



Internet Engineering Task Force                               T. Adebayo
Internet-Draft                                                O. Apalowo
Intended status: Informational                             F. Makanjuola
Expires: 7 October 2026                                      Veridom Ltd
                                                            5 April 2026


  OMP Domain Profile: AI Governance and Accountability Evidence for US
  Housing Finance Under FHFA Bulletin 2025-16 and GSE AI/ML Model Risk
                               Governance
                       draft-veridom-omp-fhfa-00

Abstract

   This document defines a domain profile of the Operating Model
   Protocol (OMP) for AI and machine learning (ML) systems deployed in
   US housing finance contexts subject to the Federal Housing Finance
   Agency (FHFA) Bulletin 2025-16 (effective March 3, 2026), which
   establishes a comprehensive AI governance framework for Fannie Mae,
   Freddie Mac, and the Federal Home Loan Banks (the GSEs), requiring
   transparency, accountability, and ethical stewardship for AI/ML
   systems used in housing finance decisions.

   The profile -- designated HomeMark -- specifies how OMP's
   deterministic routing invariant, Watchtower enforcement framework,
   and three-layer cryptographic integrity architecture satisfy the AI
   governance evidence requirements of FHFA Bulletin 2025-16, including
   per-decision accountability, named individual responsibility, model
   risk governance documentation, fair lending evidence, and
   representation and warranty compliance for mortgage origination,
   credit decisioning, property valuation, and loan servicing.

   The OMP core specification is defined in the Operating Model Protocol
   Internet-Draft (draft-veridom-omp).

Status of This Memo

   This Internet-Draft is submitted in full conformance with the
   provisions of BCP 78 and BCP 79.

   Internet-Drafts are working documents of the Internet Engineering
   Task Force (IETF).  Note that other groups may also distribute
   working documents as Internet-Drafts.  The list of current Internet-
   Drafts is at https://datatracker.ietf.org/drafts/current/.







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Copyright Notice

   Copyright (c) 2026 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

   This document is subject to BCP 78 and the IETF Trust's Legal
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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   3
   2.  Terminology . . . . . . . . . . . . . . . . . . . . . . . . .   4
   3.  FHFA AI Governance Framework Analysis . . . . . . . . . . . .   5
     3.1.  FHFA Bulletin 2025-16 . . . . . . . . . . . . . . . . . .   5
     3.2.  GSE AI/ML Model Risk Governance . . . . . . . . . . . . .   5
     3.3.  Fair Lending Obligations and Disparate Impact . . . . . .   5
     3.4.  Representation and Warranty Framework . . . . . . . . . .   5
     3.5.  FHFA Examination Authority  . . . . . . . . . . . . . . .   6
     3.6.  Convergent Requirements . . . . . . . . . . . . . . . . .   6
   4.  OMP HomeMark Profile  . . . . . . . . . . . . . . . . . . . .   6
     4.1.  Routing States Under This Profile . . . . . . . . . . . .   6
     4.2.  Named Accountable Officer: The Responsible Individual . .   7
     4.3.  Watchtower Definitions  . . . . . . . . . . . . . . . . .   7
       4.3.1.  WT-FHFA-01: Housing Finance Decision Floor Gate . . .   7
       4.3.2.  WT-FHFA-02: Fair Lending Override Gate  . . . . . . .   7
       4.3.3.  WT-FHFA-03: Fair Lending Flag Gate  . . . . . . . . .   8
       4.3.4.  WT-FHFA-04: AVM / AUS Training Limitation Gate  . . .   8
       4.3.5.  WT-FHFA-05: Model Performance Anomaly Gate  . . . . .   8
       4.3.6.  WT-FHFA-06: R&W Eligibility Verification Gate . . . .   9
     4.4.  Audit Trace Schema Extensions . . . . . . . . . . . . . .   9
   5.  Representation and Warranty Evidence Architecture . . . . . .  10
   6.  Fair Lending Evidence Package . . . . . . . . . . . . . . . .  11
   7.  The HomeMark Invariant  . . . . . . . . . . . . . . . . . . .  11
   8.  Security Considerations . . . . . . . . . . . . . . . . . . .  12
   9.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  12
   10. References  . . . . . . . . . . . . . . . . . . . . . . . . .  12
     10.1.  Normative References . . . . . . . . . . . . . . . . . .  12
     10.2.  Informative References . . . . . . . . . . . . . . . . .  13



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   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  14

1.  Introduction

   AI and machine learning systems are now foundational to US housing
   finance operations: in automated underwriting systems (AUS) for
   mortgage origination, automated valuation models (AVMs) for property
   assessment, loss mitigation decisioning in loan servicing, and loan
   acquisition models in the secondary market.  The GSEs operate at
   national scale -- Fannie Mae and Freddie Mac collectively support the
   majority of US mortgage originations -- meaning that AI/ML governance
   failures have systemic implications for housing access, fair lending,
   and financial stability.

   FHFA Bulletin 2025-16 [FHFA-2025-16] (effective March 3, 2026)
   establishes four governance pillars: transparency (GSEs must explain
   AI/ML decisions to regulators, counterparties, and borrowers at the
   individual loan level); accountability (named individuals must bear
   documented responsibility for AI/ML outcomes at scale); ethical
   stewardship (AI/ML systems must not produce discriminatory outcomes
   inconsistent with the GSEs' statutory mission); and model risk
   governance (AI/ML systems must be subject to rigorous MRM frameworks
   including decision-level reconstructability).

   These requirements converge on a per-decision accountability problem
   that OMP [I-D.veridom-omp] is specifically designed to address: for
   any individual mortgage credit decision, property valuation, or
   servicing action influenced by AI/ML, the entity must demonstrate
   what the AI/ML recommended, what data it used, which named individual
   bore accountability, and whether the record has remained intact.

   This document defines the HomeMark profile: the domain-specific
   instantiation of OMP for FHFA-regulated housing finance AI/ML
   deployments.  HomeMark denotes that every AI/ML-assisted housing
   finance decision is cryptographically marked against the entity's
   FHFA Bulletin 2025-16 obligations, producing a tamper-evident
   accountability record at the loan level.

   Related OMP domain profiles include the Employment ADS profile
   [I-D.veridom-omp-employ] and the EU AI Act Article 12 profile
   [I-D.veridom-omp-euaia].  Audit Trace payloads are canonicalized per
   [RFC8785].  The OMP specification is also archived at [ZENODO-OMP].

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
   document are to be interpreted as described in [RFC2119] [RFC8174].





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2.  Terminology

   This document uses the terminology defined in [I-D.veridom-omp].  In
   addition:

   *  Government-Sponsored Enterprise (GSE): Fannie Mae, Freddie Mac, or
      a Federal Home Loan Bank, as regulated by FHFA under the Housing
      and Economic Recovery Act of 2008.

   *  Automated Underwriting System (AUS): A GSE-operated or GSE-
      approved AI/ML system that evaluates mortgage applications and
      provides a credit recommendation (Approve/Eligible, Refer, Refer
      with Caution, or Ineligible).  Includes Fannie Mae Desktop
      Underwriter (DU) and Freddie Mac Loan Product Advisor (LPA).

   *  Automated Valuation Model (AVM): An AI/ML system that generates an
      estimate of a property's market value based on comparable sales
      data, property characteristics, and market conditions.

   *  Consequential Housing Finance Decision: An AI/ML-assisted decision
      that directly affects a borrower's mortgage application status,
      loan terms, property valuation, or loan servicing outcome.
      Subject to the HomeMark Invariant.

   *  Responsible Individual (RI): The named individual within a GSE,
      lender, servicer, or counterparty who bears documented
      accountability for an AI/ML-assisted housing finance decision.  In
      OMP terms, the Named Accountable Officer for ASSISTED and
      ESCALATED interactions.

   *  Representation and Warranty (R&W): The representations and
      warranties made by mortgage originators and sellers to the GSEs
      regarding loan quality, eligibility, and compliance.  Where AI/ML
      contributed to a loan-level decision, R&W obligations require the
      ability to demonstrate that the AI/ML operated correctly and
      consistently with applicable guidelines.

   *  Fair Lending Flag: A field indicating that the AI/ML
      recommendation involves a borrower demographic profile or
      geographic area identified in the entity's fair lending analysis
      as requiring heightened review for potential disparate impact
      under ECOA [ECOA] or the Fair Housing Act [FHA-1968].

   *  HomeMark Invariant: The two-property invariant defined in
      Section 7: every Consequential Housing Finance Decision generates
      a sealed HomeMark Audit Trace independently verifiable by FHFA
      examiners, counterparties, and auditors.




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3.  FHFA AI Governance Framework Analysis

3.1.  FHFA Bulletin 2025-16

   FHFA Bulletin 2025-16 requires transparency (individual loan-level
   documentation explaining AI/ML decisions, contemporaneous not
   retrospective), accountability (named Responsible Individuals with
   documented responsibility for AI/ML system governance and decision
   outcomes), ethical stewardship (per-decision fair lending monitoring
   and disparate impact assessment), and model risk governance
   (decision- level reconstructability, ongoing performance monitoring,
   and human oversight at defined thresholds).

3.2.  GSE AI/ML Model Risk Governance

   GSE MRG frameworks, informed by Bulletin 2025-16 and SR 11-7
   [SR-11-7], require decision- level reconstructability (for any loan-
   level decision, the entity must reconstruct the model's input data,
   version, and output consistent with the specific loan record);
   ongoing monitoring for performance degradation, distributional shift,
   and fair lending risk; and human oversight documentation at defined
   thresholds.

3.3.  Fair Lending Obligations and Disparate Impact

   The GSEs operate under ECOA and the Fair Housing Act, prohibiting
   both intentional discrimination and AI/ML practices producing
   unjustified disparate impact.  FHFA Bulletin 2025-16 requires GSEs to
   assess and document disparate impact in AI/ML- assisted housing
   finance decisions.  The per-decision HomeMark Audit Trace provides
   the loan-level evidence fair lending examinations require: what the
   AI/ML recommended, what data it used, whether a fair lending flag was
   triggered, and what human oversight was applied.

3.4.  Representation and Warranty Framework

   Where an AI/ML system contributed to loan origination or eligibility
   determination, the seller's R&W obligations require the ability to
   demonstrate that the AI/ML operated correctly and consistently with
   applicable guidelines at origination.  HomeMark Audit Traces
   generated at origination provide this loan-level evidence: the RFC
   3161 [RFC3161] timestamp proves the AI/ML recommendation was
   generated at origination (not reconstructed retrospectively), the
   interaction_hash proves input data integrity, and the
   ai_ml_system_version documents which AUS version was in effect.






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3.5.  FHFA Examination Authority

   FHFA has broad examination authority over GSEs and their
   counterparties.  FHFA examiners may request AI/ML decision process
   documentation, model risk governance evidence, and fair lending
   monitoring data at the individual loan level.  The HomeMark FHFA
   Examination Package is designed to satisfy examiner requests within
   the 30-second production capability specified in this profile.

3.6.  Convergent Requirements

   FHFA Bulletin 2025-16, GSE MRG frameworks, ECOA/FHA obligations, and
   the R&W framework converge on a structure mapping to OMP's three
   routing states: AI/ML decisions where the RI reviewed the
   recommendation and bears documented accountability correspond to
   ASSISTED; decisions where a Fair Lending Flag triggered, confidence
   fell below the housing finance floor, or a model governance concern
   was detected correspond to ESCALATED; fully autonomous AUS-eligible
   transactions are permitted under AUTONOMOUS subject to Section 4.1
   constraints, but HomeMark Audit Traces MUST be generated even for
   AUTONOMOUS routing.

4.  OMP HomeMark Profile

4.1.  Routing States Under This Profile

   *  AUTONOMOUS: Permitted for standard AUS-eligible mortgage
      transactions where: the AUS recommendation is Approve/Eligible;
      the Confidence Score meets the AUTONOMOUS threshold; no Watchtower
      has triggered; the Fair Lending Flag has not been set; and the
      loan falls within the AUS's validated operating envelope.  Even
      under AUTONOMOUS routing, the HomeMark Audit Trace MUST be
      generated and sealed for every loan-level interaction, consistent
      with FHFA Bulletin 2025-16's transparency and reconstructability
      requirements.

   *  ASSISTED: Required where: AUS recommendation is Refer or Refer
      with Caution; transaction exceeds the significance threshold
      requiring RI review; a Fair Lending Flag is set; or a model
      governance concern is detected.  The RI's identity, review
      timestamp, and decision basis are sealed in the HomeMark Audit
      Trace.

   *  ESCALATED: Triggered by: HARD_BLOCK from WT-FHFA-02, confidence
      failure below the housing finance decision floor (WT-FHFA-01),
      model performance anomaly (WT-FHFA-05), or regulatory override
      requirement.  AI/ML recommendation MUST NOT be acted upon until
      the RI has reviewed and documented a disposition.



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4.2.  Named Accountable Officer: The Responsible Individual

   The Named Accountable Officer under this profile is the Responsible
   Individual: the named person who bears documented accountability for
   the AI/ML-assisted housing finance decision.  Required fields:

   *  ri_employee_id: stable identifier consistent throughout the
      relevant loan warranty period;

   *  ri_role: role in the AI/ML governance or decision chain (e.g.,
      "underwriter", "credit_officer", "AUS_governance_lead");

   *  ri_review_timestamp: ISO 8601 UTC of the RI's review action;

   *  ri_decision: one of PROCEED_WITH_AI_RECOMMENDATION,
      PROCEED_MODIFIED, OVERRIDE, DENY_APPLICATION, APPROVE_APPLICATION,
      REFER_TO_MANUAL_UNDERWRITING;

   *  ri_decision_basis: REQUIRED for all values other than
      PROCEED_WITH_AI_RECOMMENDATION.

4.3.  Watchtower Definitions

4.3.1.  WT-FHFA-01: Housing Finance Decision Floor Gate

   *Trigger:* Composite Confidence Score falls below the housing finance
   decision floor.  For AUS: a Refer or Refer with Caution
   recommendation signals the loan is outside AUS approval parameters.

   *Action:* FORCE_ASSISTED.  RI reviews the AI/ML recommendation before
   any credit action.  Loan file MUST reflect the RI's documented
   review.

   *Rationale:* FHFA Bulletin 2025-16 requires human oversight of AI/ML
   decisions below defined confidence thresholds.  An AUS Refer
   recommendation is itself a signal that human underwriting review is
   required.

4.3.2.  WT-FHFA-02: Fair Lending Override Gate

   *Trigger:* AI/ML recommendation involves a borrower demographic
   profile, geographic area, or loan characteristic identified in the
   entity's fair lending analysis as requiring heightened review for
   potential disparate impact under ECOA or the Fair Housing Act.

   *Action:* FORCE_ASSISTED for standard heightened review.  HARD_BLOCK
   where the AI/ML recommendation conflicts with a pre-identified fair
   lending risk pattern in the entity's corrective action plan.



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   *Rationale:* ECOA and the Fair Housing Act prohibit disparate impact
   in mortgage credit decisioning.  WT-FHFA-02 ensures loan applications
   in identified heightened-review categories receive documented human
   oversight, sealed in the Audit Trace for FHFA examination.

4.3.3.  WT-FHFA-03: Fair Lending Flag Gate

   *Trigger:* Ongoing HomeMark Audit Trace monitoring identifies that
   the AI/ML recommendation falls within a demographic or geographic
   segment exhibiting an approval rate or pricing disparity above the
   entity's configured fair lending alert threshold.

   *Action:* FORCE_ASSISTED. fair_lending_flag set to true.  RI review
   and decision basis REQUIRED.

   *Rationale:* Continuous per-decision fair lending monitoring enables
   entities to identify emerging disparate impact before it reaches the
   threshold of a CFPB or FHFA examination finding.  WT-FHFA-03 converts
   a periodic audit obligation into a continuous per-decision flag.

4.3.4.  WT-FHFA-04: AVM / AUS Training Limitation Gate

   *Trigger:* Property or loan characteristics match a known validation
   limitation of the AVM or AUS model (e.g., property type with limited
   comparable sales data; geographic market where the AUS was not
   validated; loan product feature outside the validated operating
   envelope).

   *Action:* FORCE_ASSISTED.  HomeMark Audit Trace records the specific
   training limitation triggered and the RI's disposition.

   *Rationale:* GSE model risk governance frameworks require entities to
   document model limitations and ensure decisions outside the validated
   envelope receive human review.  WT-FHFA-04 gives this requirement
   structural enforcement at the per-decision level.

4.3.5.  WT-FHFA-05: Model Performance Anomaly Gate

   *Trigger:* AI/ML recommendation deviates from expected operating
   parameters suggesting model degradation, distributional shift, or
   data quality failure.

   *Action:* FORCE_ESCALATED plus model performance anomaly alert to the
   entity's model risk governance team.







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   *Rationale:* AI/ML models in housing finance can experience
   distributional shift as housing market conditions evolve.  Early
   detection prevents systematic portfolio-level impact from a degraded
   model operating at scale.

4.3.6.  WT-FHFA-06: R&W Eligibility Verification Gate

   *Trigger:* For loans destined for GSE sale: the AI/ML recommendation
   or loan data presents a characteristic requiring specific
   verification for GSE representation and warranty compliance (e.g.,
   loan type requiring additional documentation; data field outside AUS
   verified input range; characteristic identified in recent GSE quality
   control findings as a common R&W breach source).

   *Action:* FORCE_ASSISTED.  RI verifies eligibility before the loan
   proceeds to GSE sale.  HomeMark Audit Trace records eligibility
   verification and RI confirmation.

   *Rationale:* GSE R&W obligations require originators to represent
   that AUS input data was accurate.  WT-FHFA-06 creates a sealed per-
   loan eligibility verification record supporting R&W compliance and
   providing evidence in any subsequent repurchase demand.

4.4.  Audit Trace Schema Extensions

   The following fields are REQUIRED under the HomeMark profile, in
   addition to core fields in [I-D.veridom-omp] Section 7:

   *  ri_employee_id: string, REQUIRED for Consequential Housing Finance
      Decisions.

   *  ri_role: string, REQUIRED.

   *  ri_review_timestamp: string, ISO 8601 UTC, REQUIRED for ASSISTED
      and ESCALATED.

   *  ri_decision: string, REQUIRED for ASSISTED and ESCALATED.  One of:
      PROCEED_WITH_AI_RECOMMENDATION, PROCEED_MODIFIED, OVERRIDE,
      DENY_APPLICATION, APPROVE_APPLICATION,
      REFER_TO_MANUAL_UNDERWRITING.

   *  ri_decision_basis: string, OPTIONAL for
      PROCEED_WITH_AI_RECOMMENDATION; REQUIRED for all other values.

   *  loan_identifier: string, REQUIRED.  Unique loan number or mortgage
      ID, enabling per-loan audit trail retrieval for FHFA examination,
      R&W review, and fair lending investigation.




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   *  ai_ml_system_id: string, REQUIRED.  Identifier of the AI/ML system
      (e.g., "DU_v11.0", "LPA_v5.2").

   *  ai_ml_system_version: string, REQUIRED.  Version in effect at time
      of interaction.  Critical for model risk governance and R&W
      compliance.

   *  ai_ml_recommendation: string, REQUIRED.  The AI/ML output: for
      AUS, one of "approve_eligible", "refer", "refer_with_caution",
      "ineligible"; for AVM, the estimated value and confidence
      interval; for servicing, the recommended loss mitigation option.

   *  fair_lending_flag: boolean, REQUIRED.  True if WT-FHFA-02 or WT-
      FHFA-03 triggered.

   *  fair_lending_basis: string, REQUIRED if fair_lending_flag is true.
      The demographic or geographic basis for the flag.

   *  housing_decision_category: string, REQUIRED.  One of:
      "mortgage_origination", "automated_valuation", "loan_servicing",
      "loss_mitigation", "secondary_market_acquisition".

   *  rw_eligibility_verified: boolean, REQUIRED for loans destined for
      GSE sale.  True if WT-FHFA-06 evaluated and confirmed R&W
      eligibility.

   *  fhfa_bulletin_version: string, REQUIRED.  Set to "FHFA-2025-16"
      for deployments under Bulletin 2025-16.

   *  profile_version: string, REQUIRED.  MUST be "VERIDOM-HOMEMARK-
      v1.0".

5.  Representation and Warranty Evidence Architecture

   The GSE R&W framework creates a retrospective evidence requirement:
   years after origination, lenders may face repurchase demands
   requiring demonstration that AI/ML was used correctly.  HomeMark
   Audit Traces provide three specific R&W properties: contemporaneity
   (RFC 3161 timestamp proves the Audit Trace was generated at
   origination, not retrospectively); input data integrity
   (interaction_hash proves the AUS input data has not been altered);
   and AI/ML system version documentation (ai_ml_system_id and
   ai_ml_system_version prove which AUS version was in effect at
   origination).







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   Lenders delivering loans to GSEs SHOULD generate HomeMark Audit
   Traces for all AUS-assisted originations and retain them for the full
   R&W warranty period (typically seven years from the note date or loan
   payoff, whichever is later).

6.  Fair Lending Evidence Package

   The HomeMark profile generates per-loan fair lending evidence (each
   Audit Trace contains fair_lending_flag and fair_lending_basis) and
   aggregate fair lending evidence (the Audit Trace stream can be
   aggregated to compute approval rates by demographic segment,
   disparate impact ratios, and pricing disparities for FHFA Bulletin
   2025-16 monitoring and HMDA analysis).

   The Fair Lending Evidence Package for a defined loan portfolio MUST
   contain: all sealed HomeMark Audit Traces organised by
   housing_decision_category and ai_ml_system_id; aggregate approval
   rate data by fair lending segment; disparate impact ratio
   calculations; count and disposition of WT-FHFA-02 and WT-FHFA-03
   triggers; chain integrity proof (SHA-256 Merkle root); and RFC 3161
   TimeStampToken verification from the OMP Reference Validator
   [OMP-OPEN-CORE].  FHFA examiners can verify completeness and
   integrity without relying on the entity's reconstructed data.

7.  The HomeMark Invariant

   Implementations of this profile MUST satisfy the following two-
   property invariant:

   *  Property 1 (Housing finance decision accountability completeness):
      Every Consequential Housing Finance Decision MUST generate a
      sealed HomeMark Audit Trace containing: the AI/ML recommendation;
      the RI's identity and review timestamp where ASSISTED or
      ESCALATED; the RI's decision and basis where required; the Fair
      Lending Flag evaluation; the AI/ML system identity and version;
      and R&W eligibility verification where applicable.

   *  Property 2 (Immutable trail): The HomeMark Audit Trace MUST be
      sealed with the three-layer integrity architecture defined in
      [I-D.veridom-omp] Section 7.  Any modification to any historical
      Audit Trace record MUST be detectable by FHFA examiners, GSE
      counterparties, or any third-party auditor without access to the
      entity's or OMP implementer's infrastructure.

   An entity satisfying the HomeMark Invariant can demonstrate, for any
   Consequential Housing Finance Decision: the AI/ML recommendation and
   input data; the AI/ML system identity and version; the RI's identity
   and review timestamp; the RI's decision and independent basis; the



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   Fair Lending Flag status; R&W eligibility verification where
   applicable; and that the record has not been altered since sealing.
   This satisfies the transparency, accountability, and model risk
   governance requirements of FHFA Bulletin 2025-16, the fair lending
   examination evidence standards of ECOA and the Fair Housing Act, and
   the R&W compliance evidence standards of the GSE selling and
   servicing frameworks.

8.  Security Considerations

   The security considerations of [I-D.veridom-omp] apply in full.

   Borrower data sensitivity: HomeMark Audit Traces contain borrower PII
   and financial data subject to GLBA privacy requirements.  Operators
   MUST implement GLBA-compliant safeguards.  Fair lending demographic
   data used in WT-FHFA-02 and WT-FHFA-03 MUST be segregated from credit
   decision data consistent with ECOA's prohibition on using protected
   characteristics in credit decisions.

   AI/ML system version integrity: The ai_ml_system_version field is a
   critical R&W and model risk governance element.  Operators MUST
   implement controls ensuring the version recorded matches the AUS or
   AVM version actually in effect at decision time.  Version
   misrepresentation is a material R&W compliance issue.

   Loan identifier uniqueness: The loan_identifier field MUST be
   globally unique within the operator's deployment.  Duplicate
   identifiers would create ambiguity in per-loan evidence retrieval and
   undermine R&W compliance documentation.

   RI identity integrity: ri_employee_id MUST reflect the individual who
   actually reviewed or was accountable for the AI/ML-assisted decision.
   Operators MUST implement technical controls preventing RI identity
   assignment without the relevant individual's authenticated action.

9.  IANA Considerations

   This document has no IANA actions.

10.  References

10.1.  Normative References









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   [I-D.veridom-omp]
              Adebayo, T., Apalowo, O., and F. Makanjuola, "Operating
              Model Protocol (OMP): A Deterministic Decision-Enforcement
              Protocol with Externalized Proof-of-Integrity", Work in
              Progress, Internet-Draft, draft-veridom-omp-00, March
              2026, <https://datatracker.ietf.org/doc/html/draft-
              veridom-omp-00>.

   [RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate
              Requirement Levels", BCP 14, RFC 2119, March 1997,
              <https://www.rfc-editor.org/info/rfc2119>.

   [RFC3161]  Adams, C., Cain, P., Pinkas, D., and R. Zuccherato,
              "Internet X.509 Public Key Infrastructure Time-Stamp
              Protocol (TSP)", RFC 3161, August 2001,
              <https://www.rfc-editor.org/info/rfc3161>.

   [RFC8174]  Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
              2119 Key Words", BCP 14, RFC 8174, May 2017,
              <https://www.rfc-editor.org/info/rfc8174>.

   [RFC8785]  Rundgren, A., Jordan, B., and S. Erdtman, "JSON
              Canonicalization Scheme (JCS)", RFC 8785, June 2020,
              <https://www.rfc-editor.org/info/rfc8785>.

10.2.  Informative References

   [ECOA]     U.S. Congress, "Equal Credit Opportunity Act, 15 U.S.C.
              1691 et seq.", 1974.

   [FHA-1968] U.S. Congress, "Fair Housing Act, 42 U.S.C. 3601 et seq.",
              1968.

   [FHFA-2025-16]
              Federal Housing Finance Agency, "Bulletin 2025-16:
              Artificial Intelligence Governance Framework for the
              Enterprises and Federal Home Loan Banks", March 2026.

   [I-D.veridom-omp-employ]
              Adebayo, T., Apalowo, O., and F. Makanjuola, "OMP Domain
              Profile: Automated Decision Systems Accountability in
              Employment Under California FEHC CRC Regulations, New York
              City Local Law 144, and Related ADS Accountability
              Obligations", Work in Progress, Internet-Draft, draft-
              veridom-omp-employ-00, April 2026,
              <https://datatracker.ietf.org/doc/html/draft-veridom-omp-
              employ-00>.




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Internet-Draft      OMP FHFA Housing Finance Profile          April 2026


   [I-D.veridom-omp-euaia]
              Adebayo, T., Apalowo, O., and F. Makanjuola, "OMP Domain
              Profile: EU AI Act Article 12 Logging and Traceability
              Requirements for High-Risk AI System Operators", Work in
              Progress, Internet-Draft, draft-veridom-omp-euaia-00,
              April 2026, <https://datatracker.ietf.org/doc/html/draft-
              veridom-omp-euaia-00>.

   [OMP-OPEN-CORE]
              Veridom Ltd, "OMP Open Core: Reference Validator and
              Schema Library",  Apache 2.0,
              https://github.com/veridomltd/omp-open-core, 2026.

   [SR-11-7]  Board of Governors of the Federal Reserve System and
              Office of the Comptroller of the Currency, "Guidance on
              Model Risk Management (SR 11-7 / OCC 2011-12)", April
              2011.

   [ZENODO-OMP]
              Adebayo, T., Apalowo, O., and F. Makanjuola, "OMP --
              Operating Model Protocol: A Deterministic Routing
              Invariant for Tamper-Evident AI Decision Accountability in
              Regulated Industries", Zenodo DOI 10.5281/zenodo.19140948,
              March 2026.

Authors' Addresses

   Tolulope Adebayo
   Veridom Ltd
   London
   United Kingdom
   Email: tolulope@veridom.io


   Oluropo Apalowo
   Veridom Ltd
   Awka
   Nigeria
   Email: ropo@veridom.io


   Festus Makanjuola
   Veridom Ltd
   Toronto
   Canada
   Email: festus@veridom.io





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