System and Method for Computer-Implemented Governance-to-Capital Translation and Volatility Compression in Artificial Intelligence Insurance Underwriting

Abstract

A computer-implemented system (100) for real-time governance-to-capital translation and volatility compression in artificial intelligence insurance underwriting. The system (100) receives heterogeneous governance telemetry via a telemetry acquisition interface (110), canonicalises the telemetry within a trusted execution environment (123) into a unified risk data schema (130), and computes, in a Q16.16 fixed-point arithmetic unit (162), a variance reduction scaling factor and an Insurability Resilience Score over a longitudinal sliding window (161). The Insurability Resilience Score is anchored in a SHA-256 hash chain (191) aggregated under a Merkle tree (192) in a cryptographic governance evidence registry (190). A cryptographic actuarial signal gateway (200) generates and transmits a cryptographically signed Dynamic Reserve Adjustment Vector to a remote insurer reserve-allocation system (240). A Governance Trajectory Modelling Engine (170) projects governance trajectories, and a Runtime Insurance Evaluation Module (180) analytically evaluates projected trajectories against machine-readable insurance-policy representations, producing analytical governance-risk evaluation signals only. 2222 20 26 20 38 98 20 M ay 2 02 6 2 0 2 6 2 0 3 8 9 8 2 0 M a y 2 0 2 6 2 2 2 0 2 6 2 0 3 8 9 8 2 0 M a y 2 0 2 6 2 2

Description

APPLICANT AND SOLE INVENTOR John Richard Cosstick 82 Brunswick Road, Mitcham, Victoria 3132, Australia CONVENTION PRIORITY Claimed under section 38 of the Patents Act 1990 (Cth) from Australian provisional patent application AU 2026904725 filed 16 May 2026 The following statement is a full description of the invention, including the best method of performing it known to the applicant. 2026203898   20 May 2026 FIELD OF THE INVENTION

[0001] The present invention relates to computer-implemented systems and methods for translating governance telemetry generated by one or more artificial intelligence systems into machine-readable capital intelligence outputs consumed by insurer, reinsurer, and prudential reserve-allocation systems. More particularly, the invention relates to the compression of governance-state volatility, the generation of insurability indicators, the cryptographically authenticated transmission of dynamic reserve adjustment signals to remote insurer systems, and the analytical evaluation of projected governance trajectories against machine-readable insurancepolicy representations. BACKGROUND OF THE INVENTION

[0002] Professional services firms, financial institutions, and operators of artificial intelligence systems increasingly face an emerging mismatch between the velocity at which artificial intelligence governance telemetry is generated and the cadence at which insurance and reinsurance capital is allocated against the resulting exposures. Conventional insurance underwriting cycles operate on quarterly, annual, or treaty-period bases, whereas the underlying governance posture of an artificial intelligence workflow may deteriorate within hours through subtle losses of oversight discipline, drift of model behaviour, or correlated anomalies across cross-coupled workflows. The mismatch is exacerbated by the recent industry transition from silent-AI coverage to affirmative-AI exclusion and affirmative-AI warranty regimes, in which insurers and reinsurers require continuous, verifiable, and machine-readable evidence of governance posture as a condition of coverage.

[0003] Existing governance, risk, and compliance platforms typically produce point-in-time attestations, periodic audit reports, or compliance dashboards intended for human review. Such outputs are not directly consumable by actuarial and reserve-allocation systems, which require numerically stable, cryptographically authenticated, time-synchronised signals expressed in well-defined units. Where existing systems do emit machine-readable outputs, those outputs frequently lack determinism across heterogeneous source schemas, suffer from floating-point leastsignificant-bit divergence between verifier instances, and are not bound to configuration-versioned evidence registries in a manner sufficient to support actuarial reliance.

[0004] A further limitation of prior systems is the absence of a runtime-aware mapping between governance telemetry and machine-readable representations of active insurance-policy conditions. Where coverage is conditional upon governance obligations, warranty thresholds, exclusion clauses, deductible conditions, or aggregate exposure limits, existing systems do not analytically evaluate projected governance trajectories against those policy conditions to produce forwardlooking insurability evaluations. The consequence is that material changes in insurability are routinely detected only after a claim event has occurred, at which point capital allocation has already been mispriced. 2026203898   20 May 2026

[0005] There is therefore a need for a computer-implemented system that ingests heterogeneous governance telemetry from one or more upstream artificial intelligence governance systems, canonicalises that telemetry into a unified risk data schema, compresses the volatility of stabilised governance-state representations using verifier-independent fixed-point arithmetic, generates insurability indicators and dynamic reserve adjustment signals, anchors those outputs into a cryptographically authenticated evidence registry, transmits cryptographically signed reserveadjustment vectors to remote insurer reserve-allocation systems over a mutually authenticated transport channel, and analytically evaluates projected governance trajectories against machine-readable representations of active insurance-policy conditions.

[0006] References to prior systems, industry forms, regulatory regimes, and academic literature in this background section are descriptive only and are not to be construed as admissions of prior art. OBJECTS OF THE INVENTION

[0007] It is an object of the invention, in at least one preferred embodiment, to provide a computer-implemented system that translates real-time governance telemetry into machine-readable capital intelligence outputs consumable by insurer and reinsurer reserve-allocation systems.

[0008] It is a further object of the invention, in at least one preferred embodiment, to compress the volatility of governance-state representations through application of verifier-independent fixed-point arithmetic and statistical stabilisation operations, so as to produce numerically stable insurability indicators.

[0009] It is a further object of the invention, in at least one preferred embodiment, to anchor governance outputs into a cryptographic evidence registry under a hash chain aggregated by a Merkle tree, with configuration version identifiers cryptographically bound to each anchored record.

[0010] It is a further object of the invention, in at least one preferred embodiment, to project stabilised governance trajectories over forward-looking projection horizons and to evaluate those trajectories analytically against machine-readable representations of active insurance-policy conditions.

[0011] It is a further object of the invention to ameliorate one or more disadvantages of prior systems, or at least to provide a useful alternative thereto. SUMMARY OF THE INVENTION

[0012] In a first aspect, the invention provides a computer-implemented system (100) for real-time governance-to-capital translation, volatility compression, and machine-readable reserve-allocation signalling in artificial intelligence insurance underwriting. The system (100) comprises one or more processors and a memory storing instructions that, when executed by the one or more processors, cause the system to: receive, via a telemetry acquisition interface (110), heterogeneous 2026203898   20 May 2026 machine-readable governance telemetry from one or more upstream artificial intelligence governance systems, the telemetry comprising at least a calculated Oversight Conduct Degradation Coefficient representing a longitudinal decline in human review discipline and a Cryptographic Anomaly Correlation Index calculated over cryptographically chained crossworkflow anomaly signatures; canonicalise, via a semantic harmonisation and canonicalisation plane (120), the received telemetry into governance-state records under a unified risk data schema (130), the canonicalisation comprising Unicode Normalisation Form C normalisation, lexicographic key sorting, and timestamp synchronisation against a hardware-attested timing source within a trusted execution environment (123); compute, via a Volatility Compression Engine (160) using a Q16.16 fixed-point arithmetic unit (162), a variance reduction scaling factor and, from that scaling factor and a Governance Stability Coefficient, an Insurability Resilience Score; anchor the Insurability Resilience Score and related coefficients into a cryptographic governance evidence registry (190); and generate and transmit, via a cryptographic actuarial signal gateway (200), a cryptographically signed Dynamic Reserve Adjustment Vector to one or more remote insurer reserve-allocation systems (240) as a machine-readable reserve-allocation signal.

[0013] In a second aspect, the invention provides a narrower fallback embodiment of the system (100) wherein the said one or more governance coefficients comprise the Oversight Conduct Degradation Coefficient and the Cryptographic Anomaly Correlation Index in their specifically disclosed quantitative form; the variance reduction scaling factor is computed according to the relation S_V = K_base x (1 - OCDC_Q) x (1 - CACI_Q) x AE_avg; the Insurability Resilience Score is computed as IRS = O(GSC_Q) x S_V; the Dynamic Reserve Adjustment Vector is computed as V_DRA = A_adj x (1 - IRS); the cryptographic hash chain comprises a SHA-256 hash chain (191) aggregated under a Merkle tree (192); and the Q16.16 fixed-point arithmetic discipline is implemented in a dedicated Q16.16 fixed-point arithmetic unit (162).

[0014] In a third aspect, the system (100) further comprises a Governance Trajectory Modelling Engine (170) configured to project stabilised governance trajectories produced by the Volatility Compression Engine (160) across one or more forward-looking projection horizons aligned with insurance policy terms, reinsurance cession periods, prudential reporting cycles, or regulatory reporting horizons.

[0015] In a fourth aspect, the system (100) further comprises a Runtime Insurance Evaluation Module (180) configured to maintain one or more machine-readable representations of active insurance-policy conditions in a policy ontology store (181), evaluate projected governance trajectories produced by the Governance Trajectory Modelling Engine (170) against the said representations, and generate one or more analytical governance-risk evaluation outputs where a projected governance trajectory exceeds a threshold condition expressed by a machine-readable representation of an active insurance-policy condition. The Runtime Insurance Evaluation Module produces analytical signals only; application of any such output by an external enforcement system, control plane, or restriction mechanism is outside the scope of the invention. 2026203898   20 May 2026

[0016] In a fifth aspect, the invention provides a corresponding computer-implemented method comprising the said operations.

[0017] In a sixth aspect, the invention provides a non-transitory computer-readable storage medium storing instructions that, when executed by one or more processors of a computing system, cause the system to perform the said method.

[0018] Further aspects, embodiments, and variations of the invention are set out in the dependent claims and in the detailed description that follows. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Preferred embodiments of the invention will now be described, by way of example only, with reference to the accompanying drawings, in which:

[0020] FIG. 1 is a system architecture diagram of a computer-implemented system (100) for governance-to-capital translation and volatility compression, showing the dependency of the system upon an upstream actuarial signal generation system and an upstream verifiable human contribution system.

[0021] FIG. 2 is a diagram of the internal architecture of the Volatility Compression Engine (160), showing the longitudinal sliding window (161), the Q16.16 fixed-point arithmetic unit (162), and the computation of the variance reduction scaling factor S_V and the Insurability Resilience Score IRS = O(GSC_Q) x S_V.

[0022] FIG. 3 is a procedural flow diagram of the Insurability Resilience Score calculation within one sliding longitudinal window.

[0023] FIG. 4 is a diagram of the Dynamic Reserve Adjustment Vector generation and feedback loop, showing the composer, the signing module (210), the defined exchange protocol (220), the signed receipt acknowledgement (230), and the remote insurer reserve-allocation system (240).

[0024] FIG. 5 is a diagram of the Cryptographic Governance Evidence Registry (190) showing the SHA-256 hash chain (191), the Merkle tree aggregator (192), and the configuration version identifier binding.

[0025] FIG. 6 is a diagram of the Multi-Entity Governance Federation Interface (300) and the Federation Aggregator (310).

[0026] FIG. 7 is a block diagram of the Governance Trajectory Modelling Engine (170).

[0027] FIG. 8 is a block diagram of the Runtime Insurance Evaluation Module (180) and the policy ontology store (181).

[0028] FIG. 9 is a schematic of a governance digital twin embodiment.

[0029] FIG. 10 is a schematic of multi-agent governance propagation across delegated agentic workflows. 2026203898   20 May 2026

[0030] FIG. 11 is a diagram of regulatory schema mapping headers including mappings to ISO / IEC 42001, the NIST AI Risk Management Framework, APRA CPS 230, the EU Digital Operational Resilience Act, and the EU Artificial Intelligence Act.

[0031] FIG. 12 is a deployment-mode diagram showing on-premises, cloud-hosted, hybrid, and air-gapped deployment embodiments of the system (100). DETAILED DESCRIPTION OF EMBODIMENTS System Architecture (FIG. 1)

[0032] Referring to FIG. 1, the computer-implemented system (100) for real-time governance-to-capital translation and volatility compression comprises a telemetry acquisition interface (110) configured to receive heterogeneous machine-readable governance telemetry from one or more upstream artificial intelligence governance systems. The telemetry comprises, by way of nonlimiting example, a calculated Oversight Conduct Degradation Coefficient (OCDC), a Cryptographic Anomaly Correlation Index (CACI), a Governance Stability Coefficient (GSC), and a cognitive engagement score A_E.

[0033] The Oversight Conduct Degradation Coefficient is received as an input to the system (100) and is not calculated by the system (100). The Coefficient represents a longitudinal decline in human review discipline as detected by an upstream actuarial signal generation system. The Cryptographic Anomaly Correlation Index is similarly received as an input and represents a correlation calculated over cryptographically chained cross-workflow anomaly signatures. The cognitive engagement score A_E is received from an upstream verifiable human contribution system and is typically computed using a Levenshtein-based edit-distance algorithm over beforeedit and after-edit text states. Semantic Harmonisation and Canonicalisation (FIG. 1)

[0034] Referring further to FIG. 1, telemetry received via the telemetry acquisition interface (110) is delivered to a semantic harmonisation and canonicalisation plane (120). The canonicalisation plane (120) executes within or under the attestation of a trusted execution environment (123) and performs at least: Unicode Normalisation Form C normalisation; lexicographic sorting of object keys; whitespace normalisation; field-name harmonisation against the unified risk data schema (130); and timestamp synchronisation against a hardware-attested timing source (124). Canonicalised records are written to the unified risk data schema (130) and made available to the Volatility Compression Engine (160). Volatility Compression Engine (FIGS. 2 and 3)

[0035] Referring to FIG. 2, the Volatility Compression Engine (160) receives the canonicalised governance-state records from the unified risk data schema (130) and applies one or more statistical stabilisation operations to produce stabilised governance trajectories. Statistical 2026203898   20 May 2026 stabilisation operations may include, by way of non-limiting example, generalised additive models, smoothing splines, reduced-rank smoothing, moving-average transforms, variance stabilisation filters, probabilistic confidence weighting, exposure-asymmetry balancing, and iterative back-fitting algorithms.

[0036] In a preferred embodiment, the Volatility Compression Engine (160) computes, over a longitudinal sliding window (161) of canonicalised records, a variance reduction scaling factor S_V according to the relation: S_V = K_base x (1 - OCDC_Q) x (1 - CACI_Q) x AE_avg

[0037] where K_base is a baseline coefficient, OCDC_Q and CACI_Q are the Q16.16 fixed-point representations of the OCDC and CACI received from upstream, and AE_avg is the windowed average cognitive engagement score. The variance reduction scaling factor S_V is then combined with a stability multiplier O(GSC_Q) derived from a Q16.16 fixed-point representation of the Governance Stability Coefficient to produce an Insurability Resilience Score (IRS): IRS = &(GSC_Q) x S_V

[0038] All computations are performed within a Q16.16 fixed-point arithmetic unit (162) such that the same input record set, the same configuration version identifier, and the same coefficient set produce a bit-exact identical output across verifier instances. This bit-exactness ameliorates the floating-point least-significant-bit divergence that affects conventional reserve-signal generation systems.

[0039] Where the OCDC_Q and CACI_Q simultaneously exceed configured threshold values, the Volatility Compression Engine (160) may emit an Insurance Cliff threshold breach indicator denoting that a joint deterioration of oversight discipline and cross-workflow anomaly correlation has occurred. The Insurance Cliff threshold breach indicator is distinct from any upstream Automation Risk Score and is parameterised by configuration-versioned threshold values bound to the corresponding registry record. Cryptographic Governance Evidence Registry (FIG. 5)

[0040] Referring to FIG. 5, the IRS, the variance reduction scaling factor S_V, and the contributing coefficients OCDC_Q, CACI_Q, GSC_Q, AE_avg are anchored into a cryptographic governance evidence registry (190). The registry maintains a SHA-256 hash chain (191) aggregated under a Merkle tree (192). Each anchored record is cryptographically bound to a configuration version identifier so that the precise coefficient set, threshold values, and stabilisation parameters in force at the time of computation are forensically recoverable from the registry alone. Governance Trajectory Modelling Engine (FIG. 7) 2026203898   20 May 2026

[0041] Referring to FIG. 7, the Governance Trajectory Modelling Engine (170) computes the progression of stabilised governance-state outputs of the Volatility Compression Engine (160) over a forward-looking projection horizon. The trajectory modelling engine (170) may compute, by way of non-limiting example, governance deterioration trajectories, stability projections, volatility acceleration profiles, governance confidence forecasts, and projected insurability decline curves.

[0042] The trajectory modelling engine (170) may compute projected governance risk across future underwriting periods aligned with insurance policy terms, reinsurance cession periods, prudential reporting cycles, or regulatory reporting horizons. The trajectory modelling engine (170) may incorporate exogenous variables including model version change events, foundation model upgrades, deployment scope expansions, regulatory regime changes, and changes to the underlying governance system from which telemetry is received. The trajectory modelling engine (170) outputs projected governance trajectory records suitable for ingestion by the Runtime Insurance Evaluation Module (180) and by the cryptographic actuarial signal gateway (200). Runtime Insurance Evaluation Module (FIG. 8)

[0043] Referring to FIG. 8, the Runtime Insurance Evaluation Module (180) maintains, in a policy ontology store (181), one or more machine-readable representations of active insurance-policy conditions. The said representations may be expressed, by way of non-limiting example, as structured policy data objects, contract ontologies, machine-interpretable warranty schemas, or coverage-condition data graphs.

[0044] A machine-readable insurance-policy representation may encode, by way of non-limiting example: coverage thresholds; warranty conditions, including algorithmically defined performance warranties; indemnification limits; governance obligations imposed upon the insured by the policy; exclusion clauses, including affirmative artificial intelligence exclusion clauses, proxy bias exclusions, unprompted hallucination thresholds, and copyright infringement liability exclusions; deductible conditions; aggregate exposure limits; conditions precedent to coverage; and runtime conditions that, if breached, would void or limit coverage.

[0045] The Runtime Insurance Evaluation Module (180) evaluates projected governance trajectories produced by the Governance Trajectory Modelling Engine (170), together with stabilised governance trajectories produced by the Volatility Compression Engine (160) and capital intelligence outputs produced by the cryptographic actuarial signal gateway (200), against the active insurance-policy representations stored in the policy ontology store (181). The Module (180) generates one or more analytical governance-risk evaluation outputs where a projected governance trajectory exceeds a threshold condition expressed by a machine-readable representation of an active insurance-policy condition. The analytical governance-risk evaluation outputs may comprise, by way of non-limiting example, analytical alerts, governance remediation 2026203898   20 May 2026 recommendations, insurability re-evaluation signals, and forward-looking coverage-stability projections.

[0046] The analytical governance-risk evaluation outputs produced by the Runtime Insurance Evaluation Module (180) are analytical signals only. They are not applied by the method or system to any execution path of any artificial intelligence agent, control plane, or runtime restriction mechanism. Application of any such output by an external enforcement system is outside the scope of the invention. Express Exclusion of Runtime Enforcement Activity

[0047] The present invention does not determine, authorise, deny, throttle, revoke, permit, interdict, terminate, suspend, or physically constrain execution authority of any artificial intelligence process, tool invocation, workflow, network transmission, or agent action. Any runtime enforcement, execution gating, commitment validation, authority interlock, or execution control arising from analytical outputs generated by the invention is performed exclusively by external systems outside the scope of the present invention. This express exclusion preserves the architectural separation between the present invention, which is concerned with analytical governance-to-capital translation, and adjacent runtime governance enforcement systems, the present invention forming no part of any such adjacent system and producing no signals capable of compelling, gating, or interdicting execution by any artificial intelligence process or agent. Cryptographic Actuarial Signal Gateway (FIG. 4)

[0048] Referring to FIG. 4, the cryptographic actuarial signal gateway (200) comprises a payload composer, a signing module (210) operating within a trusted execution environment, and a defined exchange protocol (220) for transmission to one or more remote insurer reserve-allocation systems (240). The gateway (200) composes a payload comprising at least: a window identifier; a configuration version identifier; the Insurability Resilience Score; a Dynamic Reserve Adjustment Vector V_DRA computed as V_DRA = A_adj x (1 - IRS) where A_adj is a baseline reserve adjustment coefficient; a hardware-attested timestamp; and a cryptographic signature generated by the signing module (210).

[0049] The defined exchange protocol (220) is, in a preferred embodiment, a mutually authenticated Transport Layer Security channel. Upon receipt of the signed payload, the remote insurer reserve-allocation system (240) returns a Signed Receipt Acknowledgement (230). The cryptographic actuarial signal gateway (200) is configured such that further signal generation is gated upon recording of the Signed Receipt Acknowledgement to the cryptographic governance evidence registry (190). Multi-Entity Governance Federation (FIG. 6)

[0050] Referring to FIG. 6, in a federated deployment embodiment, a Multi-Entity Governance Federation Interface (300) receives anchored governance records from a plurality of instances of 2026203898   20 May 2026 the system (100), each instance operated by a distinct entity. A Federation Aggregator (310) computes federated capital intelligence outputs across the participating entities, preserving the cryptographic provenance of each contributing record through the constituent registries (190). Anonymised summary statistics produced by the Federation Aggregator (310) may be transmitted to actuarial benchmarking exchanges without disclosing the underlying governance records of any participating entity. Governance Digital Twin (FIG. 9)

[0051] In a further embodiment shown in FIG. 9, the system (100) is coupled to a governance digital twin module executing one or more parameterised counterfactual scenarios over the stabilised governance trajectories produced by the Volatility Compression Engine (160) and the projected governance trajectories produced by the Governance Trajectory Modelling Engine (170). The digital twin enables counterfactual analysis of governance interventions, foundationmodel upgrade paths, and regulatory-regime changes without modification to the source governance systems from which telemetry is received. Multi-Agent Propagation (FIG. 10)

[0052] In a further embodiment shown in FIG. 10, the system (100) propagates governance-state and trajectory information across delegated agentic workflows comprising a plurality of artificial intelligence agents acting in cooperative or hierarchical configurations. Propagation of insurability indicators across delegation boundaries is performed in a manner that preserves cryptographic provenance and the configuration-version binding of each constituent record. The propagation embodiment does not itself authorise, gate, restrict, or interdict the execution of any artificial intelligence agent within the delegated workflow. Regulatory Schema Mapping (FIG. 11)

[0053] In a further embodiment shown in FIG. 11, payloads transmitted by the cryptographic actuarial signal gateway (200) include regulatory schema mapping headers that express the relationship between the payload contents and one or more external regulatory frameworks. By way of non-limiting example, the mapping headers may identify correspondences to controls under ISO / IEC 42001, the NIST AI Risk Management Framework, APRA Prudential Standard CPS 230, the European Union Digital Operational Resilience Act, and the European Union Artificial Intelligence Act. Deployment Modes (FIG. 12)

[0054] In further embodiments shown in FIG. 12, the system (100) is deployed in any of an onpremises configuration, a cloud-hosted configuration, a hybrid configuration, or an air-gapped configuration. In an air-gapped configuration the cryptographic actuarial signal gateway (200) is configured to operate asynchronously, with signed payloads exported to portable cryptographic 2026203898   20 May 2026 media and ingested into the remote insurer reserve-allocation system (240) on a delayed schedule, the Signed Receipt Acknowledgement (230) being returned over the same channel. Reference Numerals

[0055] Reference numerals used in the drawings and detailed description denote: 100 — system; 110 — telemetry acquisition interface; 120 — semantic harmonisation and canonicalisation plane; 123 — trusted execution environment; 124 — hardware-attested timing source; 130 — unified risk data schema; 160 — Volatility Compression Engine; 161 — longitudinal sliding window; 162 — Q16.16 fixed-point arithmetic unit; 170 — Governance Trajectory Modelling Engine; 180 — Runtime Insurance Evaluation Module; 181 — policy ontology store; 190 — cryptographic governance evidence registry; 191 — SHA-256 hash chain; 192 — Merkle tree aggregator; 200 — cryptographic actuarial signal gateway; 210 — signing module; 220 — defined exchange protocol; 230 — Signed Receipt Acknowledgement; 240 — remote insurer reserve-allocation system; 300 — Multi-Entity Governance Federation Interface; 310 — Federation Aggregator. Express Distinction from Adjacent Subject Matter

[0056] For the avoidance of doubt, the Insurability Resilience Score generated by the system (100) is distinct from any Automation Risk Score that may be generated by an upstream actuarial signal generation system from which the OCDC, CACI, and GSC inputs are received. The system (100) does not itself generate governance telemetry; it consumes governance telemetry produced by external upstream systems. The system (100) does not itself enforce restrictions upon any artificial intelligence agent or control plane; analytical governance-risk evaluation outputs produced by the Runtime Insurance Evaluation Module (180) are analytical signals only, the application of which by any external enforcement system is outside the scope of the invention.

[0057] The system (100) is positioned downstream of, and dependent upon, upstream actuarial signal generation systems and upstream verifiable human contribution systems for the supply of its primary input coefficients. The system (100) does not require ownership of, generation of, or modification to those external upstream systems. Worked Numerical Example

[0058] By way of illustration only and not by way of limitation, the operation of the system (100) is now described with reference to specific representative input values. The values are illustrative only; the invention is not limited to the specific numerical values appearing in this worked example.

[0059] First, consider a nominal-governance state. Suppose the system (100) receives, via the telemetry acquisition interface (110), the following inputs from one or more upstream artificial intelligence governance systems over one longitudinal sliding window (161): OCDC = 0.30 (representing a 30 percent longitudinal decline in human review discipline relative to a configured baseline); CACI = 0.20 (representing a 20 percent cross-workflow anomaly correlation); GSC = 2026203898   20 May 2026 0.75 (governance stability coefficient); and AE_avg = 0.85 (windowed average cognitive engagement score received from an upstream verifiable human contribution system). The baseline coefficient K_base is configured as 1.00 and the baseline reserve adjustment coefficient A_adj is configured as AUD 2,500,000.

[0060] The Q16.16 fixed-point arithmetic unit (162) converts the received coefficients to their Q16.16 representations: OCDC_Q = 0.30 x 65,536 = 19,661; CACI_Q = 0.20 x 65,536 = 13,107; GSC_Q = 0.75 x 65,536 = 49,152; and AE_avg expressed as a Q16.16 fraction = 55,706.

[0061] The Volatility Compression Engine (160) computes the variance reduction scaling factor S_V according to S_V = K_base x (1 - OCDC_Q) x (1 - CACI_Q) x AE_avg, yielding S_V = 1.00 x (1 - 0.30) x (1 - 0.20) x 0.85 = 1.00 x 0.70 x 0.80 x 0.85 = 0.476.

[0062] The Shannon-entropy-derived stability multiplier O(GSC_Q), for GSC = 0.75, is computed as 0.90 in this worked example. The Insurability Resilience Score is therefore IRS = O(GSC_Q) x S_V = 0.90 x 0.476 = 0.4284.

[0063] The cryptographic actuarial signal gateway (200) composes the Dynamic Reserve Adjustment Vector as V_DRA = A_adj x (1 - IRS) = AUD 2,500,000 x (1 - 0.4284) = AUD 2,500,000 x 0.5716 = AUD 1,429,000. The payload comprising the window identifier, the configuration version identifier, IRS = 0.4284, V_DRA = AUD 1,429,000, the hardware-attested timestamp, and the cryptographic signature is transmitted to the remote insurer reserve-allocation system (240) over the mutually authenticated transport layer security channel. The Signed Receipt Acknowledgement (230) is recorded in the cryptographic governance evidence registry (190) before further signal generation occurs in the next sliding window step.

[0064] Where the configured threshold values for the Insurance Cliff threshold breach indicator are OCDC threshold = 0.40 and CACI threshold = 0.30, the nominal-governance state input values (OCDC = 0.30, CACI = 0.20) do not simultaneously exceed both thresholds and no Insurance Cliff threshold breach indicator is emitted in respect of this window.

[0065] Second, consider a deteriorated-governance state in a subsequent window where the upstream inputs become OCDC = 0.45 and CACI = 0.35 (with GSC and AE_avg unchanged). Both threshold values are now simultaneously exceeded and the Insurance Cliff threshold breach indicator is emitted, parameterised by the configuration-versioned threshold values cryptographically bound to the corresponding anchored record in the registry (190). The Volatility Compression Engine (160) computes S_V = 1.00 x (1 - 0.45) x (1 - 0.35) x 0.85 = 1.00 x 0.55 x 0.65 x 0.85 = 0.304, and IRS = 0.90 x 0.304 = 0.2736. The Dynamic Reserve Adjustment Vector becomes V_DRA = AUD 2,500,000 x (1 - 0.2736) = AUD 2,500,000 x 0.7264 = AUD 1,816,000, a higher reserve allocation reflecting the elevated governance risk posture detected in this window. The corresponding payload, including the Insurance Cliff threshold breach indicator and the configuration version identifier, is transmitted to the remote insurer reserve-allocation system 2026203898   20 May 2026 (240) under the same mutually authenticated channel and gated upon receipt of the corresponding Signed Receipt Acknowledgement (230).

[0066] Because all computations are performed in the Q16.16 fixed-point arithmetic unit (162) within or under the attestation of the trusted execution environment (123), the same inputs, configuration version identifier, and coefficient set will produce a bit-exact identical V_DRA in independent verifier instances examining the registry contents — a property that conventional floating-point reserve-signal generation systems do not provide. Aristocrat Eligibility — Concrete Artificial State of Affairs

[0067] The combination of: (i) the telemetry acquisition interface (110); (ii) the semantic harmonisation and canonicalisation plane (120) executing within or under the attestation of the trusted execution environment (123) and synchronised against the hardware-attested timing source (124); (iii) the unified risk data schema (130); (iv) the Volatility Compression Engine (160) computing the variance reduction scaling factor S_V and the Insurability Resilience Score IRS in the Q16.16 fixed-point arithmetic unit (162) over the longitudinal sliding window (161); (v) the cryptographic governance evidence registry (190) maintaining the SHA-256 hash chain (191) aggregated under the Merkle tree (192) with the configuration version identifier binding; and (vi) the cryptographic actuarial signal gateway (200) generating and transmitting the cryptographically signed Dynamic Reserve Adjustment Vector to the remote insurer reserve-allocation system (240) over the defined exchange protocol (220) with the Signed Receipt Acknowledgement (230) recorded to the registry (190), produces a concrete artificial state of affairs and a useful result in an external field of economic endeavour.

[0068] In particular, the bit-exact deterministic Dynamic Reserve Adjustment Vector produced by the Q16.16 fixed-point arithmetic unit (162) solves a specific technical defect of prior systems, namely floating-point least-significant-bit divergence between verifier instances which, in conventional implementations, would prevent the resulting signal from being acted upon by an automated reserve-allocation system with cryptographic confidence.

[0069] The cryptographic binding of each anchored record to a configuration version identifier provides a further technical effect, namely the forensic recoverability from the registry alone of the precise coefficient set, threshold values, and stabilisation parameters in force at the time of computation. This forensic recoverability is a concrete technical capability not present in conventional governance reporting systems.

[0070] Accordingly, the invention as claimed is not a mere computer implementation of an abstract idea or a scheme; it is a particular combination of hardware-attested, fixed-point, cryptographically anchored, and cryptographically authenticated technical elements producing an artificial state of affairs of practical economic utility in the field of artificial intelligence insurance underwriting. 2026203898   20 May 2026 Best Method of Performing the Invention

[0071] The best method presently known to the inventor of performing the invention is as follows. Heterogeneous governance telemetry is received from upstream actuarial signal generation and verifiable human contribution systems via the telemetry acquisition interface (110), the telemetry comprising the OCDC, CACI, GSC, and A_E coefficients in their as-received representations.

[0072] The received telemetry is canonicalised in the semantic harmonisation and canonicalisation plane (120) executing within the attestation of the trusted execution environment (123). Canonicalisation comprises Unicode Normalisation Form C normalisation, lexicographic key sorting, whitespace normalisation, field-name harmonisation against the unified risk data schema (130), and timestamp synchronisation against the hardware-attested timing source (124). Canonicalised governance-state records are written to the unified risk data schema (130).

[0073] Volatility compression is performed by the Volatility Compression Engine (160) over a longitudinal sliding window (161) using a generalised additive model with smoothing splines as the preferred statistical stabilisation operation. The variance reduction scaling factor S_V is computed in the Q16.16 fixed-point arithmetic unit (162) according to S_V = K_base x (1 -OCDC_Q) x (1 - CACI_Q) x AE_avg, and the Insurability Resilience Score IRS is computed as IRS = O(GSC_Q) x S_V, where O is a Shannon-entropy-derived stability multiplier.

[0074] Each computed IRS, together with the contributing coefficients and the configuration version identifier in force at the time of computation, is anchored into the cryptographic governance evidence registry (190) via the SHA-256 hash chain (191) and the Merkle tree (192).

[0075] The Governance Trajectory Modelling Engine (170), in the preferred mode, projects stabilised governance trajectories over a forward-looking horizon corresponding to the active insurance policy term, using a generalised additive model with smoothing splines and including exogenous variables for model version change events and foundation model upgrades.

[0076] The Runtime Insurance Evaluation Module (180), in the preferred mode, evaluates the projected governance trajectories against the active insurance-policy representations stored in the policy ontology store (181) and generates analytical governance-risk evaluation outputs where a projected governance trajectory exceeds a threshold condition expressed by a machine-readable representation of an active insurance-policy condition. The preferred mode of operation of the Module (180) does not itself perform telemetry generation, attestation, commitment, signal verification, or runtime enforcement actions.

[0077] Payload composition, signing, and transmission are performed by the cryptographic actuarial signal gateway (200). The payload comprises a window identifier, a configuration version identifier, the IRS, the Dynamic Reserve Adjustment Vector V_DRA = A_adj x (1 - IRS), a hardware-attested timestamp, and the cryptographic signature generated by the signing module (210). Transmission to the remote insurer reserve-allocation system (240) occurs over the defined exchange protocol (220), being in the preferred mode a mutually authenticated Transport Layer 2026203898   20 May 2026 Security channel. The Signed Receipt Acknowledgement (230) is recorded into the cryptographic governance evidence registry (190) before further signal generation occurs.

[0078] Regulatory schema mapping headers, in the preferred mode, identify correspondences to ISO / IEC 42001, the NIST AI Risk Management Framework, APRA Prudential Standard CPS 230, the European Union Digital Operational Resilience Act, and the European Union Artificial Intelligence Act. The preferred operating cadence is continuous, with one IRS computation, anchoring, V_DRA composition, and gateway transmission per sliding window step.

Claims

1. A computer-implemented system for real-time governance-to-capital translation and volatility compression of artificial intelligence risk, the system comprising one or more processors and a memory storing instructions that, when executed by the one or more processors, cause the system to: (a) receive, via a telemetry acquisition interface (110), heterogeneous machine-readable governance telemetry comprising at least a calculated Oversight Conduct Degradation Coefficient OCDC representing a longitudinal decline in human review discipline, a Cryptographic Anomaly Correlation Index CACI calculated over cryptographically chained cross-workflow anomaly signatures, a Governance Stability Coefficient GSC, and a cognitive engagement score AE received from an upstream verifiable human contribution system; (b) canonicalise the received telemetry via a semantic harmonisation and canonicalisation plane (120) executing within or under the attestation of a trusted execution environment (123), the canonicalisation comprising Unicode Normalisation Form C normalisation, lexicographic key sorting, and timestamp synchronisation against a hardware-attested timing source (124); (c) compute, in a Q16.16 fixed-point arithmetic unit (162) over a longitudinal sliding window (161) of canonicalised governance-state records, a variance reduction scaling factor S_V according to S_V = K_base x (1 - OCDC_Q) x (1 - CACI_Q) x AE_avg, where K_base is a baseline coefficient and OCDC_Q, CACI_Q and AE_avg are the respective Q16.16 fixed-point representations; (d) compute, in the said Q16.16 fixed-point arithmetic unit (162), an Insurability Resilience Score IRS = O(GSC_Q) x S_V, where O is a stability multiplier and GSC_Q is the Q16.16 fixed-point representation of the Governance Stability Coefficient; (e) anchor each computed IRS, together with the contributing coefficients and a configuration version identifier in force at the time of computation, into a cryptographic governance evidence registry (190) maintaining a SHA-256 hash chain (191) aggregated under a Merkle tree (192); and (f) generate and transmit, via a cryptographic actuarial signal gateway (200) comprising a signing module (210) operating within a trusted execution environment and a defined exchange protocol (220) being a mutually authenticated Transport Layer Security channel, a cryptographically signed Dynamic Reserve Adjustment Vector V_DRA = A_adj x (1 - IRS) to a remote insurer reserve-allocation system (240), and gate further generation of the Dynamic Reserve Adjustment Vector upon recording of a Signed Receipt Acknowledgement (230) returned from the said remote insurer reserve-allocation system to the cryptographic governance evidence registry (190); wherein the Q16.16 fixed-point arithmetic discipline is implemented such that the same input record set, the same configuration version identifier, and the same coefficient set produce a bit-exact identical output across independent verifier instances.

2. The system of claim 1, wherein the stability multiplier O comprises a Shannon-entropy sub-coefficient computed over a configured window of canonicalised governance-state records.2026203898   03 Sep 20262026203898   03 Sep 20263. A computer-implemented method for real-time governance-to-capital translation and volatility compression of artificial intelligence risk, comprising: (a) receiving, via a telemetry acquisition interface (110), heterogeneous machine-readable governance telemetry comprising at least a calculated Oversight Conduct Degradation Coefficient OCDC representing a longitudinal decline in human review discipline, a Cryptographic Anomaly Correlation Index CACI calculated over cryptographically chained cross-workflow anomaly signatures, a Governance Stability Coefficient GSC, and a cognitive engagement score AE received from an upstream verifiable human contribution system; (b) canonicalising the received telemetry via a semantic harmonisation and canonicalisation plane (120) executing within or under the attestation of a trusted execution environment (123), the canonicalisation comprising Unicode Normalisation Form C normalisation, lexicographic key sorting, and timestamp synchronisation against a hardware-attested timing source (124); (c) computing, in Q16.16 fixed-point arithmetic over a longitudinal sliding window (161) of canonicalised governance-state records, a variance reduction scaling factor S_V according to S_V = K_base x (1 - OCDC_Q) x (1 - CACI_Q) x AE_avg; (d) computing, in the said Q16.16 fixed-point arithmetic, an Insurability Resilience Score IRS = O(GSC_Q) x S_V; (e) anchoring the computed IRS, the contributing coefficients and a configuration version identifier in force at the time of computation into a cryptographic governance evidence registry (190) maintaining a SHA-256 hash chain (191) aggregated under a Merkle tree (192); (f) generating and transmitting, via a cryptographic actuarial signal gateway (200) over a mutually authenticated Transport Layer Security channel, a cryptographically signed Dynamic Reserve Adjustment Vector V_DRA = A_adj x (1 - IRS) to a remote insurer reserve-allocation system (240); and (g) preventing further generation of the Dynamic Reserve Adjustment Vector until a Signed Receipt Acknowledgement (230) returned from the remote insurer reserve-allocation system has been recorded in the cryptographic governance evidence registry (190); wherein the same input record set, the same configuration version identifier, and the same coefficient set produce a bit-exact identical output across independent verifier instances.

4. The method of claim 3, further comprising: (a) projecting, by a Governance Trajectory Modelling Engine (170), one or more stabilised governance trajectories over a forward-looking projection horizon aligned with at least one of an insurance-policy term, a reinsurance cession period, a prudential reporting cycle, or a regulatory reporting horizon; (b) maintaining, in a policy ontology store (181), one or more machine-readable representations of active insurance-policy conditions expressed as at least one of a structured policy data object, a contract ontology, a machine-interpretable warranty schema, or a coverage-condition data graph; (c) encoding in the machine-readable representation at least one of a coverage threshold, warranty condition, indemnification limit, governance obligation imposed upon an insured, exclusion clause, deductible condition, aggregate exposure limit, or condition precedent to coverage; (d) analytically evaluating, by a Runtime Insurance Evaluation Module (180), the projected governance trajectories against the machine-readable representations of active insurance-policy conditions; and (e) generating one or more governance-risk evaluation outputs where a projected governance trajectory exceeds a threshold condition expressed by a machine-readable representation of an active insurance-policy condition, the outputs comprising one or more of an analytical alert, governance remediation recommendation, insurability re-evaluation signal, or forward-looking coverage-stability projection; wherein the governance-risk evaluation outputs are analytical signals only, are not applied by the method to any execution path of any artificial intelligence agent, and application of any such output by an external enforcement system is outside the scope of the method.2026203898   03 Sep 20265. The system of claim 1, further comprising a Governance Trajectory Modelling Engine (170) configured to project stabilised governance trajectories produced by the system across one or more forward-looking projection horizons aligned with insurance-policy terms, reinsurance cession periods, prudential reporting cycles, or regulatory reporting horizons, and to incorporate at least one exogenous variable selected from model version change events, foundation model upgrades, deployment scope expansions, regulatory regime changes, and changes to an upstream governance system from which telemetry is received.

6. The system of claim 5, further comprising a Runtime Insurance Evaluation Module (180) configured to maintain, in a policy ontology store (181), one or more machine-readable representations of active insurance-policy conditions expressed as at least one of a structured policy data object, a contract ontology, a machine-interpretable warranty schema, or a coverage-condition data graph; to encode at least one of a coverage threshold, warranty condition, indemnification limit, governance obligation imposed upon an insured, exclusion clause, deductible condition, aggregate exposure limit, or condition precedent to coverage; and to analytically evaluate projected governance trajectories produced by the Governance Trajectory Modelling Engine against the said representations to produce one or more governance-risk evaluation outputs.

7. The system of claim 6, wherein the governance-risk evaluation outputs comprise one or more of analytical alerts, governance remediation recommendations, insurability re-evaluation signals, and forward-looking coverage-stability projections, and are analytical signals only, are not applied by the system to any execution path of any artificial intelligence agent, and application of any such output by an external enforcement system is outside the scope of the system.

8. The system of claim 1, wherein the hardware-attested timing source comprises a hardware-attested timestamp generator operating within a trusted execution environment, and wherein each anchored record in the cryptographic governance evidence registry is bound to a hardware-attested timestamp signature.

9. The system of claim 1, wherein each anchored record in the cryptographic governance evidence registry is cryptographically bound to a configuration version identifier denoting the coefficient set, threshold values, stabilisation parameters, and arithmetic discipline in force at the time of computation.

10. The system of claim 1, further comprising a Multi-Entity Governance Federation Interface (300) configured to receive anchored governance records from a plurality of instances of the system, each instance operated by a distinct entity, and a Federation Aggregator (310) configured to compute federated capital intelligence outputs across the plurality of instances while preserving the cryptographic provenance of each contributing record.

11. The system of claim 5, further comprising a governance digital twin module configured to execute one or more parameterised counterfactual scenarios over the stabilised governance trajectories and projected governance trajectories without modification to any upstream governance system from which telemetry is received.

12. The system of claim 1, configured to propagate governance-state and insurability indicators across delegated agentic workflows comprising a plurality of artificial intelligence agents acting in cooperative or hierarchical configurations, the propagation preserving cryptographic provenance and configuration-version binding of each constituent record and not itself authorising, gating, restricting, or interdicting execution of an artificial intelligence agent.

13. The system of claim 1, wherein payloads transmitted by the cryptographic actuarial signal gateway include regulatory schema mapping headers identifying correspondences between the payload contents and one or more external regulatory frameworks comprising at least one of ISO / IEC 42001, the NIST Artificial Intelligence Risk Management Framework, APRA Prudential Standard CPS 230, the European Union Digital Operational Resilience Act, and the European Union Artificial Intelligence Act.

14. The system of claim 1, deployed in an air-gapped configuration in which the cryptographic actuarial signal gateway operates asynchronously, with signed payloads exported to portable cryptographic media and ingested into the remote insurer reserve-allocation system on a delayed schedule, and the Signed Receipt Acknowledgement returned over the same channel.

15. The system of claim 1, configured to interoperate with a plurality of heterogeneous source schemas through a schema-registry of canonicalisation rules, the schema-registry being cryptographically version-bound to the cryptographic governance evidence registry.

16. The system of claim 1, wherein the Insurability Resilience Score is distinct from any Automation Risk Score generated by an upstream actuarial signal generation system from which the governance telemetry is received.

17. The system of claim 1, wherein the system does not itself generate the governance telemetry received at the telemetry acquisition interface; does not physically restrict, block, throttle, interdict, suspend, revoke, or terminate any execution path, tool invocation, workflow, network transmission, or agent action of an artificial intelligence process or agent; does not perform execution authorisation, commitment validation, runtime interlock enforcement, or authority interdiction; and does not transmit a deterministic restriction signal, control-plane routing instruction, or execution-gating instruction to a runtime governance control plane.

18. The system of claim 1, wherein the system is further configured to emit an Insurance Cliff threshold breach indicator where OCDC_Q and CACI_Q simultaneously exceed configured threshold values, the indicator being parameterised by configuration-versioned threshold values cryptographically bound to the corresponding anchored record in the registry.

19. The system of claim 1, wherein the cognitive engagement score AE is received from an upstream verifiable human contribution system implementing a five-stage session log comprising Create, Edit, Review, Approve, and Disseminate stages, and is computed in the upstream verifiable human contribution system as a Levenshtein-based edit-distance metric over before-edit and after-edit text states.

20. A non-transitory computer-readable storage medium storing instructions that, when executed by one or more processors of a computing system, cause the computing system to perform the method of claim 3 or claim 4.2026203898   03 Sep 2026

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