Financial institutions rely on a range of models for their operations. Models are used for diverse activities, from valuing a financial instrument to determining the credit risk associated with a borrower. However, the use of models for different operations can lead to numerous risks. Poorly developed models can generate inaccurate results. Similarly, model risks might arise due to the incompleteness of input data. While regulatory frameworks impose several norms for financial institutions, effective model risk management is still a challenge. Poor model risk management strategies can lead to penalties or sanctions for the financial institution. Read on to understand how to navigate the evolving landscape of model risk management.
What Exactly is Model Risk Management?
As discussed above, financial institutions rely on diverse models to continue operations. Models are mathematical representations of real-world scenarios used for different operations. Corporate banks, investment banks, fund houses, insurance firms, hedge funds, and other financial institutions actively use models for different processes. For instance, many lending institutions rely on credit-scoring models for borrowing activities. A credit scoring model can help determine the creditworthiness of the borrower based on the repayment history, income, and other factors. Similarly, financial institutions rely on valuation, option pricing, regression, time series, classification, cluster analysis, decision support, and other models.
Model Risk Management refers to identifying and eliminating the issues associated with the use of models for operations. Since models are mathematical representations, there can be errors or misleading results. Model risks can lead to inaccurate results, loss of revenue, and compliance issues. Regulatory authorities have imposed several rules related to model risk managements. Here are some challenging model risks for financial institutions:
- Models might produce inaccurate results at times, thus opening doors to different risks. Models might provide inaccurate results due to drawbacks in assumptions, methodologies, or input data.
- Many organizations experience model risks due to incomplete data. Poor-quality or incomplete data can lead to biased results for financial institutions.
- There might be mathematical errors leading to model risks. Also, financial institutions might experience model risks due to errors in the coding or model development process.
- Organizations test different models under different circumstances before implementation. However, poor testing and development processes can lead to model validation risks. Poor validation will not help professionals detect the drawbacks or errors.
Navigating the Evolving Landscape of Model Risk Management
Regulatory authorities expect financial institutions to implement strict model risk management strategies. There are strict regulations in place for the same reason. However, the reality is far more different than the expectations of regulators. Many financial institutions fail to manage model risks, thus leading to penalties, inaccurate results, loss of revenue, and more. Here’s how to navigate the evolving landscape of model risk management:
Understand the Regulatory Landscape
Asset managers, brokers, loan underwriters, and other finance professionals must be familiar with the regulations related to model risk. The idea should be to integrate model risk regulations in day-to-day operations. However, it can only happen when the organization updates its strategies with the changing regulations. You must be familiar with DFAST, SR 11-7, CCAR, and other regulatory frameworks.
Conduct Regular Assessments
Financial institutions must conduct a thorough assessment of their models, development strategies, testing/validation policies, and other factors. There must be a clear understanding of the model requirements of the organizations. Also, in-house employees must be familiar with the strengths and weaknesses of models. Let us say a valuation model cannot produce results for a large sum, which can be called ‘out of bounds data’. In-house employees must be familiar with this thing to avoid inaccurate results.
Partner With a Reliable Third Party
It is challenging for in-house employees to keep track of evolving model risk management regulations. It becomes even more difficult for a financial institution operating in multiple jurisdictions or countries. Luckily, firms can partner with third parties for effective model risk management services. These third parties have dedicated teams of experts for model development, deployment, validation, and other processes.
In a Nutshell
Model risk regulations are getting stricter, and organizations must find a way. It is better to outsource model risk management processes to a reliable third party like Acuity Knowledge Partners. It will allow in-house employees to focus on core competencies while reducing the costs related to model risk management. Implement strict risk management strategies now!
