Prevent friendly-fraud chargebacks via ML + automated evidence workflows

Prevent friendly-fraud chargebacks via ML + automated evidence workflows targets a $45.0B = 200M online merchants x $225 annual addressable spend on dispute prevention/chargeback recovery total addressable market with medium saturation and a year-over-year growth rate of 18% (fraud prevention & dispute automation segment).

Key trends driving demand: Friendly-fraud surge -- more cardholders dispute legitimate charges, increasing recoverable losses merchants will pay to recover.; API-first payments -- Stripe-like APIs let integrated dispute workflows access receipts, logs, and device signals for automated evidence.; ML/LLM narrative automation -- modern models produce persuasive, issuer-tailored dispute narratives and surface winning evidence.; Issuer automation -- banks use automated routing; merchants need automated, structured packets to keep up and win representments..