Unit21 provides AI risk infrastructure that unifies fraud detection, anti-money-laundering (AML) monitoring, investigation, and decisioning into a single platform built for the people who actually run risk operations: compliance analysts, fraud investigators, and AML teams. Its defining feature is a no-code, flag-and-review toolset that lets these non-technical operators write complex statistical detection models and deploy customized workflows without depending on engineering, removing a chronic bottleneck where risk teams wait weeks for engineers to ship new rules.
The platform spans the full risk lifecycle. Detection engines flag suspicious transactions and behaviors; investigation tooling gives analysts a unified case-management view to research and resolve alerts; and decisioning workflows let teams codify how flagged activity is handled and escalated. By consolidating these functions, Unit21 replaces a patchwork of point tools and manual spreadsheets with one intelligent system, while preserving the transparency and auditability regulators require.
Unit21's customer base reflects strong trust from regulated, high-volume operators: more than 200 institutions including Intuit, Chime, and Sallie Mae rely on it for fraud and compliance. These are exactly the kinds of customers, fast-growing fintechs and established financial institutions, whose risk teams are overwhelmed by alert volume and need to move faster without sacrificing control.
The company was founded in 2018 by Trisha Kothari (CEO), a former Affirm product manager, and Clarence Chio, with the explicit goal of giving risk, compliance, and fraud teams a secure, integrated, no-code platform. Unit21 has raised roughly $92M across three rounds from 27 investors. Its $34M Series B was led by Tiger Global, with participation from ICONIQ Capital and existing backers Gradient Ventures (Google's AI fund), A.Capital, and South Park Commons; angel investors include founders of Plaid, Chime, and Shape Security.
Unit21 continues to push toward more agentic automation of fraud and AML operations, fitting an industry-wide shift in which AI handles routine detection and investigation while human experts focus on the genuinely complex cases.