AI-Augmented Compliance State Inference: A Federated Learning Approach

A computer-implemented framework for inferring multidimensional compliance state in regulated industries using machine learning, with federated training across verifier organisations and differentially-private aggregation of cross-organisation decision signals. The framework departs from the dominant transactional verification paradigm by representing compliance as a probabilistic multidimensional state with explicit confidence and temporal decay, enabling continuous compliance monitoring rather than periodic event-bound verification. We discuss the architectural framing, the hybrid model design, federated training procedures with privacy guarantees, calibration considerations, and applications across financial-services AML/KYC, right-to-work assurance, professional-licensing oversight, and continuous-audit settings. The contribution is conceptual and architectural; empirical validation follows in subsequent work in this series. Companion to Patent GB2611279.7.