The role of AI governance in model risk governance

Discover AI governance as a necessary framework for managing risk in the modern world.

Table of contents

· Introduction
· Model Risk Governance Definition:
· Critical Role of Chief Risk Officers:
· Challenges in Model Risk Governance
· Solution
· Conclusion

Introduction

Most banking, financial services, and insurance organizations rely on mathematical models and computer programs to make crucial financial decisions. However, these models and programs must be checked for accuracy to prevent flawed or misleading decisions.

In an ever-evolving financial landscape, Risk Managers face numerous challenges. The case for AI governance is becoming increasingly clear as a more modern approach to risk management. By implementing AI governance, organizations can ensure that they make informed decisions based on accurate and reliable data.

As the financial industry continues to evolve, staying ahead of the curve is crucial. AI governance provides a necessary framework for managing risk in the modern world.

Model risk governance defined

Most banking, financial services, and insurance organizations use a variety of mathematical models and computer programs to make financial decisions. They have dedicated risk management functions led by Chief Risk Officers (CRO) or an equivalent role to check if those models and computer programs used for financial decisions work correctly. Such processes are called model risk governance, which aims to protect the company from making poor financial decisions based on flawed or misleading models.

The critical role of Chief Risk Officers

Most CROs are responsible for building processes and controls to assess the risk of using mathematical models and computer programs in financial decisions. They deploy regular monitoring to understand such financial models’ accuracy and reliability, including identifying potential errors and limitations. The CRO team acts as the nexus point between risk management experts and the rest of the organization. Their mission is to oversee and advise on all risk types, establish risk frameworks and stress testing scenarios, and execute risk programs, including operational monitoring.

A key objective for model risk governance teams is to ensure the company targets an appropriate balance between risk and return, mitigating unnecessary risk and protecting the company’s financial assets.

Challenges in model risk governance

For many organizations, data used in risk model governance is spread across various architectural settings, which presents unique challenges faced by Risk Management executives. A few examples include:

• Framework development: The design and development of risk frameworks require expertise to understand the data associated with the financial and non-financial assets of the company. This process is usually labor-intensive when there is a lack of data visibility across the enterprise.
• Risk models governance: A lack of a centralized place to accurately define and manage enterprise risk models. Risk experts need a shared collaborative environment to describe the business context appropriately.
• Complex data relationships: Risk model data is complex and usually spread across various IT architecture settings.
• Common business language: Understanding various risk types (e.g., operational risk, cyber risk, etc.) requires speaking the same business language to have a shared meaning and understanding by different risk management experts.
• Data quality & consistency: The outcome of stress testing scenarios depends on data quality. Missing data values can create blind spots in testing scenarios, especially when understanding the financial model’s limitations.
• Cross-functional collaboration: Risk experts act as a nexus point between the Risk Management function and the rest of the organization, which includes highly technical teams, data analytics stakeholders, and functional stakeholders. A lack of a shared platform for collaboration can be a blocker to getting valuable input from across different teams involved in the development of risk models.

Solution

AI and data governance presents an exciting solution for risk management executives. It delivers the promise of creating a central data inventory and bringing stakeholders into a shared environment to collaborate on enriching data with trust attributes. This data is then easily accessible for the risk management function or any data team needing trusted data for various business activities (e.g., creating reports, performing predictive analytics, regulatory compliance, and much more).

As large language models (LLMs) get well understood by organizations, they underline the critical role of data governance, which is crucial in the context of risk model governance, facilitating AI adoption more effectively.

• Contextual understanding & taxonomy: Data governance provides essential context and taxonomy, enabling LLMs to generate accurate responses required to understand data related to financial and non-financial assets. This foundational work ensures that AI technologies can understand and categorize information accurately, vital for their effective operation in risk model governance.
• Trust & data quality: The concept of trusted attributes, where data is categorized into bronze, silver, and gold categories, is vital. AI is crucial in reconciling data into different categories and optimizing daily operations. Moreover, AI can help construct risk indicators for various types of data, enhancing decision-making processes.
• Governance as the foundation for AI integration: Effective governance is the bedrock for integrating AI across systems, extending all the way to developing proper risk associated with financial models. AI implementations might not reach their full potential without solid governance frameworks, underscoring the necessity for robust data governance in guiding AI adoption and application.
• Productivity of highly technical teams: AI can help technical teams collaborate with Risk Management functions by automating routine support and coding tasks in daily operations, including faster data analysis. Effective logic scenarios and generating better code are supported by AI, and experiments with AI show that technical teams can deploy code to identify PII and classify data pretty well.

Conclusion

The challenges in model risk governance can be overcome by having a sound data governance program backed by AI, wherein the AI capabilities can help organizations categorize large volumes of data and generate accurate responses when evaluating associated risks.

One of the biggest challenges for risk management experts today is dealing with data complexity, wherein data is spread across various architecture settings that lack appropriate business context. Deciphering data and adding business context are labor-intensive tasks, but when automated with AI governance, they can bring significant value to risk management teams. Hence, it ensures that the company targets an appropriate balance between risk and return, mitigating unnecessary risk and protecting its financial assets.

Kash Mehdi is an experienced and passionate leader working with the industry’s first and fastest-growing data knowledge governance catalog, DataGalaxy. Over the past decade, Kash has focused on scaling go-to-market functions for hyper-growth SaaS companies and guided organizations through the complexities of data orchestration to help them empower humans with trusted data.

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