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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.

Rise of Machines Artificial Intelligence and Machine Learning for Digital CFOs

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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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