Prevent friendly-fraud chargebacks via ML + automated evidence workflows

Executive Summary

Friendly‑fraud chargebacks are rising: an increasing share of cardholders dispute legitimately fulfilled orders, leaving merchants to absorb lost revenue, interchange fees, and remediation costs. This hits the long tail of online commerce hardest—there are roughly 200 million online merchants and an estimated $45.0B annual addressable spend on dispute prevention and recovery (about $225 per merchant per year) —so the problem is broad, operationally painful, and under‑served by manual processes. You could build an API‑first platform that automates evidence collection and filing using ML to surface the right receipts, device signals, session logs, and order metadata, and LLM‑driven, issuer‑tailored dispute narratives to increase win rates. The product would integrate directly with payment providers (Stripe, Adyen, etc.) to pull canonical signals, auto‑assemble case packs, submit to issuers, and reattempt disputes where evidence improves, while exposing explainable model outputs and a dashboard for human review. Key challenges are access to high‑quality training data, model generalization across issuers and regions, regulatory/privacy constraints, and the operational work of integrations and issuer relationships. This is an attractive moment: friendly‑fraud is growing and advances in ML/LLM and API‑first payments make automated, evidence‑rich workflows feasible, which is reflected in the market and revenue scores (94/100 and 88/100). Competition is medium, so a defensible position is possible by focusing on pipeline quality (canonicalized, verifiable evidence), issuer‑specific narrative templates and transparency, outcome‑linked pricing, and strategic partnerships with PSPs—while being candid that building data access, trust with issuers, and robust compliance will take time and capital.