Don’t Be Out-Teched
You don’t have to be one of the Big Four to deploy the latest tech tools in your firm.
By Ryan Watson, CPA | Fall 2017
Much has been made about artificial intelligence (AI) in the
media, both for its promise of efficiency and also for the risks it
poses to a variety of industries, including accounting.
Just this past summer, our article, “
Accounting for AI,” highlighted
IBM Watson’s big debut into our space and the near-term
implications it will have on our profession at large. That dialogue,
though, at least so far, has primarily centered on enterprise
executions of AI, like KPMG and H&R Block leveraging Watson to
deploy custom AI solutions to their business challenges. And while
that’s exciting for teams at those firms, what’s a small or regional
accounting, tax, or advisory firm to do without the resources of
in-house developers and data scientists?
Fortunately, as AI becomes mainstream, the opportunities to
leverage it to automate work and deliver better client service are
increasing daily. In fact, it’s likely your firm is already benefiting
from AI and machine learning.
Here are three AI and machine learning technologies any size firm
can start using right now.
Image Recognition
Image recognition is being used by firms of all sizes to automate
everything from tax workflows to accounts payable and
receivable—sometimes without them even realizing it. Popular
apps like Hubdoc, Receipt Bank, and Expensify are all examples
off-the-shelf web and mobile apps that are saving accountants and
their clients from hours of manually processing transactions and
expenses, but did you know these are all powered by image
recognition and machine learning algorithms?
Financial document capture apps like the ones mentioned use an
optical character recognition (OCR) process to scan paper bills,
files, receipts, and statements, read the data, and interpret and
digitize it. Using expense reporting as an example, data entry is as
simple as having your client snap a photo of a receipt with their
smartphone and the information is automatically extracted and
populated in the client’s accounting platform, like QuickBooks or
Xero. OCR is already employed in many facets of our everyday
lives, including the tollbooths many of us pass through each day—
OCR technology is what’s behind the automatic license plate
recognition system that charges our I-Pass accounts when we forget
our transponders at home.
Hubdoc employs an extra layer of human quality assurance to
certify the correct information is extracted by its technology, but it
still means no data entry or filing for you or your client. What’s
exciting about this, besides the time-savings and convenience, is
that we’re really just getting started on our AI journey.
“Leveraging mature technologies, like OCR, and some manual
quality assurance, we’ve been able to streamline the bill and
receipt capture process for our customers. But this just scratches
the surface of what we think we’re capable of,” says Hubdoc Co-
CEO Jamie Shulman. “As we start to truly take advantage of AI and
machine learning, our goal is to make the end-to-end expense
management and bill payment process truly automated and
instantaneous.”
This is a technology that’s easy to tip-toe into. You can start with
automating a single workflow, like accounts payable. Working
within the comforts of your clients, select the right tool, perhaps
Hubdoc, and integrate it with a digital general ledger and, if applicable, a bill payments suite. From here, Hubdoc can sync with
vendor portals to automatically ingest all bills for payment or
receive them through email or standard uploads. The image
recognition software will automatically identify the date, vendor,
amount, and after some training by the firm and client, the account
code. It’s really easier than you’d think.
Recommendation Engines
Receiving recommendations from some form of AI may sound
hyper-futuristic, but when you consider the following examples,
you’ll see there’s a common thread here: AI and machine learning
systems are already part of our personal and professional lives.
When Netflix recommends what you should watch next, it’s based
on algorithms that pull from the taste graph of their entire global
subscriber base, taking into consideration what’s available by
region and similarities between content and user behavior—this is
a recommendation engine driven by machine learning.
Similarly, accounting software companies are implementing
machine learning systems within their platforms to help prevent
and correct user mistakes. Take cloud-based accounting software
provider Xero for instance; coming off of an intensive, year-long
project where developers looked at the mental processes users go
through, they’ve built a machine learning model that learns user
invoice coding behaviors and mistakes, and then notes the
corrections accountants and bookkeepers make in order to
automatically offer recommendations and corrections as users work
within the system in the future.
Xero’s machine learning system, already available to users,
improves with use, meaning it “learns” as you’re feeding it data.
And have you ever noticed how your smartphone tries to predict
the next word of your text message, or how Google seems to know
the search term you’re trying to put it? The point is, you’re already
benefiting from recommendation engines, and they’re only going
to become better—and more prevalent. The more you seek out the
tools deploying this technology, the easier your tasks will become.
Smart Assistants
Do we have any Iron Man fans here? Have you ever imagined
having your very own J.A.R.V.I.S. to organize your life? Apple’s Siri
and Amazon’s Alexa are starts, but full-fledged AI personal
assistants may be closer than you think. While smart assistants
aren’t quite capable of toasting your bread yet, they can organize
the myriad of appointments and requests for meetings that
pile into your inbox.
Smart assistants use natural language processing (NLP), a
technology that enables software to understand the actions and
language of humans. NLP gives smart assistants the ability to not
only interpret the language but to understand the intent behind it.
Smart assistants can also learn the unique preferences of the user,
such as their preferences for meeting length or location.
Amy Ingram, for example, is a smart assistant developed by x.ai,
or an AI personal assistant who schedules meetings on your behalf
in an intelligent and human-like manner. The beauty of Amy is that
it’s easily integrated into your workflow by CC’ing “her” into your
emails. Amy then reads the emails and communicates directly with
the contact on your behalf, arranging meetings and appointments
based on your personal schedule and preferences of times,
locations, and meeting types. The most significant benefits of
Amy are her efficiency, human-like capabilities, and ability to
respond in a thoughtful and intelligent manner—people actually
think she’s a real person. Talk to her like a real-life assistant and the
magic of her AI takes over.
Big Data Player
We don’t have to tell you that your business deals with a lot of data.
But businesses today do need to be reminded that this data is only
useful if it’s monitored and leveraged for strategic decision-making
and effective risk management.
The truth is that you’re already a big data user; you just need to
embrace it. If you’re a user of Facebook, Google, payment
processors Stripe or PayPal, or any of the other platforms mentioned
in this article, for instance, you’re contributing to our evolving
big data ecosystem, helping to plant the seeds of future AI
advancement. This data serves as the foundation upon which AI
and machine learning models are built, refined, and reinvented.
For example, Facebook utilizes insights from its big data to provide
targeted advertising based on a consumer’s preferences. PayPal
leverages data from its closed-loop network to improve customer
experiences and detect and prevent payment fraud. And Stripe
utilizes data to analyze and identify patterns of suspicious activity
in payments on its network to safeguard your transactions. Hubdoc,
Xero, and Google are all building big data architectures that will
automate more of our business workflows in the future.
The point is that you don’t have to be one of the Big Four to take
advantage of big technologies. AI, machine learning, and big data
are all around us. The earlier we all embrace change and begin
employing the tech tools available to us, the better we’ll be
positioned to be firms of the future.
Ryan Watson, CPA is a founder and principal of tech-savvy accounting firm
Upsourced Accounting and Xero’s Midwest ambassador.