OOF™ Value Flow Mechanism
Canonical Hierarchical Scheme (Regulator-Grade)
4. Origin Asset™ Layer
Level: Provenance AnchorAn Origin Asset™ is a Value Asset whose provenance, integrity, and
origin conditions are verifiably established.
Origin Assets act as reference anchors within the Value Flow Mechanism
and represent assets eligible for achieving or restoring ORIGIN VALUE™
status under validated criteria.
7. Canonical Definitions (Regulator-Grade)
Value Flow Mechanism (VFM™)A non-discretionary governance mechanism defining how value states are
assigned, withdrawn, neutralized, and restored over time based on
validated methodological criteria.
Value Asset
A neutral entity capable of carrying value within the system. Value Assets
are not judged; only their states change.
Origin Asset™
A Value Asset with verifiable provenance and integrity, eligible for
origin-based valuation and structured re-monetization.
Value States
An objective, system-validated condition reflecting the current valuation
status of a Value Asset.
ZVA™ (Zero Value Asset)
A confirmed value state indicating zero economic value under the
governing methodology.
DEMONET™ (De-Monetization)
A value state representing the structured withdrawal of monetization rights
under defined validation rules.
REMONET™ (Re-Monetization)
A transitional value state enabling conditional restoration of value following
verified remediation.
ORIGIN VALUE™
A state of validated, origin-bound, audit-confirmed value within the defined
governance framework.
8. Regulatory Positioning Statement
The OOF™ Value Flow Mechanism is not a punitive system, not
a market intervention, and not a discretionary rating tool.
It is a methodological governance framework designed to provide
regulators, institutions, and systems with an objective, auditable, and
repeatable model for managing value transitions.
OOF™ does not execute state transitions.
It defines the architectural methodology under which compliant systems
may implement them within their respective legal and operational environments.
VFM™ Use Case
Digital-to-Physical Value Transition
Governance Context Modern economic value is increasingly created in digital environments:a>
credit evaluation, asset recognition, or regulatory reporting — there is no
standardized methodology governing how that transition should be
validated.
Current systems track transactions.
They do not define structured value-state transitions.
Governance Context Modern economic value is increasingly created in digital environments:a>
- AI-generated services
- cloud-based platforms
- tokenized representations of physical rights
- cross-border digital revenues
credit evaluation, asset recognition, or regulatory reporting — there is no
standardized methodology governing how that transition should be
validated.
Current systems track transactions.
They do not define structured value-state transitions.
Structural Gap
When digital value moves into physical or jurisdictional recognition,critical questions arise:
- Where did the value originate?
- Under which conditions is it economically valid?
- When should monetization rights be suspended?
- How can value be restored after compliance failure?
transitions across domains.
VFM™ Application
The OOF™ Value Flow Mechanism introduces:- a neutral Value Asset model
- objective Value States
- rule-based, non-discretionary state transitions
- audit-bound validation logic
framework enables:
- origin-bound validation (ORIGIN VALUE™)
- structured monetization withdrawal (DEMONET™)
- confirmed economic neutralization (ZVA™)
- controlled restoration pathways (REMONET™)
It provides a methodological structure under which value transitions may
be assessed consistently and transparently.
Relevance for Regulators
For regulatory bodies and supervisory authorities, VFM™ offers:- a structured vocabulary for cross-domain value recognition
- a non-discretionary logic for economic state assignment
- audit-aligned transition governance
- conceptual compatibility with digital economy oversight
It defines how value states may be methodologically interpreted across
digital and physical domains.
VFM™ Use Case
AI-Based Credit and Economic Decision
Governance Context Financial institutions increasingly rely on automated and AI-driven systems
for:
financial stability.
However, there is no standardized governance architecture defining how
the economic state of a decision should be assigned, suspended, or
restored when model integrity changes.
Governance Context Financial institutions increasingly rely on automated and AI-driven systems
for:
- credit approval
- risk assessment
- insurance pricing
- capital allocation
financial stability.
However, there is no standardized governance architecture defining how
the economic state of a decision should be assigned, suspended, or
restored when model integrity changes.
Structural Gap
Current AI governance frameworks focus on:- bias mitigation
- explainability
- model transparency
consequences of automated decisions.
When a model fails validation:
- What happens to monetization rights?
- How is economic impact neutralized?
- What is the structured path toward reinstatement?
VFM™ Application
Within the Value Flow Mechanism:An AI credit model can be treated as a Value Asset.
The economic impact of its decisions may be governed by objective value states:
- ORIGIN VALUE™ – validated, audit-confirmed operational integrity
- DEMONET™ – temporary suspension of monetization rights following
compliance failure - ZVA™ – confirmed economic neutralization under defined methodology
- REMONET™ – structured reinstatement after corrective validation
economic consequences of automated systems.
Relevance for Regulators
For supervisory authorities and policy frameworks, VFM™ provides:- a structured state model for economic consequence management
- audit-aligned transition logic
- conceptual compatibility with AI governance regimes
- non-punitive, rule-based value control architecture
It defines methodological governance of value transitions.
Relevance for Banks and Financial Institutions
For financial institutions, the architecture may support:- structured control of AI-driven monetization exposure
- clearer internal governance of model-risk consequences
- defined remediation pathways before economic reinstatement
consequences are state-bound rather than discretion-bound.