10 Common Pitfalls to Avoid When Implementing Model Risk Management in Financial Services Companies
Financial services companies routinely confront a variety of risk factors, threats, and vulnerabilities as part of their risk management and mitigation efforts. In fact, the financial sector is amongst the most highly targeted by criminals and it’s amongst the most highly regulated in terms of regulatory compliance. Beyond all of this, there is also the matter of model risk management — a risk factor that is both unavoidable and inherent to the financial business model.
Effectively managing model risk, however, is very important for a financial services company’s risk management strategy. These in-built risk factors can prove devastating if an organization ignores the issue, leading to significant financial losses and adverse impacts on the company’s operations, clients, and interests. Recognizing the need for model risk management measures is one thing; actually implementing an action plan is something else entirely. This begs the question: what are the most common pitfalls to avoid when implementing model risk management measures?
What is Model Risk Management?
To fully appreciate the pitfalls of model risk management and mitigation implementation, you must have a good understanding of the underlying dynamics such as what model risk entails.
At the most basic level, model risk arises when a company or organization — such as a bank, lender, or financial services provider — sustains losses as a result of a faulty model. For instance, a financial services provider may use a model to guide or inform the development of a new policy, strategy or as part of a data-driven decision-making process. But if there is an inaccuracy or flaw in the model — or an error in the interpretation of that model — then anything that is derived from that model will also be flawed. Therefore, that financial services company may see losses because a data-driven decision was based upon an erroneous model. That is model risk and it can have serious effects on a business, especially if the error or flaw is not readily detected.
Model risk must be considered a critical aspect of a sound risk management strategy, especially in a high-risk industry such as the financial services space. By understanding the potential pitfalls, you can take measures to mitigate risk and avoid common challenges that may interfere with your organization’s model risk management efforts.
10 Common Pitfalls of Implementing a Model Risk Management Initiative
There is no shortage of potential pitfalls that may come into play as your financial services company implements measures to mitigate model risk and related vulnerabilities. These risk factors tend to be rather challenging to deal with simply due to their inherent and unavoidable nature, but a good understanding of the potential pain points will position your organization to be more proactive in avoiding pitfalls. Here is a look at some of the most common model risk management pitfalls.
Failing to Double-Check Your Models’ Integrity
Never assume that a model is free of errors or flaws, even if you’ve performed a manual check. A double-check should be the standard when evaluating models so as to ensure that your organization is working with viable, valid, and accurate models as the basis of your strategy, policy, etc.
Failing to Identify Incomplete or Partial Data Sets
A model is typically created using one or more data sets as its basis. A partial or incomplete data set will result in a flawed model that lacks integrity because you’re only considering a portion of the pie. Depending on how much data is missing, the resulting model could be significantly different if you were to work from a complete data set. For this reason, it’s extremely important that you verify the integrity of your models by confirming that all are based upon full, complete data sets.
Failing to Determine the Age of the Data Used to Generate Your Model(s)
When selecting a data set to use as the basis of a model for your financial services company, you must determine the age of that data and verify that it remains valid and accurate. Some data sets remain valid for many months or even years, while others may have a relatively short shelf life — a trait that renders this sort of data set into a significant area of vulnerability from a risk management and mitigation perspective. To avoid this pitfall — whereby an out-of-date set of data adversely impacts a model and everything that is based upon that model — take time to verify the age of the data that you’re utilizing. Whenever possible, opt for data sets with a longer shelf life.
Failing to Account for Changes in Surrounding Dynamics Such as Regulatory Measures
The dynamics surrounding a model hold the power to dramatically impact its overall validity. This can be a major issue in situations where you have an extremely dynamic landscape, such as in the case of regulatory compliance. Groups and organizations that perform regulatory oversight are constantly updating the numerous rules and regulations that impact the financial space. This creates a constantly-evolving environment that can have a dramatic impact on model risk implementations. It is prudent that you monitor the financial industry’s regulatory compliance landscape. As you do this, perform periodic evaluations to ensure that any regulatory changes or updates have not adversely impacted the models that are currently in use or models that you are planning to implement in the immediate future.
Failing to Develop a Solid Implementation Plan
Don’t underestimate the value of a well-architected implementation plan. Effective risk management and mitigation demand a solid strategy, especially when it comes to the implementation and deployment phases. Without this, you could end up with a flawed or partial implementation that causes major problems down the road. And once you’ve developed a strategy, remember to actually follow it.
Failing to Alert Staff When a New Model Risk-Related Initiative Will Be Implemented
The success of any initiative — risk management-related or otherwise — can be compromised if the impacted parties are not alerted in advance. Remember to take time out to identify the staff who will be affected and provide them with information on when and how their work will be affected.
Failing to Provide Proper Training or Education
The most effective risk management-related implementation and deployments require some degree of training or preparation. This rings true for model risk initiatives too. Take some time to consider how the implementation will impact your business and if necessary, invest in training, workshops, or educational sessions to maximize your chances of success.
Failing to Look for New Vulnerabilities
Any time you implement changes within your risk management landscape, you risk a scenario whereby you fix one issue but in doing so, you create a new vulnerability. This underscores the importance of properly evaluating the impact of a new model risk implementation and deployment effort. These evaluations ought to be performed at the time of implementation and then periodically in the following weeks and months.
Failing to Use Model Risk Management Frameworks
Model risk frameworks — also called MRM frameworks — are contained within risk management software platforms and they provide valuable tools and features that can be used to guide a company’s model risk management implementations, amongst other things. An MRM framework will provide valuable guidance for implementation and deployment far into the future too. The best platforms include tools for evaluating potentially-problematic models too, with project planning-type features that are used to map out action plans in the event that a problem area is identified. These frameworks are ideal for financial services companies and others that are confronting a complex risk management landscape.
Failing to Align With Your Business and Risk Management Strategies
Your model risk management efforts must be in alignment with your overall business strategy and your broader risk management and mitigation strategy. This is the key to bringing about long-term success. As you implement model risk management initiatives, you must also consider future evaluations and assessments. The best practice is to implement and deploy with these assessments in mind to ensure that your risk management strategy is reflected in your efforts both today and in the future.
Poorly-implemented model risk measures can have a dramatically negative impact on a business, as evidenced by high-profile incidents such as the 1998 Long Term Capital Management incident and the 2013 trading losses sustained by JPMorgan Chase. In the case of the latter, a “value at risk” model was blamed as the culprit, with its operational errors and formulaic flaws.
Model Risk Management Solutions to Aid in Your Risk Mitigation Efforts
The financial space is situated within a complex risk management arena, but the right technology can go a long way toward simplifying and streamlining your financial services company’s risk mitigation efforts, especially when it comes to model risk. At iTech, risk management software solutions are among our specialties. We work with clients across the financial sector, including financial services providers, working to understand their challenges and strategic objectives. Then, we allow these factors to drive the development of a custom risk management software solution. Contact the iTech team today to begin a dialogue on your company’s risk management strategy.