AI Fraud Detection Savings Calculator
Calculate the net savings from AI-driven fraud detection (Sift, Riskified, Stripe Radar, Forter, Signifyd, Kount) — fraud rate times average transaction value times catch rate improvement, minus false-positive cost from rejected legitimate transactions and platform fees.
The Fraud Detection Trade-off
Fraud detection has two costs: missed fraud (false negatives — fraudsters succeed) and rejected good customers (false positives — legitimate buyers blocked). AI fraud detection improves both: 30-60 percent more fraud caught (Sift, Riskified case studies 2024-2025) and 20-40 percent fewer false positives versus rule-based systems. The combined ROI is real but requires careful measurement of both sides.
Net Savings Formula
Fraud Caught Savings = Total Volume × Fraud Rate × AOV × Additional Catch %
False Positive Cost = Volume × FP Rate × AOV (lost transaction)
Net = Fraud Savings - FP Cost - Platform Fee
Vendor Pricing as of 2026
Stripe Radar (basic, included): free with Stripe payments. Stripe Radar for Teams: 0.05 percent of charge volume. Sift: USD 8-15K/month base plus per-transaction cost. Riskified: 0.5-1.2 percent of approved transaction value (guaranteed chargebacks). Forter: similar guaranteed model. Signifyd: 0.4-1.0 percent of order value (chargeback guarantee). Kount: USD 0.10-0.50 per transaction screened.
Guaranteed vs Screening Models
Guaranteed models (Riskified, Forter, Signifyd) charge 0.4-1.2 percent of approved value but absorb all chargeback losses. Screening models (Sift, Kount, Stripe Radar) charge less per transaction but you eat losses on missed fraud. Guaranteed wins for high-fraud verticals (digital goods, travel, marketplaces). Screening wins for low-fraud retail with strong existing fraud teams. Run the math both ways before committing.
Building the Business Case
You need three numbers from your data: current fraud rate (chargeback rate plus refund-for-fraud rate), average ticket value, and current false-positive rate. Run a 60-day pilot with the new vendor in shadow mode (their score, your decision) to measure incremental catch and false-positive change. Net annual savings of USD 100K-5M is realistic for mid-market e-commerce; less for very-low-fraud verticals (under 0.1 percent base rate).
Sources: Sift Fraud Industry Report 2025, Riskified State of Fraud Report 2024, LexisNexis True Cost of Fraud Study 2024, Stripe Radar Documentation 2026. Last updated: April 2026.
Frequently Asked Questions
Guaranteed or screening model?
Guaranteed (Riskified, Forter, Signifyd) wins for high-fraud verticals (digital goods, travel, marketplaces above 1 percent fraud rate). Screening (Sift, Kount, Stripe Radar) wins for retail under 0.5 percent fraud where you have a fraud ops team. Run both pricing models against your data.
What false-positive rate should I target?
Under 1.5 percent is the modern target. Above 2.5 percent, you are losing serious customer revenue. AI systems typically deliver 0.5-1.5 percent FP rate with adequate training data. Always measure FP rate in shadow mode before flipping decision authority to the AI.
Does AI handle account takeover (ATO) fraud?
Yes — most modern AI fraud systems cover transaction fraud, ATO, promotion abuse, fake account creation, and refund fraud in a unified model. Older rule-based systems often need separate tools per category. Bundling in AI is a cost-saver.
How long does the model need to train?
30-90 days of your transaction data minimum for reasonable accuracy. Vendor models use cross-merchant signal (you benefit from their network), so cold-start is faster than building in-house. After 90 days, accuracy converges within 5 percent of mature steady-state.
What about regulatory implications?
EU PSD3, US state fair-lending laws, and consumer credit regulations require explainability for fraud decisions that affect customers. Modern AI fraud vendors provide reason codes for decisions. Always retain audit logs for at least 24 months for regulatory review.
Is this tool private?
Yes. All calculations happen in your browser. Transaction volumes, fraud rates, and platform pricing are never sent, stored, or shared.