Think of Apate as a digital fraud detective that never sleeps. It watches transactions, behaviors, and case data across government programs, looking for suspicious patterns and alerting investigators before money is lost.
Fraud investigation in the public sector is heavily manual and slow, leading to high losses, delayed interventions, and overworked investigation teams. Apate aims to automatically detect, prioritize, and help investigate fraud cases across public-sector programs using AI.
Domain-specific fraud patterns and labeled case data from public-sector deployments, coupled with integration into government workflows, can create a data and workflow moat over time.
Hybrid
Feature Store
High (Custom Models/Infra)
Model training and monitoring on large, highly imbalanced datasets with strict data-governance and privacy constraints; plus latency and throughput requirements for real-time fraud scoring.
Early Majority
Positioned as an AI-native fraud and intelligence platform specifically targeting public-sector fraud (benefits, tax, public services) rather than generic financial services; likely emphasizes modern ML/LLMs and investigator-friendly intelligence interfaces over legacy rules engines.
15 use cases in this application