The investment management industry is increasingly using models and complex algorithms to enhance the speed and accuracy of decision-making. Hence, investment managers are becoming more interested in comprehending the sources of model risk and standing up model risk management frameworks—while also considering new risks associated with generative AI models and digital assets.
Models are pervasive in the investment management industry, and they are used to facilitate important business activities, such as asset allocation, algorithmic trading and portfolio rebalancing, market and liquidity risk management, and regulatory compliance. While models help organizations drive competitive advantage and achieve operational efficiencies, not managing them effectively can lead to flawed predictions and erroneous decisions—which in turn can erode investor trust and damage the reputation of investment firms.
Given the increasing attention to models by investors, stakeholders, and regulators, investment managers need to proactively design an effective model risk management framework to mitigate the strategic, regulatory, and operational risks for businesses.
Model risk can be understood as the risk of experiencing monetary loss, harm to clients, erroneous performance or risk metrics, improper investment or managerial decisions, or damaged reputation resulting from poorly built, used, or controlled models. To mitigate the model risk, model risk management (MRM) is a discipline of risk management that provides a structured approach across the model life cycle. It can help organizations define the shared roles, responsibilities, and accountabilities (inclusive of decision rights) across business functions, and facilitate the development of an effective control environment, including policies, procedures, and corollary controls.
Rigorous model development processes, a broad model testing and evaluation approach, and an effective model operations framework serve as the cornerstone for an organization’s robust model environment. We explore leading practices to enhance model resiliency with a focused lens on artificial intelligence (AI) models.
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Organizations that aim to manage generative AI risks should consider beginning by managing the risks already identified with “traditional” AI. These risks can be mitigated by addressing model risks such as the potential for bias in data or models, or lack of accuracy of the output. This is in addition to ethical considerations, data privacy, and safety issues. Investment firms need to determine the proper oversight, validation, and monitoring of generative AI systems to maintain transparency, fairness, and accountability in their operations.
By embracing generative AI technologies responsibly, investment management firms can gain a competitive edge, provide more value to clients, and adapt to the evolving landscape of the industry.
As institutional interest in investing in digital assets continues to rise, there are additional financial risk management challenges to consider. Market risk models designed to evaluate the risks and returns of traditional financial assets do not address the idiosyncrasies of risk factors of cryptocurrency and digital assets as an alternative asset type. In addition, limited liquidity and fragmented markets for certain cryptos with smaller market size make valuation methodologies inadequate to estimate the market value for instruments based on digital assets.
By recognizing the importance of model risk management and taking appropriate actions, investment managers can navigate the complexities of the industry, adapt to changing market dynamics, and strive for sustainable long-term achievements. Download our report to learn more.