Tom Riley, the vice president of finance for the American professional soccer club Seattle Sounders FC, is a beta user of financial modeling software. The decision to be a beta user is both a choice and a necessity. Riley has a self-described early adopter mindset, but operating out of Seattle makes the need to try new things a must.
"If you're not up to date, it's a little embarrassing," Riley said of doing business in tech-forward Seattle.
The Seattle Sounders averages more than 40,000 fans a game. The Major League Soccer franchise is second in attendance in the country, and Riley has charge of its financial planning.
Riley is testing Adaptive Insights anomaly detection capability in its financial modeling software. It relies on machine learning, a type of AI, to detect irregularities as simple as identifying a typo or as sophisticated as flagging unusual data that should be investigated. The software automates what is otherwise "expert insight" by Riley and others to find problems with data and to improve workflow, he said. Workday bought Adaptive in 2018 for $1.55 billion.
The Sounders use this system for its income statement, balance sheet, cash flow and other financial functions. Financial modeling software is used to analyze past performance and forecast performance, as well as budgeting. Users can change assumptions and see how they impact the business.
Riley's beta testing of anomaly detection capabilities began in December. He said using the software hasn't been too much work. Every now and then he spends an hour or two on the phone with developers to provide feedback. The cloud-based capability is now live for some customers, and Riley said he's in the queue for a rollout. General availability of this financial modeling software upgrade, including the machine learning-enabled feature, is later this year.
Financial modeling software directions
While the error detection capability is important, Riley sees it as one step to more sophisticated modeling. His bigger question is this: If the capability discovers a number they can't explain, how do they resolve that?
"What are the factors outside of the model," Riley said, "that would help explain it better?" That's where he thinks the future of financial modeling software is going.
Tom RileyVice president of finance, Seattle Sounders FC
Riley believes financial modeling software will combine more external data sets, such as weather, social media streams and demographic information. This external data will inform the financial models and improve their accuracy, he said.
Being able to produce financial models that are, three years out, "explainable, reasonable, rigorous and accurate gives me credibility when I'm talking with my ownership group and my senior team about the big decisions that we have to make," Riley said. Those decisions include "how much we're going to spend on players in this organization to be a successful soccer team," he said.
Brian Hopkins, vice president and principal analyst at Forrester, pointed to IBM's acquisition of Weather.com in 2016 as indicative of the trend.
"The reason [IBM] did that is because they recognized that the number one external impact on company supply chains, retail, things like that, is the weather," Hopkins said.
Hopkins said the need for external data is giving rise to "insight providers" with specific data sets. He described the use of external services for modeling as still in the "early growth stage."