Google strives to automate as many finance tasks as possible to reduce the amount of manual work its employees have to do.
The Mountain View, Calif.-based software giant uses a combination of tools including artificial intelligence, automation, cloud, data lake and machine learning to manage its financial operations and offers programs and other training to its employees.
CFO Journal spoke with
vice president and chief financial officer at Google, about these new technologies and how they speed up quarterly closing, the use of spreadsheets in finance, and things that can’t be automated. This is part four of a series that focuses on how CFOs and other leaders are digitizing their financial operations. Edited excerpts follow.
WSJ: What are the central elements of your digitization strategy?
Kristin Reinke: We try to focus on the most important things: automation and [how] we can improve our processes, by being better partners in the business, and then [reinvesting] the time we gain in the next business challenge.
WSJ: What tools do you use?
Mrs Reinke: We are using [machine learning] in just about every area of finance to modernize the way we close the books or manage risk, or improve our [operating] or working capital. Our controllers now use machine learning to close the books, using outlier detection.
The flow analysis needed to close the books used to be a very manual process. It took about a full day of knitting various spreadsheets to identify these outliers. Now it takes one to two hours and the quality of the analysis is improved. [We] can spot trends more quickly and diagnose outliers. There is another example in our [finance planning and analysis] organization: One of our teams has built a solution using outlier detection. So they combined outlier detection with natural language processing on the surface of anomalies in the data. We use this machine learning to help us predict and identify where we need to dig a little deeper. [Note: A flux analysis helps with analyzing fluctuations in account balances over time.]
WSJ: What remains to be done?
Mrs Reinke: One place we are looking to improve is with our Forecast Accuracy tool. This tool uses machine learning to generate accurate forecasts, and it outperforms manual forecasts developed by analysts in 80% of cases. There is interest and excitement about the possibility of this type of work being automated, but adoption of the tool itself has been slow, and our analysts have told us they want more. granularity and transparency in the way models are structured. We are working on these improvements to better understand and trust these predictions.
WSJ: What skills do the people you hire bring?
Mrs Reinke: We want to hire the best minds in finance. In many cases, this talent is technical. They have [Structured Query Language] Skills [a standardized programming language]. We have a finance academy where we offer SQL training for those who want it. We try to give our talent all the tools they need so they can focus on what the business needs. We give them access to [business intelligence] and [machine learning] so they don’t spend time on things that can be automated.
WSJ: You’ve worked in Google’s finance department since 2005. What changed when
became CFO of Alphabet and Google in 2015?
Mrs Reinke: When Ruth came on board, she emphasized organization and that discipline to automate where we can. She talks about this fundamental principle: “You can’t drive a car with mud on the windshield. Once you clear that, you can go much faster,” and that’s the importance of data.
WSJ: What are the next steps as you continue to digitize the finance function?
Mrs Reinke: I think there will be many more applications of [machine learning] and ensure we have data from across the business. We have this financial data lake that combines Google Cloud’s BigQuery [a data warehouse] with the financial data of our [enterprise resource planning system] and all kinds of business data that we will continue to feed as the business grows.
WSJ: Can you give other examples of new technologies and how they make your finance function more efficient?
Mrs Reinke: We use Google Cloud’s BigQuery and Document AI technology to process thousands of supply chain invoices from our suppliers. [Document AI uses machine learning to scan, analyze and understand documents.]
By pulling data from our ERP and other data from the supply chain system, we can take those thousands of invoices and systematically validate and approve them. [them]. When we have outliers, we can actually redirect them to the business. And so it’s a less manual process for the business and for finance.
WSJ: Does your finance team use Excel or a similar tool?
Mrs Reinke: We use Google Sheets. Our finance teams love spreadsheets. I remember in the beginning we had a bunch of financial Googlers using it and it wasn’t exactly what we needed. So they worked with our fellow engineers to incorporate features and functionality to make it more useful in finance.
WSJ: Are there any tasks that will be prohibited as you automate more?
Mrs Reinke: Anything that can be automated, we strive to automate it. There’s so much judgment that’s required as a finance organization, and it’s something you can’t automate, but you can automate the more routine activities of a finance organization by giving them these tools.
WSJ: Do you have any other examples of things that can’t be automated?
Mrs Reinke: When you sit down with the company and go through a problem they have, you can never automate it. This kind of interaction will never be automated.
WSJ: How many people work in your financial organization?
Mrs Reinke: We do not disclose the size of our teams within Google.
write to Nina Trentmann at [email protected]
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Google Finance Head: Anything That Can Be Automated, We’re Striving To Automate
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