AIRE (AI Risk & Explainability) is a governance framework that helps compliance teams document, monitor, and demonstrate oversight of AI systems without touching the models themselves. The package brings together policy, procedures, and practical tools that regulators expect, enabling compliance officers to operationalize AI oversight immediately and defend their programs during examinations.

With AIRE, you’ll receive:

• AI Governance Policy Template

• A regulator-ready policy template defines purpose, scope, key terms, governance framework, roles and responsibilities, training and awareness, record retention, independent review, vendor oversight, escalation authority, and board reporting. It references OCC, SR 11-7, DFS (if applicable), FATF, ECOA, and the EU AI Act, where applicable, to establish a solid governance foundation.

• AIRE Framework and Oversight Procedure

• The manual AIRE framework is provided as a fillable workbook with tabs for Dashboard, Risk-Rating, Testing, Assignment, Input Control, Evidence, Fairness Testing, and Examiner Packet Summary. A companion User Guide explains how to complete each tab. Together, these serve as the procedural backbone for documenting and demonstrating effective control.

• Model Risk Crosswalk

• A one-page reference maps AIRE to existing regulatory expectations, such as SR 11-7, OCC 2011-12, FATF, DFS, and ECOA. This makes clear that AI oversight is not discretionary, but rather an extension of established model risk and consumer protection standards.

• Training Module

• A short training deck or e-learning script introduces staff to AIRE. It explains definitions, roles, responsibilities, and oversight red flags, fulfilling the training and awareness requirement outlined in the governance policy.

• AI Vendor Due Diligence Checklist

• A fillable checklist helps clients evaluate AI vendors. It covers explainability, fairness testing, version control, data sources, and governance documentation, ensuring AI oversight integrates with existing vendor risk processes.

• Fairness Testing Starter Kit

• As part of Phase 2 expansion, clients receive templates and scenarios to begin fairness testing. Examples include disparate loan approvals by ZIP code or chatbot language bias. This allows clients to demonstrate proactive fairness oversight before deploying advanced analytics.

• Implementation Roadmap

• An advisory guide offers a structured rollout plan over ninety days. Phase one fills the workbook with pilot use cases. Phase two incorporates fairness testing. Phase three prepares the examiner's packet. This roadmap makes governance a turnkey, step-by-step process.

• Optional Oversight Service

• Clients may engage RayCor for quarterly independent reviews. These reviews result in a findings memo and sign-off that can be shown to regulators as independent validation, providing added assurance and satisfying supervisory expectations for model validation.

Organizations typically engage AIRE when there’s:

• An upcoming regulatory exam

• A Sponsor bank onboarding or review

• AI expansion into credit/fraud/onboarding

• Fairness or complaint issues

• Investor diligence

• Internal audit findings

AIRE is usually adopted just before oversight pressure hits, or just after a scare.

AIRE is deployed based on the organization’s AI use cases, regulatory exposure, and oversight needs. Most engagements begin with an AIRE Readiness Consultation to scope governance coverage and determine the appropriate delivery model.

Reach out to info@raycorconsulting.com with any additional questions or concerns, and we'll be happy to help.

AIRE is part of RayCor’s broader governance advisory platform supporting fintechs, payments companies, and digital asset firms operating under U.S. regulatory oversight.