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AN EXL WHITE PAPER
Rise of Machines
Artificial Intelligence and Machine Learning for Digital CFOs
Sanjeev Bhatt
F&A Capability Development, [email protected]
There is a subtle difference between AI
and Machine Learning. AI is a branch of
computer science attempting to build
machines capable of intelligent behavior,
while machine learning can be defined as
the science of getting computers to act
without being explicitly programmed. In
another words, AI researchers build the
smart machines, while machine learning
experts would make them truly intelligent.
Deep learning, a further subset of machine
learning gaining lot of prominence of late,
imitates the workings of the human brain
in processing data and creating patterns
for use in decision making. Facebook’s
use of face and image recognition is an
example of Deep learning.
AI and machine learning are already
driving the technology we use in our
everyday lives. For example, typing the
first few letters of a query into Google
and having the remainder anticipated
is a result of machine learning, as are
recommendations from Netflix on what to
watch next. Similarly, driverless cars, smart
personal assistants such as Siri, Cortona,
and Alexa are some of the common
examples of AI applications.
Incorporating AI and machine learning into
business processes creates an intriguing
prospect.
AI and ML: Promising applications in Finance
Managing Portfolio
Algorithm-based robo-advisors are built
to calibrate a financial portfolio to the
goals and risk tolerance of the user. Users
fill in their goals (for example, retiring at
age 62 with $350,000.00 in savings), age,
income, and current financial assets. The
AI: Not artificial anymore. Incorporating artificial intelligence (AI) and machine learning (ML) into
business processes creates an intriguing prospect. With finance being one of the most critical
functions of an enterprise, CFOs should understand and leverage AI and ML to provide real time
insights, inform decision making and drive efficiency across the enterprise.
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robo-advisor then calibrates to changes
in the user’s goals and real-time changes
in the market. These AI backed robo-
advisors have gained significant traction
with millennial consumers who don’t need
a physical advisor to feel comfortable
investing.
Algorithmic Trading
Algorithmic trading utilizes advanced and
complex mathematical models to make
high-speed transactions in and determine
trading strategies for optimal returns. Most
hedge funds and financial institutions do
not openly disclose their AI approaches
to trading, but it is believed that machine
learning and deep learning plays an
increasingly important role in calibrating
trading decisions in real time.
Fraud Detection
While earlier or conventional financial
fraud detection systems relied heavily
on complex and exhaustive sets of rules,
modern fraud detection goes beyond
following a checklist of risk factors – it
continuously and actively learns and
calibrates to new security threats. By using
machine learning for fraud detection,
systems can detect unique activities or
behaviors and flag them for security teams.
Loan / Insurance Underwriting
Machine learning algorithms can effectively
process and get trained on millions of
examples of consumer data such as age,
job and marital status, as well as financial
lending or insurance results including
whether an individual defaulted or paid
back a loan on time. Algorithms can
continuously analyze and sense trends that
might influence lending and insuring in
the future.
Customer Service
Chat bots and similar conversational
solutions are a rapidly expanding area of
investment in customer service budget.
These virtual assistants are built with robust
natural language processing engines as
well as the nuances of finance-specific
customer interactions. Banks and financial
institutions that provides such a swift
querying and interactive experience might
pick up customers from traditional banks
that require people to log into a time
-consuming online banking portal and do
the digging themselves.
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Sentiment Analysis
Much of the future applications of AI and
machine learning will be in understanding
social media, news trends, and other data
sources – not just stock prices and trades.
The stock market moves in response to
numerous human-related factors, and the
ability of AI and machine learning to process
and understand their large data sets will
one day be able to replicate and enhance
human financial intuition by discovering new
trends and telling signals.
New Security Norms
The current personal security features like
login credentials and security questions
may no longer be the norm for user security
in the coming years. In addition to glitch-
detection applications like those currently
being developed and used in fraud, future
security measures might require facial
recognition, voice recognition, or other
biometric data, powered by AL and ML in
the background.
Intelligent Approval workflows
Currently, approval workflows mostly
include matrices that list various conditions
based on which approval levels are
triggered. But these approval workflows
don’t consider the broader circumstances,
like if the requester is new in role and
might require more supervision, or whether
previous request from this requester been
rejected or approved. AI-based intelligent
workflows could allow finance team to
distinguish and filter out the true exceptions
from the standard low-risk exceptions
that are usually approved anyway. This
way, employees do not need to wait for
approvals and feel empowered, while still
limiting the risk to the corporation.
AI and Machine Learning: The way forward for the CFOSurprisingly, AI and machine learning are
still not on the radar for many CFOs as part
of their strategic future investment areas.
This might limit their long-term chances
of either maintaining or achieving strategic
positioning in the market or among their
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competitors. It’s similar to when cloud
technologies emerged as a potential
disruptor to the accounting profession.
Many didn’t foresee that cloud adoption
would become so widespread, but it has
now become standard. AI and machine
learning present an even larger potential
disruption.
A lot of accounting technology companies
are experimenting with AI-based solutions
and implementing them in their platforms,
something forward-looking CFOs should
be optimistically in. Some next-generation
applications powered by machine
learning can significantly optimize the
cash application process by continuously
analyzing historic data such as pay patterns,
behavior and clearing documents, and
based on this information update matching
principles to clear payments automatically.
With this approach, the efficiency and
effectiveness of cash application can
be improved significantly and achieve
extraordinary automation rates.
The AI and machine learning business
opportunities have only just begun to
scratch the surface of what’s possible.
Many companies have already initiated their
strategic investments in this field. CFOs
should begin considering their company’s
investment strategy on these future
technologies.
The smart way for handling the risks of AI and machine learningWhile there are immense possible business
applications for these two technological
trends, CFOs should also be aware of the
associated risks.
A simple example of unintended
consequences is price discrimination.
A machine, still not evolved that
much, cannot make moral judgments
about discrimination; it can only make
decisions about classes of customers
with no understanding of who is part of
a marginalized group or what the legal
implications might be. As more decisions
become automated, the risk of having
conflicts with laws and regulations
increases if these applications are not
fine-tuned during implementation stage.
One wrong decision by a machine might
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Source: McKinsey Global institute
In 2016, companies invested
$26B t0 $39BIn artificial intelligence
TECH GIANTS
$20B t0 $30BSTARTUPS
$6B t0 $9B
3x External investment growth since 2013
not amount to much, but they can have a
material financial impact in aggregate. It’s
the CFO who will have to answer for any
fiscal impact, making it important for them
to understand how the algorithms operate,
make decisions, and what the ramifications
might be for shareholders.
In spite of the initial risks associated with
any new technology, AI and Machine
learning is shaping up to be the next major
evolution in the transformation of finance
CFOs should prepare for. CFOs might want
to explore the following ways to unlock the
value machine learning has to offer.
Start experimenting with data
Machine learning is about data
experimentation, hypothesis testing, fine
tuning data models and automation. CFOs
should consider using innovation labs,
ideation forums, and create skunk work
project teams where developers can bring
together a discrete data set that hasn’t been
tested before and use machine learning to
identify hidden patterns. It will help assess
the potential risks, before they are put into
production.
The National Health Service in the United
Kingdom delivers healthcare to more than
60 million citizens of the UK. By using AI
to learn more from its huge volume of
patient data, they redesigned the health
card application process over three
months by using variance detection to find
fraudulent activity. By delivering value in
a short timeframe, they received backing
to expand. Now they have a long-term
strategic goal of saving £1 billion over
5 years.
Put data under the business ethics lens
At the 2015 Gartner Business Intelligence &
Analytics Summit in Munich, Gartner shared
an estimate that half of business ethics
violations will occur through the improper
use of big data analytics by 2018. This can
lead to a loss of reputation, limit business
operations, losing out to competitors,
inefficient or wasted use of resources, and
legal sanctions.
Therefore, CFOs should examine all the
potential ramifications before putting their
experimented data findings into practice,
including any legal, financial, and brand
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implications. Ideally, an expert committee
on business data ethics should audit
algorithms for unintended consequences,
thus reducing the risks associated with
machine learning.
Figure out the high-value data
Today, due to the humongous rate of
data generation, even small to midsize
companies collect far more information
than they can ever utilize. Only a fraction of
it will ever hold predictive value. So, CFOs
should carefully decide which data might
be worth something, and which data sets
can be discarded. Some of it may need to
be kept for regulatory purposes; others,
for commercially useful predictions and
products. Keep only what is needed and
what is potentially valuable.
Identify processes where AI and MLL can bring value: better, faster and cheaper
CFOs must continually make choices about
how they allocate resources. There’s always
internal tussle for funding to pursue new
business opportunities, and an investment
in one area requires savings in another.
AI-based machine learning can enable
increased savings by taking automation to a
much higher level than previously possible.
In many companies, a high percentage
of staff still perform transactional tasks
that can be automated through machine
learning. By letting self-learning algorithms
find patterns and solutions in data instead
of following preprogrammed rules,
transactional tasks can be completed
exponentially faster and with fewer people.
Back-office processes like procure-to-pay,
order-to-cash, and record-to-report can be
radically automated as business networks
eliminate manual work.
Invest in developing future skills set
The real value in AI and machine learning
are about gaining control, identify pain
In many companies, a high percentage of staff still perform transactional tasks that can be automated through machine learning.
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areas and bringing improvement by using
advanced AI-based business solution, to
drive the business forward, which is one of
the fundamental responsibilities of CFOs.
For CFOs, it also means automating as
much as they can, moving away from the
traditional accounting tasks of performing
transactions, reconciling accounts, and
compiling reports. With the automation
of transactional tasks, CFOs and their
teams can focus on partnering with the
business to analyze available data, identify
new business opportunities, and provide
strategic guidance. At the same time,
they must consider how to train, develop,
and create a future-ready talent pool for
changing business models. By collaborating
with professional accounting bodies, CFOs
can offer continuous learning opportunities
to their critical talent pools.
Align finance to the overall digital strategy of the enterprise
CFOs need to actively start taking part in
the organization’s discussions about digital
transformation. Being part of this strategic
conversation helps generate the required
momentum and reduce resistance to
change during the implementation phase.
CFOs must be adequately aware of the
expected digital angles to help solidify the
organization’s digital strategy so that when a
business case is up for review, they are well
informed and can make the right decisions.
As AI and machine learning evolves, CFOs
should make proactive efforts to familiarize
themselves with its business opportunities.
AI and ML: Implications for the present
AI and machine learning are no longer
the upcoming trend of the future. With
significant investment and technology
maturity advancement already started,
many firms are actively exploring the
technology and identifying practical ways
it can impact the business. Some have
already begun adoption, as revealed by
the prediction that the market for AI-based
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solutions will experience a compound
annual growth rate of 55.1% over the 2016-
2020 forecast period. Furthermore, almost
25% of today’s jobs are expected to be
impacted by AI technologies by as soon as
2019, according to Forrester.
For CFOs, that means they need to observe
these vital indicators not just for future
investment decision making but also drive
discussion to bring them into the digital
transformation objectives.
A true digital transformation program
requires more than just applying the latest
technology. It needs a customer-focused,
outside-in perspective to empower
the design of digital solutions that can
drive customer loyalty, engagement,
consumption and satisfaction. AI and
machine learning can be the key to
providing the capability, insight and
acceleration that enable tomorrow’s
business to thrive in this environment.
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