MouseCat is an AI-powered fraud investigation platform designed to emulate the analytical capabilities of top human analysts. It automates the research process into users and businesses, reasons across disparate data sources, and provides complete audit logs with explainable decisions. By transforming investigative findings into actionable features, rules, and improved decision-making processes, MouseCat enhances the efficiency and effectiveness of risk teams. Key Features and Functionality: - Automated Research: Conducts in-depth investigations into users and businesses, automating the research process to scale manual fraud investigations. - Data Integration: Connects to platforms like Databricks and Snowflake, working seamlessly with in-house rule engines and feature stores to understand existing risk signals. - Feature Engineering: Extracts intelligent features from unstructured data, enabling the automatic exploration and backtesting of new features and rules. - Synthetic Label Generation: Creates synthetic labels to identify fraudulent activities before ground-truth labels are available, aiding in proactive fraud detection. - Explainable Decisions: Provides complete audit logs and deterministic back-tests, ensuring transparency and accountability in decision-making processes. Primary Value and Problem Solved: MouseCat addresses the limitations of traditional AI tools that often stop at summarizing alerts and providing recommendations. By closing the loop from investigation to production rules, models, and decisions, MouseCat enables risk teams to take end-to-end actions. It automates manual fraud investigations, reduces the need for manual reviews, and accelerates fraud mitigation efforts. This comprehensive approach allows organizations to proactively discover and address anomalies, generate high-precision rules, and build better models, ultimately enhancing their fraud detection and prevention capabilities.