Predictive Hazard Modelling of Compliance State Degradation in Regulated Industries
Oyelokiki George Egbedayo · Zenodo (CERN European Organization for Nuclear Research) · 2026
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.