The chief financial officer of a large Indian hospital group had a problem. To optimize spending and enable the organization to provide the highest levels of patient care, she had to analyze hundreds of indicators: patient information, such as age and insurance; clinical data, such as treatment and laboratory tests; operational data, such as food quality and the responsiveness of medical personnel; and financial data, such as costs and revenues. The hospital’s management software was limited in its ability to handle this complex analysis. To address this challenge, the CFO installed a new decision-support system powered by machine learning. After action was taken on the resulting insights, patient satisfaction scores increased by 12%, and the hospital group enjoyed significant savings by eliminating activities that didn’t add value.
Like the hospital CFO, finance leaders in a wide range of industries are trying to integrate data and analytics into finance office processes to improve operations. However, not all CFOs have cracked the code for capitalizing on the promise of these tools, in particular new machine learning applications. In a 2022 study by Gartner, 80% of CFOs said they believe that finance needs to significantly accelerate implementation of machine learning and artificial intelligence in order to effectively support and protect the business.1 Why are so many CFOs slow to take full advantage of these technologies, and what can be done to bring them up to speed?
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In my work as an academic researcher and consultant, I’ve studied hundreds of CFOs and senior finance leaders from more than 20 industries at companies with revenues ranging from $250 million to more than $20 billion. In conducting research that included interviews and a survey, I sought to understand how CFOs use digital technologies in their finance offices, what they want to achieve through digitization, and the main challenges they face in developing these capabilities. I found that the CFOs who had successfully deployed advanced analytics were able to deliver more real-time insights, reduce human error and bias, and speed up processes and decision-making.
Digital finance leaders also differentiated themselves by spending a greater portion of their time on value creation activities — such as performance management, mergers and acquisitions, pricing, and strategic planning — and value protection activities, including fraud detection and risk management.
1. “Gartner Survey Shows CFOs Turning to Process Mining to Drive Better Returns From RPA,” Gartner, April 27, 2022, www.gartner.com.
2. D. Gopinath, “The Shapley Value for ML Models,” Towards Data Science, Oct. 26, 2021, https://towardsdatascience.com.