This is like giving fraud investigators a super-smart digital assistant that can scan huge amounts of payments, claims, and case files in real time and yell “this looks suspicious” long before a human could spot the pattern.
Rising AI-enabled fraud (deepfakes, synthetic identities, automated scams) is overwhelming traditional rule-based and manual fraud controls in government programs and financial institutions. The solution uses advanced analytics and AI to detect, investigate, and prevent fraud at scale before money is lost.
Deep domain expertise in fraud and compliance, long-standing access to labeled fraud/non-fraud data with partners such as ACFE members and public agencies, and integration into existing investigation workflows and case management systems make this harder to replicate than a generic AI model.
Classical-ML (Scikit/XGBoost)
Feature Store
High (Custom Models/Infra)
Model retraining cadence and data engineering/feature-store maintenance to keep up with rapidly evolving AI-driven fraud tactics, plus latency and cost of scoring very high transaction volumes in real time.
Early Majority
Positioned as an AI-first evolution of established fraud analytics platforms, combining traditional supervised/unsupervised models with newer AI techniques to counter AI-generated threats (deepfakes, synthetic IDs, automated scams) across both financial services and public-sector benefits programs.