Roe AI

Roe is an agentic investigation platform for fraud and anti-money laundering teams. Its AI agents investigate every fraud, dispute and AML case from start to finish, produce cited evidence and hand the analyst a finished case to review. Roe says it runs inside card networks, buy now pay later providers, marketplaces and crypto-native compliance teams, where alert volumes and regulatory stakes are highest.

The problem

Roe argues that risk teams are stuck between two bad options. Hiring and outsourcing makes costs climb and quality drift. Leaning on rules and models lets new fraud patterns slip through. Neither approach scales with a growing queue of alerts.

How it works

Roe is a layer on top of your existing stack, not a replacement. It reads data where it already lives and posts results back where your team works. It connects to risk data providers for sanctions, politically exposed persons, adverse media, KYC and KYB, business filings and credit signals. It reads customer, transaction and case data from data warehouses. It posts cited dispositions and narratives ready for suspicious activity reports back into your case manager. It joins CRM records, internal apps and operator workflows into the same case file, and drafts customer responses and handles appeals in your support inbox. For systems without an API, Roe runs as a browser agent on legacy consoles, partner portals and internal tools.

What customers say

A senior compliance director at Dutchie says Roe was a quick way to start without changing how the team already works. A risk manager at Affirm calls it essential for complex risk workflows, and a Crossmint engineer says cost is down, accuracy is up and turnaround is faster. The Nilson Report noted that Roe's analysis details the full trace of what was checked and found.

Pricing

Roe is sold to businesses; you request a demo for pricing.

Who it suits

Roe suits fraud, disputes and AML compliance teams at fintechs, banks, credit unions, marketplaces and crypto firms.

Things to consider

Regulators expect human accountability, so analysts should review every disposition. Check data handling, model governance and audit requirements in your jurisdiction before deployment.

A pilot on one alert type is a sensible start.