What question did this study set out to answer?

The study aims to develop a framework for inferring compliance states using AI and federated learning techniques.

May 16, 2026Open Access

AI-Augmented Compliance State Inference: A Federated Learning Approach

Key Points

The study aims to develop a framework for inferring compliance states using AI and federated learning techniques.
Developed a framework for compliance inference using machine learning and federated training.
Employed differential privacy for cross-organisation decision signal aggregation.
Outlined architectural design and model considerations for continuous compliance monitoring.
Introduced a probabilistic approach for assessing compliance state with high confidence.
Enabled continuous monitoring rather than periodic event-based verification.
Illustrated applications in various regulatory settings including financial services and licensing.

Abstract

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.

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