We propose recasting compliance state degradation in regulated industries as a time-to-event problem amenable to the established statistical machinery of hazard modelling and survival analysis. The framework combines longitudinal records of per-dimension compliance state estimates, entity-level behavioural and transactional signals, and federated population-level patterns to produce calibrated predictions of state-transition hazard. The framework supports per-dimension baseline hazards with time-varying components and discrete external-shock terms; entity-level covariate functions parameterised as Cox proportional-hazards models, deep-survival neural networks, or gradient-boosted survival ensembles; and federation-level patterns inferred under privacy-preserving differentially-private aggregation. Alert thresholds are calibrated against verifier-specified target false-positive rates with periodic recalibration as population distributions shift. We analyse the theoretical properties, discuss applications to financial-services AML, right-to-work assurance, professional-licensing oversight, and continuous-audit settings, and identify the principal limitations. Companion preprint to UK Patent Application GB2611285.4 filed at the UK Intellectual Property Office on 14 May 2026.
Oyelokiki George Egbedayo (Fri,) studied this question.