Overcoming The Pre-Accounting Hurdle Of Data

Following anxieties that robots would replace their jobs, small business (SMB) accountants are now on a path to potentially become more strategic partners with their SMB clients. Many industry players are touting the ability to use analytics and automation technology to handle mundane, time-consuming tasks, freeing up resources for accountants to take on more advisory roles.

However, other players have warned that, despite the promises of technology, accountants have yet to elevate their positions into a more strategic collaboration with SMBs — manual data entry remains one of the largest burdens, for instance. That’s because, particularly for smaller accounting firms, the challenges associated with collecting and categorizing all the necessary information on their SMB customers continue to be a hurdle. After all, just because data is digital, that doesn’t mean it’s easily accessible or analyzed.

In a recent conversation with PYMNTS, Receipt Bank CEO Adrian Blair explained that this predicament is a massive opportunity for an emerging field of the small business accounting arena: the pre-accounting phase.

“In many ways, the biggest pain point for accountants and small businesses is assembling the books in the first place,” he said. “That means collecting information, compiling information from a variety of sources, transcribing that information, digitizing information if it isn’t already digitized.”

That’s only the beginning. Blair went on to discuss the continued data management hurdles for small business accountants after the aggregation phase, which includes the standardization and categorization of all that information.

He offered the example of a small business that has purchased an automobile. While an accountant may be able to access digital information about that transaction, that professional must also understand whether that purchase was for inventory (for a car dealership, for example), or whether that asset is one that will depreciate (say, if a business has procured that automobile as part of an operational fleet).

Once all this information has been be gathered and codified, only then can it be entered into an accounting system — another time-consuming process.

“When you go into the detail of what it takes to successfully compile, transcribe and codify information, then send it to accounting software, across many different small business clients, then you realize this is a huge pain point for accountants,” said Blair.

Data Integration Opportunities

The small business accounting space has seen significant innovation in recent years, with many technologies looking to address this problem of data.

For some emerging accounting platforms, integration with other back-office platforms — like payment tools and expense management solutions — can support the ability to automatically capture data. These solutions can come with drawbacks, however, whether it be from the inability to capture data from all sources necessary to file taxes or close the books, or from an inability to accurately categorize the information that is captured.

Open Banking is another initiative that has opened up opportunities to ease the data burden in small business accounting. Increasingly, the framework is spreading beyond consumer financial services, and into the B2B and small business space, as accounting platforms like QuickBooks take advantage of data connectivity with banks and other FinTech platforms.

Blair noted that Receipt Bank — which recently announced $73 million in Series C funding, led by Insight Partners — is now licensed to connect into bank account data for accountant users. He also emphasized that service providers must be able to accommodate the various preferences of SMB accountants, in terms of whether they want client bank data fed directly into a portal like Receipt Bank or directly into their accounting platforms.

Machine Learning Gains Traction

While innovations continue to focus on collecting data across services, and integrating information from one platform to another, the challenge of understanding what that data actually means, and how to categorize it, continues to be a major barrier for accountants. According to Blair, machine learning has surfaced as an instrumental technology that can tackle this challenge — and tackle it more accurately and quickly than a human accountant could, he noted.

This will continue to be important as the industry migrates toward real-time insights and analytics, added Blair — a capability that cannot be achieved by accountants, who have traditionally stepped in to manually analyze months-old data for their small business clients. However, the true value of machine learning is not in being better than a human. It’s in doing mundane tasks better than a human, enabling a human accountant to then make use of that information for advisory services.

“What’s really important is we’re not in the business of saying we’re going to get machines to do the work of accountants and replace human beings,” he said. Rather, this technology is designed to enable accountants to make the greatest use of their expertise and intelligence, and provide the high-value advisory services SMBs demand today.