Model Risk Management: Current Advancements, Challenges, and Future Trends

Model Risk Management: Current Advancements, Challenges, and Future Trends

In the rapidly evolving finance and technology landscape, effective model risk management (MRM) is more critical than ever. Financial institutions, regulatory bodies, and technology pioneers are continuously innovating and refining approaches to ensure the robustness, transparency, and adaptability of their model risk management frameworks. However, challenges do remain, driven by the intermittently volatile macroeconomic landscape and a notable surge in event risk. Additionally, a rapid shift toward digitization and a significant wave of acquisition activities, is impacting future business initiatives as well. Faced with these hurdles and an uneven economic rebound, numerous financial institutions have sought to harness advanced analytics and technology capabilities across various business processes. In this article, we explore the latest advancements and approaches in model risk management, as well as key challenges that are shaping the future of model risk assessment across industries.

Current Trends

The MRM landscape is constantly evolving, with some of the largest developments occurring in areas like model documentation, real-time monitoring, efficient model inventory tracking, integrated stress testing and scenario analysis, third-party outsourcing and collaboration.

Comprehensive documentation is a critical means of preserving model transparency, accounting for not only technical details but also ethical considerations, data sources, and potential biases inherent to a model. As reliance on third-party model development and validation increases, the risks associated with obscure model development call for robust documentation to verify the reliability and transparency of externally developed models. In certain cases, vendor model documentation may fall short, transferring the risk management responsibility to users — who must be prepared to take on the role. From a model governance perspective, this requires real-time monitoring of these opaque, third-party models, giving users the ability to react and adapt to changing conditions or potential hazards before they escalate. These instances also underscore the need for collaborative approaches to model validation between institutions and external entities, including regulators and independent auditors, as standards continue to evolve.

The fast-growing model landscape within organizations means they will also need to find ways to integrate inventory tracking, stress testing, and scenario analysis into the model validation process through the latest technology paradigms such as ModelOps. This provides a structured approach to operationalizing all artificial intelligence and machine learning models that are tracked in the inventory. But tracking and testing becomes increasingly complex as model dependencies grow, with a series of upstream models impacting downstream model outcomes. To solve this problem, Blockchain’s decentralized and immutable features can help provide secure and transparent tracking of models throughout their lifecycle. Additionally, AI-driven stress testing programs can simulate complex scenarios and assess the resilience of models in dynamic and unpredictable environments.  

Challenges and Considerations

Ethical considerations in model development are a top priority for model validators, followed closely by model risk acknowledgment and communication. In the realm of ethics, addressing biases in model development is a primary concern, especially in models where inherited social biases may generate inequitable outcomes. Interpretability poses another challenge. Achieving the right balance between AI model complexity and interpretability is crucial for gaining stakeholder trust and making sure that users understand model outputs. Many institutions are investing in technologies and processes to detect and mitigate biases and improve interpretability in pursuit of fair and unbiased outcomes. 

While these can be useful measures to increase model adoption, they will be difficult to realize without additional focus on model risk acknowledgment and communication. The first step in this direction is to gain a full understanding of regulatory requirements, which can pertain to data privacy and ethics, model transparency, monitoring and governance. This should be followed by establishing an effective communication model to relay risks to stakeholders in a transparent manner. Institutions should invest in accessible communication strategies by adopting technologies that facilitate clear and compliant communication of model risks to regulators and other stakeholders. This helps convey complex model-related information to diverse audiences and addresses regulatory compliance needs.

An uptick in new model types, such as climate risk and CECL models, also introduces new complexities and risks due to the scarcity of historical data. These models often depend on external factors and data sources, driving a need to integrate qualitative methodologies and experienced judgment. The effectiveness of these models depends on strong scenario-building techniques and dependable data, so it is crucial that practitioners have access to specialized knowledge and skills in validating climate risk.

Future Trends in Model Risk Management

The MRM space is set to see both new technologies and new complexities enter the scene in the near future. These changes are poised to impact three key areas of the practice:

  • Gaining Efficiencies: Model risk management practices offer a wide range of applications that could benefit from groundbreaking technologies like Generative AI — streamlining labor-intensive tasks like documentation and reporting, for example. Technology-driven initiatives can also help enhance processes in inventory management and continuous monitoring. However, initial implementation could face challenges, including regulatory uncertainties, concerns about data confidentiality, and considerations of accountability. Avoiding these pitfalls will require companies to employ strategic approaches when integrating secure large-language model APIs for internal use. MRM leaders can work proactively to help minimize overlaps, optimize processes, and cultivate a risk-aware culture throughout their organizations. Such strategic efforts will be important as MRM expands into diverse areas like climate, cyber, sales and marketing, and human resources.
  • Continuous Monitoring: The current trend towards continuous monitoring of models is expected to intensify. Institutions will leverage real-time data and AI-driven analytics for frequent testing to maintain a proactive and agile approach to risk management. Regulatory expectations are also evolving to prefer a real-time and adaptive approach to model risk management. Regulators increasingly emphasize the importance of vigilance and promptness in addressing issues and tracking emergent risks. Continuous monitoring aligns with these expectations, demonstrating a commitment to robust risk management practices.
  • Explainable AI (XAI): Explainable AI is expected to play a pivotal role in enhancing the transparency of AI models. Innovations in XAI, driven by the need to understand outcomes at individual decision levels, will contribute to a better understanding of complex models. For this reason, XAI will likely become a crucial ingredient to building stakeholder trust, enhancing regulatory compliance, helping address ethical considerations, and facilitating effective collaboration between humans and AI systems. As AI adoption accelerates across all industries, the importance of explainability will only grow, shaping responsible and ethical deployment of AI technologies in the future.

How Can BDO Help

BDO’s comprehensive MRM approach offers a practical framework for identifying, quantifying, and mitigating model risk that is consistent with industry and regulatory guidelines. Our MRM team can perform risk and efficiency assessments, help define MRM strategy and remediation plans, work to enhance governance procedures in collaboration with internal risk teams, and support model lifecycle management through development, implementation, and validation. Our ModelOps-driven approach makes it easier for institutions at any stage to implement MRM requirements by defining explainability requirements for developers, benchmarking AI and machine learning models against simpler approaches, inventorying models, and helping to enhance monitoring standards.

Contact BDO’s Valuation & Capital Market Analysis team to learn more about how to develop your model risk management program.