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.