Advice and Information for Finance Professionals

How 3 Big Digital Disruptors Are Reshaping Finance

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With the arrival of process automation, cognitive tools, and blockchain, finance organizations are facing the potential for major transformation. In the first of a 3-part series, Deloitte looks at the impact of process automation.

Part 1: How Automation Is Disrupting Finance

By Girija Krishnamurthy and David Carney, Deloitte Consulting LLP 

The rumblings of disruption are rolling through the world of finance. And it’s about time. More than ever, disruptive technologies are needed to help finance combat a barrage of new challenges, from exploding data volumes to accelerating business cycles to an unprecedented talent crunch. In this first of a three-part blog series, let’s look at one of the biggest disruptors facing finance today: Automation.

Finance is embracing its new best friend, the bot. Many of the organizations we work with have heard of robotic process automation – or RPA – and every day more of them deploy software bots to boost efficiency and control costs. Right now, we have several clients developing smart bot solutions running in the cloud to turn their payment processing into a touchless end-to-end routine. And that’s just one out of a multitude of processes that finance manages.

Intelligent Automation

It turns out many financial process can be automated. A good example is the close, consolidation and reporting process, the bane of a controller’s existence. Recently we asked one of the world’s leading professionals in the process – a professor at a Top 20 U.S. University – to compile a list the different activities a typical organization performs during its monthly close. Then we asked him which ones could be automated – and which ones may likely require hands-on work.

A week later he came back with a startling answer. Of the literally thousands of activities involved in book closings, there were essentially none that couldn’t be automated. Although he admitted a few dozen might be difficult to automate, there were no “show stoppers.” This suggests that we are nearing the day when bot-driven automation could power a completely hands-off “continuous close” process, the equivalent of Nirvana in finance. A big push in our practice at Deloitte is showing clients how they can automate the close, consolidation and reporting process; and to a large extent, bots are the answer. In fact, Deloitte predicts that the vast majority of financial transactions will be completely touchless by 2025.

Taming a Torrent of Data

Finance is swamped in data and the vast majority of it is going to waste. In fact, my team recently discovered an astonishing fact: Of all the data that exists in the world, less than half of one percent is actually being used. That’s a staggeringly low number, suggesting that there is enormous potential for putting more data to work – and extracting value from it.

But many organizations have been slow to harness the torrent of data. The problem is the maddeningly complex mixture of data sources and formats. Fortunately, Oracle is helping to simplify matters with automated cloud-based platforms and tools that filter out the “background noise” and expose the really valuable data hidden in the mix. Data visualization tools, for example, can provide quick-and-easy graphic interpretations of complicated data sets. CFOs typically love data visualization because it helps them cut to the chase on many topics, saving time. It’s not surprising that data visualization tools are becoming commonplace in finance and are now a standard part of Oracle cloud services.   

All of this is great news for finance – and for the rest of the business too. Organizations can use the efficiency savings from automation to double down on financial planning and analysis and sponsor other projects aimed at helping the business grow and profit. I see this as part of the “big pivot” finance organizations are making as they move from being mostly operational in structure to overwhelmingly strategic.

Ready, Set, Pivot

To make the pivot, finance will likely need to employ a much different mix of talent. The new finance workforce will still need number-crunching proficiency, also known as STEM skills for science, technology, engineering and math. But finance increasingly will need more people who also have a knack for building “empathetic” business relationships. We call the blend of these talents “STEMpathy”—and currently, there’s a real shortage of people possessing that valuable hybrid skillset. Finance will also need to cultivate a new generation of staffers versed in the cloud-based technology tools that are swiftly becoming ubiquitous across organizations.

In other words, automation will compel finance to rethink everything about its workforce as it adapts to new ways of working (hand-in-hand with bots) and new types of workplaces (increasingly virtual and distributed). Recruiting methods will need to change to attract this new breed of finance staffer. Recently I saw a job requisition for a data scientist posted by a Fortune 50 telecom and compared it to a posting for an identical job by a nimble Silicon Valley startup. The jobs were the same but the descriptions made them sound like totally different positions: one cutting-edge and exciting (the startup); the other…well, not so much.

Is automation changing how your finance organization works? I’d love to hear from you. And be sure to watch this space for my next blog, which will explore how cognitive solutions like artificial intelligence, machine learning, and analytics are influencing the evolution of finance.

Tune in to the joint Oracle and Deloitte Digital Finance webcast series to learn more about how automation, cognitive, and blockchain are disrupting finance.

Girija Krishnamurthy is a principal with Deloitte Consulting LLP and leads the Digital Finance area of Deloitte’s Technology practice. Girija brings over 17 years of deep finance transformation and implementation experience for clients spanning North America, EMEA and Asia Pacific. She holds an undergraduate degree in Engineering and an MBA in Finance and Information Systems.

David Carney is strategy & operations principal with Deloitte Consulting LLP. With over 25 years of professional experience, David has advised senior executive teams and boards of directors, typically of large, global clients, on issues of improving shareholder returns.  He is a trusted advisor to many Fortune 500 CFOs and other finance executives.  With a strong background in mergers & acquisitions, he has led many consulting projects involving some of the world’s largest, most complex life sciences, high tech and telecommunications integrations and divestitures. David also leads Deloitte Consulting’s Finance practice, advancing the Chief Financial Officer (CFO) agenda and advising the Finance function to increase contributions to company performance.

As used in this document, “Deloitte” means Deloitte Consulting LLP, a subsidiary of Deloitte LLP. Please see www.deloitte.com/us/about for a detailed description of our legal structure. Certain services may not be available to attest clients under the rules and regulations of public accounting.

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Comments ( 3 )
  • Feroz Merchhiya Friday, September 14, 2018
    Highly relevant subject data-driven insight is not a catch phrase anymore but a reality across all domains. Time is now for business leaders to take decisive actions to embrace technological disruptions taking place all around us. This week at Technology symposium at WBEC WEST @wbecwest #wbecwest WBENC. KAYGEN along with Oracle and other technology partners discussed this very subject with Women Entrepreneurs from across the western US. Encouraging to see these women leaders were leaning in and fully engaged in embracing the new technological realities to drive growth in their organizations.
  • Katy Cristine Teixeira Saturday, September 15, 2018
    Girija, this is a nice article. I see automation as key for transformation but intelligent automation is mandatory!
  • Vipan Gujral Tuesday, October 16, 2018
    Regarding astonishing fact: Of all the data that exists in the world, less than half of one percent is actually being used and believe this is a data which is documented and/or available online. There might be billions of data points which are not documented in the under developed parts of the world. The problem is not the low percentage of data being used but the abundance of unorganized, irrelevant, problematic data which makes it really difficult to make it usable sample for preparing any meaningful analysis. One need a group of people with different skill set to get the expected results. For example, may be it will be helpful to have a historian or people of wide range of age difference or totally different skill sets instead of filling them with only finance and IT people..
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