June 30, 2024

The Impact of GenAI in Model Risk Management (MRM)

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Ask anyone working in financial technology today where they see the most significant opportunities, and you’re likely to hear the phrase generative AI (GenAI) — especially when it comes to model risk management.

Artificial intelligence is experiencing significant transformation, with an unprecedented rate of technological advancement driving innovation across industries. Over the past few years, we’ve witnessed the shift from traditional AI to generative AI offering broader capabilities and the need for new approaches to data, governance, and user interaction. This advancement has been a game changer in the financial services industry, where the stakes are high, and the potential benefits of AI are immense — and increased risk lurks at every turn.

ValidMind head of AI Risk Management Kristof Horompoly recently hosted an in-depth webinar on “The Impact of GenAI in Model Risk Management,” which simulcast to LinkedIn and YouTube. Horompoly discussed the transition from traditional AI to GenAI, particularly focusing on its implications, governance, and deployment within organizations, specifically in financial services. The webinar highlights the importance of thoughtfully and methodically implementing, using, and governing AI — plus, the changes in data requirements, the broad capabilities of GenAI, and the significant governance challenges it poses.

YouTube video

The Big Deal with GenAI for Financial Services

GenAI, exemplified by large language models (LLMs) like GPT-4, differs fundamentally from classical AI. Traditional AI models were designed to solve specific tasks using relatively small, task-specific datasets. For instance, a financial institution might use one model for fraud detection, another for credit scoring, and yet another for marketing. These models were precise but limited in scope.

In contrast, GenAI models are trained on vast and diverse datasets, enabling them to perform a wide range of tasks. This shift from “small data” to “big data” changes the game. The sheer scale and variety of data used in training GenAI models mean that these models can tackle numerous applications simultaneously, from answering customer queries to generating financial reports. However, this versatility comes with its own set of challenges.

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Governance Challenges in GenAI

One of the most pressing issues with GenAI is governance. Traditional AI models, even complex neural networks, allowed for some level of explainability. Financial institutions could create proxy models to understand the key features driving their decisions. With GenAI, this transparency is much harder to achieve. The models operate as black boxes, making it difficult to understand their decision-making processes.

This lack of transparency raises significant governance challenges. Financial institutions must find ways to manage and mitigate the risks associated with deploying these powerful but opaque models. This involves narrowing the scope of GenAI applications to manageable, well-defined use cases. By focusing on specific applications, organizations can better contain and manage the risks.

The Importance of Human Oversight

Another critical aspect of using GenAI responsibly is ensuring human oversight. Generative AI models, while powerful, are not infallible. They can produce convincing but incorrect or inappropriate outputs, known as hallucinations. They can also generate toxic or biased content. Therefore, it is crucial to maintain a human-in-the-loop approach, particularly for sensitive applications.

Ideally, this oversight should come from experts who understand the intricacies of the models and the data they process. However, given the broad range of potential users, from technical experts to non-technical employees, educating end-users about the limitations and risks of GenAI is essential. Users need to be aware of the potential for errors and trained to recognize and address them.

Collaboration Across Disciplines

Effective governance of GenAI requires collaboration across various disciplines within an organization. Legal, compliance, and control teams need to be involved in the review and approval process for AI applications. This interdisciplinary approach ensures that all potential risks, from legal implications to data privacy concerns, are thoroughly considered and managed.

This collaborative governance model is more complex and time-consuming than traditional approaches. Still, it is necessary to ensure the safe and responsible use of generative AI. Over time, as organizations develop and refine their governance frameworks, these processes will become more streamlined and efficient.

Feedback and Continuous Improvement

Implementing robust feedback mechanisms is another vital strategy for managing generative AI. Allowing users to provide quick feedback on AI outputs helps organizations identify and address issues promptly. Whether it’s a thumbs-up/thumbs-down system or more detailed feedback forms, these mechanisms enable continuous improvement and ensure the AI models become more reliable and effective over time.

Watch our previous webinar | Navigating MRM Challenges: Strategies for Compliance

Industry Trends and Future Outlook

The adoption of GenAI in financial services is accelerating. Initially, organizations were hesitant to deploy these models due to the high risks and regulatory uncertainties. However, as the technology has matured and its potential benefits have become clearer, there is a growing expectation for financial institutions to incorporate GenAI into their operations.

Despite the challenges, the industry is moving towards broader adoption of generative AI. The development of robust governance frameworks and the increasing involvement of regulators in shaping best practices will likely drive this trend forward. In the next 6 to 12 months, we can expect to see more GenAI applications moving into production as organizations gain confidence in their ability to manage the associated risks.

Moving Forward with GenAI in Financial Services

The shift to GenAI represents an exciting but challenging new chapter for the financial services industry. By focusing on well-defined use cases, ensuring human oversight, fostering interdisciplinary collaboration, and implementing robust feedback mechanisms, organizations can harness the power of generative AI while effectively managing its risks. As the industry continues to evolve, the successful integration of generative AI will depend on the development of mature governance frameworks that balance innovation with responsibility.


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