Financial modeling is a powerful tool that – when leveraged correctly – can boost your institution’s ability to make educated decisions. Its benefits, attested to by the Corporate Finance Institute, include increasing general understanding of how your business works while affording analytical abilities for forecasting, valuation, stress testing, mergers, acquisitions, investments, and that’s just the start!
What is a Financial Model and why use one?
Essentially, modeling leverages sophisticated mathematical processes to build an abstract representation of actual financial situations, projecting future performances in a low-risk theoretical setting.
This approach has been successfully employed in a variety of settings, from business strategy analysis and risk assessments to compliance, loan pricing, and stress testing. A financial model is usually the preferable method for institutions over tools that tend to make calculations built upon static inputs. While most tools generally work with limited analysis and consider fewer possibilities, financial models account for broader scenarios and outcomes.
Similarly, modeling transcends statistical-input calculation in favor of a greater ability to modify variables and assumptions, making them easily tailored to your real-world needs and environment. As a result, financial modeling is widely known as a more accurate, fluid, and in-depth approach compared to other financial tools.
The Life Cycle of a Financial Model
A successful modeling strategy will usually follow a few standard steps. From development to maturity, generally, the lifecycle of a model looks like this:
- Development At the early stages of a model’s lifecycle, the emphasis is on identifying requirements, scope, and intended usage of the projection. Once those have been pinpointed, it will be time to focus on model selection/methodology, design & framework, as well as highlighting key points of model development.
- Implementation Moving on to the second step, it will be time to work out the variable selection and carry out pre-implementation tests. This stage also involves user acceptance tests, parallel testing, developing model policy and documentation.
- Production In the penultimate step of a model’s lifecycle, production revolves around environment control, model performance monitoring, model validation, and continued documentation.
- Maturity Finally, as we approach the financial modeling lifecycle’s maturity, it will be time for continued performance testing, access functionality, recalibrating/upgrading, and retirement.
Throughout its production and maturity phases, a model will usually be subject to ongoing monitoring. Such monitoring has the purpose of incorporating real-time information into the process so that the model can remain relevant to its intended use.
Best Practices for Model Testing
It is of vital importance to assess current performance against one or several case studies. This process is known as benchmarking.
After following the four steps outlined for a proactive ongoing model monitoring program, benchmarking is one of the best practices to confirm model accuracy.
According to the Federal Reserve in SR 11-17, benchmarking covers the comparisons from any given model’s inputs and outputs to estimates from any alternatives. Benchmarking thus establishes how reasonable the model’s results and input behavior is.
A good example is an income simulation model, we can compare current results of yields and costs to the first forecasted months’ results of yields and costs. Reviewing makes sure the results are logical. The comparison of forecasted vs. actual results should be easily explained due to trends or other reasons (such as variable-rate index increases).
Sensitivity analysis is used to test the model’s resilience against changes in key inputs and assumptions. It consists of calculating the output variable for a new input variable – leaving all other variables unchanged – to assess the model’s sensitivity to variable coefficients. The result of residual tests can shed light on the reasonableness of the model against all variables.
Scenario analysis differs from sensitivity testing as it is designed to determine the stability of model outputs by altering a set of key drivers vs a single driver at a time.
Through what-if and ad-hoc analysis, it will be possible to draw and describe different scenarios and their possible outcomes. This simulation is meant to determine whether the model’s output is reasonable.
Last but not least, outcome analysis compares model results to corresponding real-life results. This approach is wholly reliant on the model’s objectives and consists of an assessment of the accuracy of the same model.
If the outcomes show a poor performance; where the model’s threshold predictions have unexplainable variations from the real-life, actual results, then action can be taken to remedy this. By comparing the projected and factual outcomes, it is possible to assess the model’s accuracy, determine ranges for actual outcomes and collect data to improve the level of prediction of future, ongoing models.
The Bottom Line
It was financial modeling that sparked the invention of the spreadsheet back in 1978, and its use has continued to revolutionize the financial services industry. When leveraged correctly, modeling plays a vital role in the evaluation of possible scenarios, allowing your institution to make informed decisions concerning budget, investments, and correct allocation of business resources.
If you are interested in learning more about how to maximize your usage of this dependable practice, get in touch to schedule a meeting. We can support you to assess your business’s current situation and help you build an effective strategy moving forward.
Written by Chris Mills, Senior Director
About the Author
Chris has over 25 years experience in financial institution modeling and has been leading MVRA’s model validation services and core deposit / loan analyses teams supporting strategic balance sheet and risk management for over 8 years. She brings a wide range of expertise across treasury, asset/liability management and model risk assessment processes. Experienced with multiple ALM models, she also is skilled in capital modeling, capital markets, liquidity and contingency funding planning, funds transfer pricing, model risk governance practices, and investment banking.