Financial business Analytics Transforms the business into Gold.pdf

September 2020
Business Application Software
Handbook
Financial business
analytics transforms
numbers into gold

1 FINANCIAL BUSINESS ANALYTICS TRANSFORMS NUMBERS INTO GOLD
In this handbook:
Analytics is CFOs' ticket to
greater strategic planning
power
Financial tools for business
breathe life into transaction
data
Beta tester imagines
financial modeling software
future
Digital transformation of
finance unleashes analytical
minds
<Document Title>
Business Application
Software Handbook
Analytics is CFOs' ticket to greater strategic
planning power
DAVID ESSEX, EDITOR AT LARGE
For the past decade, finance departments and CFOs have aspired to augment
their perennial roles as bean counters and regulatory compliance scolds by
adding the analytical skills needed to function as strategic partners in the entire
business. Business analytics tools have been their ticket to garnering greater
influence, a way to work smarter.
Now financial business analytics is reaching new levels of sophistication and
intelligence, thanks to AI, machine learning and robotic process automation. This
handbook provides real-world proof that companies are using AI-boosted
business analytics and other automation tools to be smarter, more responsive
actors in fast-changing economies.
Our first feature shows how AI and predictive analytics help collection
departments spot late payers and CFOs deliver detailed analysis of customer
buying patterns. Meanwhile, robotic process automation speeds up accounts

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payable and other core -- but often mundane and time-consuming -- processes
and gives a helping hand to more value-adding customer renewal processes.
Next, the Seattle Sounders soccer club applies modeling software with machine
learning capabilities to its financial reports. The club plans to add external data,
such as weather information and social media streams, to improve the quality of
its strategic decision-making.
Finally, the CFO of a leading vendor of financial business analytics software
explains why using AI and predictive analytics to effect digital transformation will
make financial analysts work smarter, but only if they have the analytics and
data science training and political savvy to communicate persuasively with senior
executives.

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Financial tools for business breathe life into
transaction data
LINDA ROSENCRANCE, CONTRIBUTOR
The role of finance is changing. No longer does business finance center on
someone in the back office pushing papers and recording transactions.
"It's the ability to understand the transaction, understand who your buyer is and
who your suppliers are, and understand the predictability of them and
understand what your cost factors are or what your risk factors are. It's the
ability to predict that," said Stuart Pasternak, CFO at GreenSpace Brands, an
organic and natural food company in Toronto. As a result, CFOs must act more
like general managers with a vision toward the future, he added.
"CFOs need to know what's going on today, so they can make the appropriate
decisions today, so they're ahead of the curve and can deal with those issues
before they become problematic," he explained. That's where newer financial
tools for business, such as artificial intelligence, machine learning, predictive
analytics and robot process automation, come into play.

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CFOs, for example, can use AI to mine collected supply chain data and derive
insights into a company's buying trends. "Who's buying, what they're buying,
what margins they're buying at. How much time is spent dealing with a
[particular client]. What your cost structure is," Pasternak said. "And you can use
that information to hone in on the best ways to improve results."
IDENTIFYING AND LOWERING RISK
In the collections process, AI is being used to better segment the late payers for
a particular collection period, said Paul Morrison, managing director at the
consultancy Hackett Group. "So, using AI, looking at the total history of
collections or late-payment data, the software is able to identify the high-risk,
nonpayers, and use that information to focus the human team on interacting
with that high-risk group," he explained. "And the net outcome is improving cash
flow because you're focusing efforts of the team on exactly where it's needed in
that collections process, rather than chasing in a less guided way."
The Hackett Group is currently working with a manufacturing company whose
forecasting team can't keep up with the complexity of predicting when to buy
key inputs, Morrison said. "They spend tens of millions of dollars on their key

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inputs for their manufacturing process, and there's just so much data. They don't
know what to do with it," he said. "They just cannot handle it. But with the
combination of AI and automation, they can run daily forecasts about pricing in
the market, and AI tells them when the best time is to buy the commodity."
Another company, which manages solar and wind farms for major U.S. utilities,
receives data every five minutes on potential wind patterns and wind usage for
the wind turbines. "Now they're going to use AI because, before, they couldn't
have a human downloading data every five minutes to make a forecast," Hackett
Group principal for digital enablement services Justin Gillespie said. "They're
using automation to go out and grab that data and merge it with weather data
so they can get a much more accurate forecast into what the potential output of
these wind turbines is going to be. So they'll have a much finer way of predicting
revenues, based on that external data."
PREDICTING BUSINESS OUTCOMES
Predictive analytics is among the financial tools for business playing an
increasing role in corporate planning. "Predictive analytics can identify a pattern
and an outcome in the past," said Imran Sabir, senior manager of robotic process

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automation (RPA) at digital consultancy OZ. "Using clean data, it can predict
future outcomes based on identified patterns, thus preparing companies to plan
and correct conditions for the best business outcome. For finance, it can
improve profit, revenue growth, operational efficiency and net cash flow."
That means looking at key predictive drivers. "I think if you look historically, ... a
lot of companies out there are taking past performance, applying some type of a
factor to it and trying to predict future performance based on history," said Dan
Wheadon, a finance and accounting practice lead at auditing and tax firm RSM
U.S. LLP.

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"You can look at what the cost is to do something," Pasternak added, "and use
predictive analytics to determine the future cost by looking at customers' buying
habits at different times of the year. Then you can predict, depending on the
type of customers you have, what your inventory levels should be. If you're

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going to run a special, you can use predictive analytics to determine which
customers to target as well as the effect it will have on your financials."
Predictive analytics can also figure into improving customer experience, product
quality, business operations and eventually the bottom line, according to Ivan
Latanision, executive vice president and chief product officer at enterprise
expense management software provider Tangoe. "It could be improved
customer service, or maybe you're going to deliver better quality products," he
said. "But you could use predictive analytics as a way to look at the operational
processes, the cost, the people, the changes you're going to make and forecast
out what that would do to improve quality and [consequently revenue]."
AUTOMATING ANALYTICS AND OPERATIONS
Machine learning can be used to help identify patterns in data and predict what
to expect in the future and can further adjust other variables to suggest how the
best outcome can be achieved, Sabir said, offering this hypothetical: "Past sales
data shows that, on any given sale, if prices are reduced by 10%, there was an
increase in sales by 20%, compared to the same period without any sale, for a

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net margin of 40%. But if prices are reduced by 15%, there is an increase of 25%
in sales, but net margin dropped to 38%."
Looking at this historic data, he added, analytics can predict the outcome if sale
prices are reduced by 12%. Machine learning can suggest the best outcome by
trying different sale price percentages.
Sabir called machine learning specific in nature, using different algorithms like
decision trees and neural networks to mimic the way humans learn, while AI is
more general and can learn a pattern in one specific data set and apply the same
pattern recognition to other data sets. AI, he said, can be especially helpful in a
company's financial health planning.
In addition, financial tools for business, such as RPA, are being applied to
automating accounts payable and receivable, compliance and reporting on the
way to more efficient and profitable operations, according to Sabir. The
customer renewal process is generally a complex process, he said, mainly due to
the number of operations and synchronization between different departments
and systems.

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"RPA can take over the entire process [and speed renewals]," Sabir said. "It can
standardize communication with the client, help process changes and draft
documents. Furthermore, it can update the internal systems accordingly."
Bots can also help maintain data consistency. "Customer information is
constantly changing," Sabir noted. "For example, their names, their addresses or

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their credit scores. Software bots can use bank statements as reference points,
extract the relevant data and update records, limiting the amount of manual
work [by] the finance department."
Companies could also tie RPA together with other financial tools for business like
machine learning, Latanision added. "Say you're looking at the usage of
smartphones in your company, and you realize that the usage is coming in higher
than you expected," he conjectured. "And your analytics engine provides a
recommendation. It says, 'Go to the carrier and make this change today.' The
company could tell a bot to log into the carrier's system, make the change and
do it in real time. So you're using the technology to identify something that you
could change that could control your costs."
Bottom line is financial tools for business can provide companies with multiple
options in streamlining traditional functions and deriving insights that improve
operations. Companies need to think things through holistically and understand
from start to finish how to apply the right financial technologies so they can
extract data from the source of a transaction and use it to predict future
performance.

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Beta tester imagines financial modeling
software future
PATRICK THIBODEAU, NEWS WRITER
Tom Riley, the vice president of finance for the American professional soccer
club Seattle Sounders FC, is a beta user of financial modeling software. The
decision to be a beta user is both a choice and a necessity. Riley has a self-
described early adopter mindset, but operating out of Seattle makes the need to
try new things a must.
"If you're not up to date, it's a little embarrassing," Riley said of doing business in
tech-forward Seattle.
The Seattle Sounders averages more than 40,000 fans a game. The Major League
Soccer franchise is second in attendance in the country, and Riley has charge of
its financial planning.
Riley is testing Adaptive Insights anomaly detection capability in its financial
modeling software. It relies on machine learning, a type of AI, to detect
irregularities as simple as identifying a typo or as sophisticated as flagging

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unusual data that should be investigated. The software automates what is
otherwise "expert insight" by Riley and others to find problems with data and to
improve workflow, he said. Workday bought Adaptive in 2018 for $1.55 billion.
The Sounders use this system for its income statement, balance sheet, cash flow
and other financial functions. Financial modeling software is used to analyze past
performance and forecast performance, as well as budgeting. Users can change
assumptions and see how they impact the business.
Riley's beta testing of anomaly detection capabilities began in December. He said
using the software hasn't been too much work. Every now and then he spends
an hour or two on the phone with developers to provide feedback. The cloud-
based capability is now live for some customers, and Riley said he's in the queue
for a rollout. General availability of this financial modeling software upgrade,
including the machine learning-enabled feature, is later this year.

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FINANCIAL MODELING SOFTWARE DIRECTIONS
While the error detection capability is important, Riley sees it as one step to
more sophisticated modeling. His bigger question is this: If the capability
discovers a number they can't explain, how do they resolve that?
"What are the factors outside of the model," Riley said, "that would help explain
it better?" That's where he thinks the future of financial modeling software is
going.
Riley believes financial modeling software
will combine more external data sets,
such as weather, social media streams
and demographic information. This
external data will inform the financial
models and improve their accuracy, he
said.
Being able to produce financial models
that are, three years out, "explainable, reasonable, rigorous and accurate gives
me credibility when I'm talking with my ownership group and my senior team
If you're not up to date,
it's a little embarrassing.
Tom Riley, Vice
president of finance,
Seattle Sounders FC

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about the big decisions that we have to make," Riley said. Those decisions
include "how much we're going to spend on players in this organization to be a
successful soccer team," he said.
Brian Hopkins, vice president and principal analyst at Forrester, pointed to IBM's
acquisition of Weather.com in 2016 as indicative of the trend.
"The reason [IBM] did that is because they recognized that the number one
external impact on company supply chains, retail, things like that, is the
weather," Hopkins said.
Hopkins said the need for external data is giving rise to "insight providers" with
specific data sets. He described the use of external services for modeling as still
in the "early growth stage."

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Digital transformation of finance unleashes
analytical minds
DAVID ESSEX, EDITOR AT LARGE
The digital transformation of finance is like all automation: an effort to speed up
and standardize manual processes. CFOs can lose their jobs if they don't get the
essential processes right, such as moving invoices and closing the monthly books.
But once a firm digital foundation is laid, finance departments can become
strategic advisers to the CEO by harnessing more advanced technologies, such as
analytics and AI.
For 11 years, Dan Fletcher has had what he calls a "side seat" to the technical
and business challenges of CFOs, including the digital transformation of finance,
as a consultant focused on improving the performance of companies owned by
private equity firms. He became CFO of Host Analytics, a provider of SaaS
corporate performance management and financial analytics based in Redwood
City, Calif., when his employer, Vector Capital, bought the vendor this past
January.

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Fletcher shared his views on the digital transformation of finance in his company.
What do you think digital transformation of finance really means?
Dan Fletcher: The fintech that's used by finance has a really unique impact on
the organization holistically, because everything flows through [it] from a data
and decision-making perspective. Fintech can influence cross-functional
partnerships, compensation and incentives that can influence how decisions are
made ... and the processes through which work is completed across the org, how
outcomes are measured and how the results of the business are communicated.
It's really the lifeblood of the business. Perhaps 'hub' is the best word I can use
to describe what I think of fintech as in both its current iteration and as it will
evolve.
What technologies are central to the digital transformation of finance?
Fletcher: The way I break it down is by function, workflow and process. You've
got all these finance processes, such as procure to pay, and then you've got
everything from reporting and budgeting and forecasting, from invoicing all the
way through to cash.

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The question, of course, is why automate? There are multiple reasons. No. 1 is
speed. These processes, when they're done in [Microsoft] Excel, are done offline
and largely manually by the team. It takes time. So, speed and accuracy.
The second would be the professionals in the finance and accounting function
are often some of the most highly educated, sophisticated and intelligent and
ambitious professionals in the organization. The less time I have my team
bogged down in manual tasks and doing account recs in Excel, the better,
because then I can turn those high-powered guns on to really value-additive
activities, like decision support.
The last would be either cost takeout or future cost prevention. At a company
the size of Host Analytics, we've got a nice team in place here, a nice solid
accounting team and finance team. And because of what we've done in the back
office, I think we could double the size of our business without adding a single
additional head to our finance and accounting team.

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Some say the next frontier in the digital transformation of finance is the
analytical side, including financial modeling, advanced analytics and AI. What
do you see happening in the industry or your company?
Fletcher: There's analyzing your own data -- being able to consolidate clean data
that your team can trust and access quickly -- whether you're pulling it into
reporting from a Host perspective, or whether you're pulling it into modeling
and running scenario analysis on it. That's kind of step one, and I think we as an
industry are there.
We've built the functionality with all the connectors into ERP systems such as
NetSuite and Sage Intacct, and pulling that into solutions like Host Analytics or
even a BI [business intelligence] tool like Looker. We're able to very quickly
consolidate, gather and trust our data, and parse it and use it to make decisions
now or to run scenario analysis and forecast the future. We can continue to
evolve that.

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The next frontier is predictive analytics.
There are different parts. No. 1 is: Are we
able to access data that is correlated to
our business and, therefore, be smarter
when we're forecasting or making
decisions? That may be macroeconomic
data, it may be industry trends -- you just
need to find those correlations. It may be
the price of oil, depending on your
industry.
The other piece of that would be writing
code -- algorithmic code that's not relying
on humans working in Excel-like solutions
... into which we can input our base data. Then, it runs its secret sauce, whether
that's an algorithm or some other complex code, and then outputs data that is
useful to us. That is the part of the business, part of the automation, that I think
we're still working on.
Manual tasks are being
slowly automated away,
and there's no doubt
that the tasks remaining
are largely nonmanual,
or certainly require a
higher level of skill and
intelligence.
Dan Fletcher, CFO, Host
Analytics

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How far could the use of robotic process automation go?
Fletcher: It can be applied to almost any of those various workstreams and
functions in the finance org. I'm not trying to get rid of humans, [but] I'm not
naive enough to say it doesn't remove some demand for human labor in the
market.
Reporting is a real pain, as any finance professional knows. And because of our
system, reporting is essentially automated, except for the commentary, the
MD&A [management discussion and analysis]. That may be somewhere in the
future -- automating that commentary. But, right now, it's the human touch.
The marriage of operations and finance has long been a goal of corporate
performance management. How successful have Host Analytics customers
been at achieving synergy and coming up with something that's greater than
the sum of the parts?
Fletcher: If you're a company that has a lot of inventory, and you need to do
constant, vigilant analysis of inventory level and SKU level and profitability, our
software and software of adjacent technology in this space can be very valuable.
You pull all your data into a data lake or a data cube, and you provide pretty

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deep, high-powered resources ... to help analyze it. One example might be if
you're a building-products distributor, and you've got thousands upon thousands
of SKUs -- we're talking 30,000 SKUs -- and your ops group doesn't have the type
of resourcing to constantly stay vigilant about profitability.
Finance can access the same data lake and build what you might call an
economic contribution margin cube, allocating costs across to different product
lines and different SKUs, so that we really get a sense of that long tail -- the
products that we're selling and aren't providing a lot of margin to us. And then
there's an opportunity to rationalize those SKUs, or to at least negotiate better
pricing on them with our vendor partners.
What you've described sounds like it puts some demands on the skill sets of
finance people. How big an issue is reaching this higher level of predictive
analytics to do sophisticated things with operational and financial data?
Fletcher: Institutions of higher education and the AICPA [Association of
International Certified Professional Accountants] and other stakeholders in
educating our finance and accounting professional workforce [are] eventually
going to have to step up. The manual tasks are being slowly automated away,

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and there's no doubt that the tasks remaining are largely nonmanual, or
certainly require a higher level of skill and intelligence.
Finance professionals are going to need to be very adept and very flexible at
[handling] large volumes of data and pulling out the insight, and then on the
back end of that -- and this is often overlooked -- being able to communicate
that to the stakeholders.
You can do all the greatest work in the world as a data scientist or as an FP&A
[financial planning and analysis] professional and pull out your insights, but if
you don't have the political savvy and the communication skills to bring that to
the proper executive, be able to win them over and be persuasive enough to
provide or prompt action, then your skill set is largely going unutilized.
Have CFOs become more strategic? How has their relationship to IT and the
CIO evolved?
Fletcher: When you think about the responsibilities of the past, they were largely
defined as maintaining regulatory and statutory compliance and reporting,
analyzing and reviewing financial data, financial performance and, of course,
preparing budgets and monitoring expenses and cash flow -- all that core stuff.

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And, of course, all the processes that underlie those functions: payroll, [travel
and expense] approvals, etc. Those are our bread-and-butter activities that the
CFO and CFO team still have to execute at a high level.
The modern CFO ... is the strategic partner to the CEO. They may be expected to
identify and execute value-creation opportunity. They may be the owner of
[mergers and acquisitions]. They're often the center of business intelligence, not
just an owner of the financial results, but of the intelligence derived from those
results. And they're expected to interface with the organization both vertically
and horizontally to implement a finance mindset.

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