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Predictive algorithms can reduce biases and errors common in traditional forecasting, while freeing finance teams to spend more time evaluating conclusions and translating them into action.

It’s 8:00 a.m., and over the next few hours, a finance team will be helping its CFO settle on a business forecast for the coming quarter. At 2:00 p.m., the CFO will tell that story to a dozen board members on a conference call.

In the past, a forecasting team would pull multiple all-nighters to make that deadline, grinding through spreadsheets, calculating growth percentages, chasing down anomalies, and drinking way too much coffee. Despite their best efforts, biases, guesswork, and human errors would creep into their results.

Eric Merrill

While leading finance organizations are already using automation tools to help with manually intensive work like transaction processing, automating routine forecasting tasks remains ripe for improvement. “Many companies continue to struggle with forecasting—and business leaders are looking to Finance for help,” says Eric Merrill, managing director, Finance and Enterprise Performance, Deloitte Consulting LLP.

Algorithmic forecasting, which involves people working alongside data-rich predictive applications powered by advanced technologies, may help improve the forecasting process while relieving Finance professionals of tedious and repetitive work, according to Merrill.

Common applications for algorithmic forecasting can range from top-down planning (integrated P&L, working capital, and cash forecasting), bottom-up forecasting (operational, product, or geography/market-level forecasting), as well as support external reporting and guidance.

Algorithmic Forecasting—How it Works

At the most basic level, algorithmic forecasting uses statistical models to describe what’s likely to happen in the future, in much the same way consumers use weather apps predict the likelihood of rain or sunshine in the hours and days ahead. It’s a process that relies on warehouses of historical company and market data, statistical algorithms developed or chosen by experienced data scientists aided by machine learning, and modern computing capabilities that make collecting, storing, and analyzing data fast and affordable.

But models increasingly go beyond the basics. “More valuable and precise forecasting models can account for biases, adjust for events or anomalies in the data, and course-correct using machine learning,” says Merrill. “Over time, forecasting accuracy improves as algorithms learn and adapt from previous cycles,” he adds.

Models also become more valuable when their algorithms are informed by richer, more granular data. With advances in natural language processing, this increasingly includes unstructured or text-based data contained in articles, social posts, correspondence, and other documents.

Steven Ehrenhalt

But it’s the symbiotic relationship between people and IT that makes algorithmic forecasting truly effective, says Steven Ehrenhalt, principal and U.S. and Global Finance Transformation leader, Deloitte Consulting LLP. “Machines, can help prevent humans’ unconscious biases and conscious shortcuts from influencing results, and humans can evaluate and translate the machine’s conclusions into decisions and actions.”

“Algorithmic forecasting doesn’t deliver 100% precision, but it is an effective way of getting more value from planning, budgeting, and forecasting processes,” says Ehrenhalt. “We’ve seen companies substantially improve annual and quarterly forecast accuracy, with less variance and in a fraction of the time traditional methods require, while at the same time building their predictive capabilities,” he adds.

How the Workforce Adapts

For organizations adopting algorithmic forecasting, the models themselves may pose less of a challenge than redesigning processes, building trust and transparency, and creating partnerships among teams and with machines, and adjusting finance talent models to reflect new ways of working. That will likely require a different mix of people than many teams currently have in place, although once up and running, these teams can move among businesses in need of forecasting improvement, embedding capabilities and driving integration. These teams are integral to establishing an algorithmic solution that can work for the business, bring insights to life within the organization, and support continued business ownership of the outcomes.

Beyond the Finance Function

Forecasting isn’t limited to finance. Functions from marketing to supply chain to human resources all need predictive capabilities to drive important decisions. While CFOs may not lead function-specific forecasting, they can help shape forecasting initiatives since finance will inevitably use the outputs they generate.

A shared forecasting infrastructure—even a physical Center of Excellence (CoE)—can help improve collaboration and coordination while providing efficiencies in data storage, tool configuration, and knowledge sharing. And once the organization develops the forecasting muscle to solve one problem, the capability can quickly be extended and applied in other areas.

Some companies start small, by selecting a part of their business or a specific revenue, product, or cost element to use as a pilot or proof of concept for algorithmic forecasting. They often run their old and new forecasting methods in parallel for a period to compare accuracy and effort.

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With better models analyzed more quickly, CFOs may improve their ability to assess hidden trends and factor them into their planning. How are unemployment and disposable income trends going to affect the business? How much of that cut in trade spending should I expect to fall through to the bottom line? Scenario modeling done in collaboration with business units gives business leaders visibility into performance drivers, such as seeing how to manage existing markets by building out price, product mix, and volume analytics.

Algorithmic forecasting can relieve finance professionals of tedious, repetitive work while producing more accurate and timely forecasts—and more informed decisions. A commitment to algorithmic forecasting involves great people working with elegant technology. Neither is sufficient on its own.