Publications

Right Sizing Model Risk Management for Financial Institutions Between $10-50 Billion

Whitepapers

This technical report, titled "Right Sizing Model Risk Management for Financial Institutions Between $10-$50 Billion," provides model risk management (MRM) practices suited for smaller banks and credit unions with assets between approximately $10 and $50 billion. It was published by the Model Risk Managers' International Association (MRMIA), a non-profit industry advocacy group focused on promoting MRM best practices.

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The Role of an Audit Practitioner with Model Risk

Whitepapers

Internal audit has an important role in the assessment of the model risk. Within its assessment, it should conduct a comprehensive review and testing specific to the compliance of model risk policy and procedure. Any significant findings resulting from audit should be reported to the Board.

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MRMIA Roundtable March 2025

Whitepapers

Some of the largest fair lending fines in history have been issued by U.S. regulators:

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IMPACTS OF COVID-19 ON MODEL RISK MANAGEMENT

Whitepapers

The COVID-19 pandemic provided both a test of the lending industry’s model risk management practices and an education into issues not seen before. Following the SARS outbreak in 2002 in China and Hong Kong, many institutions globally started using a pandemic as a stress testing scenario, but the reality has been more complex than any prior stress test.

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Request for Information and Comment: Financial Institutions' Use of Artificial Intelligence, including Machine Learning

Whitepapers

An increasing number of business decisions within the financial industry are made in whole or in part by machine learning applications. Since the application of these approaches in business decisions implies various forms of model risks, the Board of Governors of the Federal Reserve System, the Bureau of Consumer Financial Protection, the Federal Deposit Insurance Corporation, the National the Credit Union Administration, and Office of the Comptroller of the Currency issued an request for information and comment on the use of AI in the financial industry.

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Managing Artificial Intelligence Risk in Small to Mid-Size Banks: A Practical Governance Playbook

Whitepapers

As banks explore the purchase, development, and use of AI systems to assist in everyday tasks, they quickly discover that these powerful tools come at a price of increased risk. Some risks, such as sustainability, are well understood and can be managed through established practices for management and control, such as training, access controls, and change management.

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MACHINE LEARNING AND MODEL RISK MANAGEMENT

Whitepapers

An increasing number of business decisions within the financial industry are made in whole or in part by machine learning applications. Since the application of these approaches in business decisions implies various forms of model risks, this white paper tries to answer questions such as

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Effective Generative AI Model Risk Management

Whitepapers

This paper outlines a model risk management (MRM) framework tailored to Large Language Models (LLMs) in finance and insurance. While existing MRM principles remain relevant, their application must evolve. Traditional models are validated against fixed datasets and exhibit stable behavior, but LLMs generate dynamic, unpredictable outputs that demand continuous oversight rather than static validation.

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Principles for Model Risk Management

Whitepapers

Models sit at the centre of modern decision-making, risk management, and operational processes. From credit scoring and capital planning to fraud detection and customer personalization, they help organizations turn data into actionable insights. However, as organizations rely more heavily on models — and as these models become increasingly complex — the risks they carry also grow. Poor design, inadequate validation, misuse, or neglect can undermine not only a model’s performance but also stakeholder trust and regulatory compliance.

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MRMIA’s Standard on Vendor Model Documentation Standard

MRMIA' Standard

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Short Description

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