Cloud For Finance

How Predictive Analytics Helps Finance Teams Get Ahead


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If your finance team isn't already using predictive analytics, chances are high they will be soon. In fact, the predictive analytics market is expected to reach $3.6 billion by 2020, with financial and risk management accounting for one of top areas of application, according to Global Industry Analysts, Inc.

"Within a few years, every finance team will be using some form of predictive analytics because it will be embedded in their software applications," says Rich Clayton, vice president of Oracle's Business Analytics Product Group. Read: You don't need to be a programmer to make use of it.

A little foresight goes a long way

Predictive analytics can enhance a variety of finance processes, as well as offer insights into business problems and areas of potential. Here are several ways finance leaders are putting predictive analytics to use:

  • Predicting revenue. A plethora of marketing, sales, operation and even customer behavior data is making it possible for finance teams to not only forecast revenue more accurately but anticipate future demand for products. For instance, car companies have long used historical purchase data to predict demand; now they can overlay that data with information regarding current web searches to better forecast sales.
  • Improving supply chains. Supply chain inefficiencies can be a big drain on profits. But finance leaders are using predictive analytics to maximize efficiency in creative ways, such as ranking vendors to see who is most vulnerable to fraud, or identifying equipment that may fail.
  • Analyzing loss drivers. Predictive analytics can also identify trouble spots, including what's driving company losses. For example, CFO Magazine relayed the story of a large insurance carrier that was losing market share. The company used predictive analytics modeling to analyze millions of rows of data, ultimately producing 14 real-time indicators of customer loyalty. Using those, the company was able to create a model that could predict with 81% accuracy whether customers would leave, and provide the executives with real-time information they could use to address the issues and prevent them from doing so.
  • Detecting fraud. Finance leaders have been especially keen on tapping analytics to detect potential fraud, both internally and externally. Clayton cited several examples including Oracle customer StubHub, a ticketing company that uses advanced analytics to help predict and prevent ticket fraud at events. In many companies, finance teams are using predictive analytics to identify potentially fraudulent purchase orders.

Proceed with caution

The potential for predictive analytics is only growing—and while the possibilities are exciting, there can also be serious pitfalls. This targeted use of data can lead to privacy missteps, faulty use of data and even discrimination. For example, companies can end up losing customers if they overstep perceived personal privacy boundaries; in some cases, they can even break privacy laws, depending on the country.

Clayton says such possible pitfalls are why finance teams need to establish some ethical oversight as they ramp up their analytics capabilities, ensuring that the standards and values of the company are being adhered to. The key: Applying critical thinking to the technological process. "It's an opportunity to combine human judgment with analytics and automation," he says. "The union of those things is the future of finance."

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