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The Coming Revolution in Local Government Finance

The views expressed are those of the author and do not necessarily reflect the views of ASPA as an organization. 

By Matthew Teal
March 5, 2019

“Artificial intelligence [AI] isn’t coming. It’s already here.” Sarah Ovaska-Few wrote those words in the CPA Insider newsletter for the Journal of Accountancy in October 2017. Since then, AI and Big Data have only grown more prevalent in the finance and accounting worlds. In a September 2018 Forbes article titled, “AI’s Impact On Accounting And Finance,” Jonathan Gass wrote that, “Many traditional bookkeeping tasks … are already being performed by AI. Accounts payable and receivable AI handles much of the work of initiating payments and matching purchase orders. Automated data entry and data categorization help accountants more quickly analyze broad financial trends. … Although transaction approval should be largely left to humans, experts foresee payroll, auditing and tax remittance being performed by AI.”

In a February 2019 McKinsey article titled “How governments can harness the power of automation at scale,” Jens Riis Andersen, Matthias Daub, Andrew Goodman and David Taylor  noted that, “Private firms have married lean process design, which is focused on minimizing waste and maximizing value, with robotics and machine learning to push automation into new activities, many of which previously required human input. Processes such as procurement, from purchase request to order, are now automated to operate around the clock and at around a third of the cost of manual approaches.”

Yet little of that innovation has made its way to local government finance offices. This must change, and quickly. In fact, no part of local government operations is set to take advantage of AI and Big Data more quickly, or be more fundamentally transformed, than finance. In a January 2018 McKinsey article titled, “Bots, algorithms, and the future of the finance function,” Frank Plaschke, Ishaan Seth and Rob Whiteman wrote that, “Currently demonstrated technologies can fully automate 42 percent of finance activities and mostly automate a further 19 percent.” As part of their analysis, Plaschke, Seth and Whiteman estimate that a full 89 percent of, “General accounting operations” are either fully or highly automatable.

At the other end of the scale, more complex parts of the finance function that require context and narrative, such as auditing, are much less automatable with today’s technologies. However, even auditing may not hold out for long. Citing Jon Raphael, Deloitte’s audit chief innovation officer, Ovaska-Few writes that, “At Deloitte, auditors can access AI tools with natural language processing capabilities to interpret thousands of contracts or deeds … The technology can extract key terms and compile and analyze that information to perform risk assessments or other functions.” Yet at the same time, Raphael argues that rather than software replacing auditors entirely, auditors will be needed to spot incorrect analyses and handle exceptions. Augmentation, rather than automation, seems to be the future of auditing. Applying this model more broadly, Ovaska-Few cites Babson College professor Tom Davenport, who argues that, “At some point, there might be some job loss on the margins, but mostly we’re giving people more sophisticated work to do than just looking through documents.”

Adopting AI and Big Data in local government finance would accomplish several key objectives. First, it would improve employee engagement. Anderson et al point out that, “Repetitive manual work is frequently cited as one of the main sources of public-sector job dissatisfaction.” Freeing up employees from, “Just looking through documents,” would give them more opportunities to perform well at higher-order work. Second, AI and Big Data would allow local governments to more quickly and accurately do the important and necessary work of managing taxpayer money. In particular, local governments could move away from simplistic incremental or linear regression-based revenue forecasting models. These models are easy to use and tend to work well until they don’t, such as when the economy declines. Suddenly the comfortable, routine one-to-two percent revenue increase projection turns out to be off by a significant margin.

How would local governments even begin the process of adopting AI and Big Data in their finance departments? After all, most local government officials do not have much experience using AI or Big Data tools. In a November 2018 McKinsey article titled, “New Technology, New Rules: Reimagining the Modern Finance Workforce,” Steven Eklund, Michele Tam and Ed Woodcock argued that formal and informal training opportunities are essential. Examples include classroom and online training opportunities, as well as expert coaching. Finance managers should recognize employees who effectively use their new skills and should link promotion criteria to skill attainment. These steps would signal to line employees that this transformation in finance is real and is taken seriously by management.

Finally, local government finance departments should start small and scale their AI and Big Data efforts. Anderson et al argue that governments should identify opportunities that make sense in that government’s context and launch proof-of-concept models. Next, governments should establish clear governance and build cross-functional teams, pairing subject-matter experts with IT and legal resources. Finally, governments should scale their investments and budget for ongoing maintenance.

AI and Big Data are transforming how finance departments are doing their jobs. Local governments must move quickly to catch up in order to more efficiently and effectively manage taxpayer dollars.

My opinions are my own and do not represent the University of North Carolina at Chapel Hill.

Matthew Teal, MA, MPA
Policy Analyst
University of North Carolina at Chapel Hill
Email: [email protected]
Twitter: @mwteal

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The American Society for Public Administration is the largest and most prominent professional association for public administration. It is dedicated to advancing the art, science, teaching and practice of public and non-profit administration.

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