Risk Management & Analytics: Is it a financial model or a tool?

Is it a financial model or a tool?

Ensuring compliance with key model risk management (MRM) guidelines and regulations requires that risk managers for financial institutions assess and validate all financial models. As economic conditions evolve and regulators demand greater balance sheet transparency, financial institutions are developing and managing more models than ever before. To meet regulatory guidelines, many financial institutions must decide whether a method of financial calculation is considered a financial model or a tool.

Distinguishing between the two could be the difference between compliance and non-compliance — between passing an audit and paying a fine. Moreover, neglecting to validate a model may lead to inaccuracies in financial reporting, or adversely impact interrelated models. There is also an impact to MRM planning and costs: The more models to manage, the more third-party validations are required, and that adds to MRM costs.

Unfortunately, the difference between a model and a tool frustrates many financial risk managers. While all financial models are tools, not every tool is a financial model. An Excel spreadsheet may be a model, depending on its use and impact, but it may also be a tool. Let’s explore the definition of a financial model and outline a few ways to tell the difference.

According to the Board of Governors of the Federal Reserve System’s SR 11-7 Guidance on Model Risk Management, a “model” refers to “a quantitative method, system, or approach that applies statistical, economic, financial, or mathematical theories, techniques, and assumptions to process input data into quantitative estimates.” The board also states that the definition covers “quantitative approaches whose inputs are partially or wholly qualitative or based on expert judgment, provided that the output is quantitative in nature,” and lays out a range of uses for models meeting this definition, ranging from informing business strategy to minimizing regulatory, financial and market risk.

Based on the above definition, financial institutions should consider the following criteria to classify whether their methodology is a model or a tool.


Q: Is it a component of another financial method/calculation? Is its purpose to accomplish a task?

A: If it is a component that is not material in nature and used for calculations such as net present value, or budgeting, it is probably a tool. Conversely, if what the institution has informs or supports a business or financial decision, it is likely a model. A financial model comprises may use many tools within its process but its overall structure brings together various components to answer a larger question. This can also be based upon materiality.


Q: Does it focus on what’s important for the business and its users? Will an error or misuse impact business decisions, balance sheet accuracy or reporting at your organization?

A: If the impact of misuse is significant and has business or financial implications, it is more likely that the institution is using a model.


Q: Does it incorporate scenarios that enable an institution to predict changes in behavior?

A: The ability to run multiple scenarios, change parameters, incorporate assumptions, and run dependencies is probably a good indicator that the institution is dealing with a model, not a tool. Some models are extremely simple, but the ability to predict or forecast financial performance suggests that the institution is working with a model. A tool will consider few possibilities and utilize static inputs to derive an answer.

Whether an institution thinks it has a tool or a model, it must consider many factors. One thing to keep in mind is that a series of tools that have a material impact on business decisions or financial statements could be deemed a model, especially if there is an overreliance on the tools.

If you need support with MRM decisions, please email us at marketing@situs.com to set up a discussion with our model risk team.

Federal Reserve’s Kashkari, citing yield curve, wants rate hike pause

The narrowing gap between yields on long-term and short-term Treasury bonds to little more than the equivalent of one rate hike from the Federal Reserve has helped sour at least one U.S. central banker on any further interest rates increases.

Minneapolis Federal Reserve Bank President Neel Kashkari, who does not vote this year on Federal Reserve policy but takes part in the U.S. central bank’s regular discussion of interest-rate policy, said last week that the flat yield curve means interest rates are close to neutral.

“This suggests that there is little reason to raise rates much further, invert the yield curve, put the brakes on the economy and risk that it does, in fact, trigger a recession,” he said in a blog post. “If inflation expectations or real growth prospects pick up, the Federal Reserve can always raise rates then.”

Read more: Reuters

Export-Import Bank needs to continue to improve fraud risk management

The Export-Import Bank of the United States helps U.S. companies that want to sell to foreign buyers but can’t get private financing. According to the Bank, its programs support tens of thousands of U.S. jobs annually. However, the Bank is backed by the U.S. government, so taxpayers could be responsible for losses.

GAO reviewed the Bank’s controls for preventing losses from fraud. The Bank has taken steps to improve fraud risk management, including adopting some practices in our fraud risk management framework. However, it should conduct a comprehensive fraud risk assessment and use that to design antifraud controls.

Read more: Government Accountability Office

Eyeing risk to reap rewards

It’s no secret that banks and FinTechs are collaborating at a rate faster than ever before. Where once traditional finance companies viewed tech-driven service firms with caution and even a bit of skepticism, the realization more recently has been that joint efforts can reap dividends.

There’s the long-term lure, of course, of increased customer headcount and cross-pollination of new revenue streams.

But the regulatory landscape is becoming more unforgiving in some ways, with data privacy and consumer protection top of mind. The need is there for a comprehensive approach for risk management, which in turn means that both FinTechs and financial institutions (FIs) need a strong, consistent strategy and road map from the very start of collaborations.

Marrying new tech with older line, traditional services has its challenges, and they must be met even as rewards (the profitable kind) beckon.

Read more: PYMNTS

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