided − Costs) ÷ Costs

Where costs include three things people usually forget to add up:

1. The tool subscription
2. The review labor (time spent triaging held orders)
3. The false-positive cost (legitimate revenue lost to over-blocking)

Ignore any of these and your ROI looks better than reality. Include them and you get a number you can defend.

The inputs you need

Gather these for a consistent period (monthly works well):

• Fraud loss without prevention — chargebacks + fees + lost goods you *would* incur unprotected. Estimate from your history before you had a filter, or from industry benchmarks (fraud commonly runs ~0.5-1%+ of revenue).
• Fraud caught — value of fraudulent orders your filter blocked or flagged.
• Chargeback fee — typically $15-$25 per disputed charge, on top of the lost goods.
• Tool cost — your monthly subscription.
• Review time — hours/month spent on the queue × loaded hourly cost.
• False-positive cost — legitimate orders blocked × AOV × your gross margin (you only lose the *margin*, not the full price, on a blocked good order).

The formulas

Losses avoided = (fraud orders caught × AOV) + (chargebacks avoided × chargeback fee)

Total cost = tool subscription + review labor + false-positive cost

Net benefit = losses avoided − total cost

ROI % = net benefit ÷ total cost × 100

A worked example

Take a mid-size store: 3,000 orders/month, $65 AOV, 60% gross margin.

Losses avoided:

• Fraud orders caught: 40/month → 40 × $65 = $2,600
• Chargeback fees avoided: 40 × $20 = $800
• Total losses avoided = $3,400/month

Costs:

• Tool subscription (Enterprise plan): $8.99/month
• Review labor: 6 hours/month × $20/hr = $120
• False positives: say 15 legitimate orders blocked → 15 × $65 × 60% margin = $585 in lost margin
• Total cost = 8.99 + 120 + 585 = $713.99/month

Net benefit = $3,400 − $713.99 = $2,686/month

ROI = $2,686 ÷ $713.99 × 100 ≈ 376%

Even after honestly counting labor and false positives, every dollar spent returns nearly four. And notice the tool subscription is the *smallest* line by far — the real levers are fraud caught and false positives avoided.

What the model reveals

Running the numbers usually surfaces three insights:

1. The subscription is trivial. At $8.99-$16.99/month, the tool cost is noise next to the fraud it prevents. Chasing a cheaper tool is optimizing the wrong line.
2. False positives are the hidden killer. In the example, over-blocking cost $585 — nearly as much as everything else combined. Push that up and ROI collapses fast. This is why measuring and reducing your false-positive rate matters so much.
3. Labor scales with queue size. If your rules dump too many orders into review, labor balloons. Tighter, higher-precision rules protect ROI.

Stress-test with scenarios

Run the model three ways:

• Conservative: assume you catch less fraud and block more legit customers. If ROI is still positive here, you're safe.
• Expected: your best-estimate inputs (the example above).
• Optimistic: best case. Useful for the ceiling, but don't budget on it.

If even the conservative case is positive, prevention is a clear win. If only the optimistic case works, your rules need tuning before the spend is justified.

A copy-paste template

Drop this into a spreadsheet:

INPUTS
Orders/month:              [ ]
AOV ($):                   [ ]
Gross margin (%):          [ ]
Fraud orders caught/mo:    [ ]
Chargeback fee ($):        [ ]
Tool subscription ($/mo):  [ ]
Review hours/mo:           [ ]
Hourly cost ($):           [ ]
Legit orders blocked/mo:   [ ]

CALCULATED
Losses avoided = (fraud caught × AOV) + (fraud caught × chargeback fee)
FP cost        = legit blocked × AOV × margin
Total cost     = subscription + (review hrs × hourly) + FP cost
Net benefit    = losses avoided − total cost
ROI %          = net benefit ÷ total cost × 100

Update the inputs monthly and you'll have a living view of whether prevention is paying off — and hard evidence to keep or adjust your approach.

Don't forget the intangibles

The model above is deliberately conservative because it only counts what's easy to quantify. Fraud prevention also buys you:

• Lower chargeback ratio, which protects your Shopify payments standing
• Less firefighting time for your team
• Fewer angry disputes and the reputation hit they carry

These rarely go in the spreadsheet, but they tilt the real ROI further in prevention's favor.

Bottom line

Fraud prevention isn't a cost center — but only if it's tuned. Count the losses avoided *and* the false-positive cost, and most stores find ROI comfortably in the triple digits. The biggest risk isn't paying for a tool; it's letting an untuned tool over-block.

Want to plug real numbers into this model? Shieldy — Fraud Filter starts free, so you can measure fraud caught before spending a cent — and its plans (see pricing) are a rounding error next to the losses they prevent.