The Federal Reserve, Office of the Comptroller of the Currency, and Federal Deposit Insurance Corporation issued SR 26-2 on April 17, 2026, replacing the 2011 model risk management framework that has governed bank model governance for fifteen years. The revised guidance supersedes SR letter 11-7, "Guidance on Model Risk Management" (April 4, 2011), and SR letter 21-8, "Interagency Statement on Model Risk Management for Bank Systems Supporting Bank Secrecy Act/Anti-Money Laundering Compliance" (April 9, 2021).

The letter is addressed to the Officer in Charge of Supervision at each Federal Reserve Bank and is expected to be most relevant to banking organizations with over $30 billion in total assets regulated by the Federal Reserve.

What Changed and Why

The original SR 11-7 was issued in 2011 when bank models were primarily statistical credit scoring tools, loan loss reserve calculations, and interest rate risk models. The fifteen years since have produced a fundamentally different model landscape: machine learning credit decisioning, AI-assisted fraud detection, natural language processing for BSA/AML compliance, large language models for regulatory reporting, and agentic AI systems beginning to operate inside bank workflows.

The agencies state the revised guidance reflects supervisory experience and industry feedback accumulated over the past fifteen years, as well as significant advancements in modeling practices. The revision emphasizes a risk-based approach to model risk management that is tailored to a banking organization's model risk profile and the size and complexity of its operations.

The practical shift is from a prescriptive framework to a principles-based one. SR 11-7 established specific requirements around model development, validation, and governance. SR 26-2 maintains those structural requirements while explicitly recognizing that model risk management practices appropriately vary among banking organizations based on their specific risk profiles and model usage.

What This Means for Bank-Owned Equipment Finance Programs

The applicability threshold of $30 billion in total assets covers every major bank-owned equipment finance program in the U.S. bank channel. Bank of America, Wells Fargo, U.S. Bank, PNC, Huntington, TD Bank, City National Bank, Fifth Third, EverBank, and M&T Bank all exceed this threshold. Their equipment finance credit decisioning models, lease pricing models, residual value models, and portfolio stress testing models fall within the scope of SR 26-2.

The equipment finance implications are specific:

  • Credit scoring and decisioning models used to approve or decline lease and loan applications at bank-owned programs are subject to the revised governance requirements. If those models include machine learning components, the validation and documentation standards now explicitly address that complexity.
  • Residual value models used to price operating leases and set end-of-term values are quantitative models under the SR framework. Banks that have not formally validated these models under the SR 11-7 standard will need to assess compliance with SR 26-2.
  • BSA/AML models at bank-owned lessors are directly affected. The 2021 SR 21-8 guidance, which SR 26-2 supersedes, was specifically written for BSA/AML model risk. The revised framework consolidates that guidance into the broader model risk management structure.
  • AI and machine learning tools being deployed in equipment finance origination, underwriting, and portfolio management at bank-owned programs now have a clearer regulatory framework. SR 26-2 explicitly addresses the model risk implications of AI systems in a way SR 11-7 did not.

The Gap

The equipment finance industry press has not covered SR 26-2. The letter was issued on April 17, 2026, and as of the date of this article, no equipment finance publication has analyzed its implications for bank-owned programs.

The gap is consequential. Banks subject to SR 26-2 will be conducting model inventories, validation reviews, and governance assessments that directly affect the credit models, pricing models, and compliance systems used in their equipment finance operations. Independent lessors and brokers that rely on bank program capacity should understand that their bank partners are operating under a revised regulatory framework for every quantitative model in their credit and compliance stack.

Inference: The consolidation of SR 11-7 and SR 21-8 into a single principles-based framework, combined with explicit acknowledgment of AI and machine learning model risk, suggests the agencies anticipate a significant expansion of the model universe at regulated banks over the next supervisory cycle; equipment finance credit and pricing models that were not formally inventoried under SR 11-7 may now require documentation and validation under SR 26-2.