Problem

Fraud teams typically combine rules, traditional machine-learning models, and manual review. These systems adapt slowly as fraudsters change tactics, focus mainly on point-in-time transaction decisions rather than continuous risk analysis, and can generate costly false positives that lose legitimate revenue. At the same time, AI is making fraud easier to produce, while operations teams cannot scale analyst headcount tenfold to review every suspicious case. The killer use case is therefore investigating more transactions—especially emerging fraud patterns—without proportionally expanding a human fraud-operations team.

The economics are both loss prevention and labor leverage: missed fraud creates direct financial losses, while false positives and repetitive manual investigations damage conversion and consume skilled staff. MouseCat says its agents can uncover high-confidence false positives that affect top-line revenue, and reports that a private-beta crypto-exchange deployment prevented $1.5 million per month in fraud losses.

Product / Service

MouseCat is an agentic-AI platform for risk operations. Its agents investigate cases in a human-like way by reviewing internal case information, querying databases and APIs, using prior cases for context, and distilling the evidence needed for a decision. The platform can run on existing investigation queues or continuously in the background, allowing companies to identify fraudulent activity weeks before disputes arrive.

The delivery model is customer-controlled and security-oriented: MouseCat deploys inside the customer’s cloud environment and integrates with the customer’s data, vendors, and APIs. It learns from historical case notes, analyst feedback, and ground-truth labels such as chargebacks; those inputs support evaluation and ongoing improvement. The company also handles the operational infrastructure around agents, including integration, evaluation, monitoring, tuning, and retries, while analysts can contribute feedback or build agents on the framework.

Market

MouseCat competes in agentic AI infrastructure for fraud investigation and broader risk operations, overlapping with fraud detection, AML, identity, and case-management platforms. The adjacent competitive set includes Sardine’s agentic financial-crime platform, Unit21’s configurable fraud-detection and AML tooling, Sift’s fraud-prevention and investigation capabilities, and Persona’s identity and fraud-prevention platform. MouseCat’s differentiation is less a conventional point-in-time scoring model than an always-on investigation layer that uses customer-specific data and feedback to automate analyst-quality work and improve detection over time.

The company appears to be at an early commercialization stage rather than a mature, broadly scaled vendor: its own site describes results from a private beta at a crypto exchange, and MouseCat is identified as a YC W26 company backed by Y Combinator. Third-party funding profiles report approximately $500,000 raised. No public revenue, pricing, or customer-count disclosure was found in the gathered evidence, so the strongest visible traction is the reported beta outcome—$1.5 million per month in prevented fraud losses—rather than evidence of established recurring revenue.