When planning a financial model, developers need to take measures to understand the various risks within the model. Since models eventually undergo real-world deployment, model risk management (MRM) services help pinpoint any possible shortcomings or failures in the model’s performance.
Developing and maintaining a model with continuous MRM can significantly help enterprises with the following:
- Understand and predict customer behaviour
- Determine funding requirements
- Assess capital adequacy
- Make effective investment decisions
- Manage data analytics
The SR 11-7 put forward by the U.S. Federal Reserve Bank in 2011 laid out guidelines for enterprises seeking model risk management. These MRM guidelines helped ensure that the model’s development happened in a disciplined manner with a strong knowledge base to be appropriately implemented.
With this development in model risk management services, there are now several clear markers of MRM constantly evolving with real-time changes.
The Evolution of Model Risk Management
The earlier approach to MRM relied on various conservative estimates that failed to understand the capital allocation a model would need adequately. However, breaking free of these habits can lead to mature MRMs.
Mature MRMs significantly boost an institution’s earnings. When using realistic assumptions, the uncertainties and dynamics of a model become more apparent, allowing for more appropriate mitigation strategies. Such systematic cost reduction is only possible with comprehensive model risk management services.
End-to-end services that optimise and automate various processes can reduce model costs by up to 30%. Additionally, many banks want to manage a model-validation budget that can support such an industry. With such a turn towards mature MRMs, here are some steps that follow.
Building a Foundation
The first step in model validation is creating the basic infrastructure. This includes understanding your MRM’s objectives and scopes through various policies, comprehending the models, and managing risk by charting out the model’s life cycle.
Here, governance and standards also come, regulating the model’s life cycle and the board and senior management. Governing involves defining model development standards, inventory and validation, alongside other roles and responsibilities for people involved in the project.
Introducing a Program
After the foundation, banks include model risk management services that create transparency for senior stakeholders. Banks usually use detailed templates for various aspects of the MRM program, with scorecards to track the risk evolution over time. Such vigilance helps prioritisation and promotes efficiency if all model submissions follow the guidelines in the validation process.
Mature MRMs focus on maximising value while reducing the cost of managing model risk. A large percentage of financial institutions consider incomplete model submissions to be the more significant hindrance to validation timelines.
Therefore, having standards for model inventory and validation promises transparency and better model quality. Institutions can also monitor process efficiency through key metrics with a good workflow system in place. Standards help in developing accurate, cost-effective models through big data and automation.
Model risk management services now understand where to focus MRM function for high value. This understanding reflects better, faster operations and value-based MRMs that can be implemented on an enterprise-wide basis.