Customers & Industries: Solis Financial Forensics LLC

Financial Forensics Expert Forecasts Damages using @RISK

  • Industry: Legal
  • Product(s): @RISK
  • Application: Projecting Economic Damages for Attorneys Involved in Complex Lawsuits.

Summary

David Solis, the founder and managing member of Solis Financial Forensics LLC, first came across @RISK as a graduate student at Seattle University. Today he uses the software to project economic damages in a wide variety of complex legal cases.

@RISK allows me to say that I looked at a range of possible outcomes in regard to unknown variables, and based on my analysis incorporating those ranges, I believe lost profits to be X with a reasonable degree of certainty.
David Solis, Solis Financial Forensics LLC

Although he founded the Seattle, Washington-based company that bears his name in early 2017, David Solis has been performing business valuation and financial forensics services since 2007. Today, his company undertakes financial investigations and analyses, forensic economic services, and business valuations. Much of the work is in the form of consulting, litigation support, expert testimony, and analysis of other expert reports in lawsuits of all kinds, including personal injury, wrongful death, wrongful termination, malpractice, business interruption/lost profits, fraud, breach of contract, construction claims, and partnership/shareholder disputes.

Solis, a Certified Valuation Analyst and a Master Analyst in Financial Forensics, uses @RISK to model many things, the most common being economic damages such as lost earnings, lost profits, and the diminishment of value. The inputs for his models vary according to the specifics of the cases in question. Some common inputs include growth rates, discount rates, and future loss periods.

“If I’m projecting future lost profits, for example, there can be many unknowns,” Solis says. “What will lost revenues be in the future? How long is the loss period? What growth rate is expected for future revenues? What would the variable cost percentage have been, had the damaging event not occurred? These are all factors that can be difficult to predict with certainty, because they have not or did not occur.”

Take, for instance, the case of a manufacturer who initiates a business interruption lawsuit against one of its major vendors that includes, among other things, a claim for damages in the form of lost profits. Solis’ job in such a scenario is to determine what would have happened in the past and in the future had the incident which caused the lawsuit to be launched not occurred, and compare that to what has in fact happened and what will likely happen because of the incident.

Forecasting the Future

Looking at the past is a little bit easier. Where @RISK and Monte Carlo simulation typically come into play is when Solis is looking to the future. He’s not only trying to forecast what will actually happen, but what would have if there had been no problems and business had proceeded as usual. To do so, he looks at a variety of factors like historical trends and management forecasts in sales and expenses, and economic factors like the demand for the manufacturer’s products and inflation, for example.

To find the reasonable degree of certainty his industry requires, Solis uses indicators such as historical averages on factors like inflation rates and/or reports such as economic forecasts as the inputs he needs for his models. Because the variables he is working with to determine future outcomes cannot be known with 100% certainty, @RISK allows Solis to incorporate ranges or probability distributions into the simulations he runs, and to run a substantial number of iterations per simulation. The results, he says, provide great insight into the potential outcomes.

“Using @RISK allows for a range or distribution in regards to the inputs,” Solis says. “For example, with the inflation rate I can say I believe it will not exceed X, it will not be less than Y, and I believe it will be Z in the future. @RISK allows me to say that I looked at a range of possible outcomes in regard to unknown variables, and based on my analysis incorporating those ranges, I believe lost profits to be X with a reasonable degree of certainty.”

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