Most business owners massively underestimate fraud losses — because they've never measured them. The number is hidden across line items, support tickets, marketing reports, and chargebacks. Nobody owns it. Nobody sees the total.
This calculator gives you a fast estimate. Spend 5 minutes with it. The result usually surprises people.
Step 1: Pick your category
Fraud math is vertical-specific. Pick yours.
iGaming / online gambling: bonus abuse, multi-accounting, collusion FinTech / lending: account takeover, synthetic identity, loan stacking E-commerce: promo abuse, returns fraud, card-not-present fraud Crypto / Web3: Sybil attacks, airdrop farming, KYC bypass AdTech / publishers: click fraud, impression fraud, conversion fraud SaaS: free tier abuse, trial abuse, account sharing
Step 2: Industry benchmarks
These are conservative estimates from published research and real customer data:
iGaming: → Bonus abuse: 5–15% of bonus budget → Multi-accounting: 15–40% of new signups → Collusion (poker products): 1–3% of revenue → Total impact: 8–20% of revenue
FinTech: → ATO: 0.5–2% of active accounts per month → Synthetic identity (loan products): 5–10% of portfolio → Loan stacking: 2–5% of approved loans → Total impact: 3–10% of revenue
E-commerce: → Promo abuse: 5–10% of discount budget → Returns fraud: 5–10% of total returns → Card-not-present fraud: 0.3–1% of transaction volume → Total impact: 3–8% of revenue
Crypto/Web3: → Airdrop farming: 50–80% of distribution → Sybil attacks: highly variable → Total impact: massive variance, use-case dependent
AdTech: → Click fraud: 15–25% of paid clicks → Impression fraud: 10–20% of impressions → Conversion fraud: 10–30% of affiliate-driven conversions → Total impact: 15–30% of ad spend
SaaS: → Free tier abuse: 20–40% of free signups → Trial abuse: 30–60% of trial signups → Total impact: 5–15% of potential revenue
Step 3: Quick calculation
Take your relevant number from above. Multiply by the conservative end (the lower bound).
Example for an iGaming operator with €4M annual bonus budget: → €4M × 5% (low end) = €200K/year minimum loss → €4M × 15% (high end) = €600K/year realistic loss
Example for a FinTech with $100M loan portfolio: → $100M × 5% synthetic identity exposure = $5M/year potential loss → Even 50% catch rate at device layer = $2.5M/year saved
Example for an e-commerce with $5M/month revenue: → $5M × 5% promo abuse = $250K/month → Annual: $3M lost to promo abuse alone
Step 4: Hidden costs people miss
The numbers above are direct losses. Add the hidden costs:
→ Customer support time on fraud-related tickets (10–20% of total ticket volume) → Chargeback fees ($15–25 per chargeback, plus the lost goods) → Brand damage when fraud victims blame your business → Opportunity cost of fraud-fighting team → Compliance penalties (in FinTech especially)
These hidden costs typically add another 30–50% to the direct loss number.
Step 5: Compare to defense cost
Tracio pricing: → Plus: $99/month = $1,188/year (50K verifications/month) → Business: $499/month = $5,988/year (250K verifications) → Enterprise: custom from there
Realistic catch rate for a properly deployed device intelligence layer: 60–80% of attempts.
Cost-savings ratio examples: → iGaming operator with €400K/year bonus loss: spend €6K, save €280K. Ratio: 47×. → FinTech with $2.5M/year synthetic identity exposure: spend $6K, save $1.5M+. Ratio: 250×. → E-commerce with $3M/year promo abuse: spend $6K, save $1.8M. Ratio: 300×.
These are conservative. Most customers see 100×+ ROI in the first year.
Why most businesses skip this calculation
Three reasons fraud loss stays invisible:
→ It's spread across multiple line items — no single number ever shows up in dashboards → "Fraud" is everyone's problem and nobody's problem (operations blames marketing, marketing blames finance, finance blames compliance) → Teams without dedicated fraud expertise default to assuming "it's under control"
The honest answer for most businesses: it isn't under control. You just haven't measured it.
What to do with the number
If your calculated loss is over $50K/year: → Investment in device intelligence pays for itself in the first quarter → Build a business case, get budget approved, deploy
If between $10K–$50K/year: → Edge case. Worth doing, but ROI is months instead of weeks → Start with free tier (2,500 verifications/month) to validate the calculation
If under $10K/year: → You're either very small or measuring wrong → Most teams underestimate by 5–10×. Re-check with broader categories included.
Bottom line
The cost of inaction is typically 5–10× cost of solution. The question isn't "should we invest in fraud prevention" — it's "how fast can we deploy."
Talk to your team about doing this calculation honestly. If the answer is "we don't actually know," that's the data point worth acting on.
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