In this new Accenture Finance & Risk presentation we explore machine learning as a solution to some of the most important challenges faced by the banking sector today. To learn more, read our blog on Machine Learning in Banking: https://accntu.re/2oTVJiX

1. MACHINE LEARNING IN
BANKING
FINANCE
& RISK

2. WHAT IS MACHINE LEARNING?
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2
What Is It? Why now?Why is it useful?
Machine
Learning
Artificial
Intelligence
Data
Mining
Statistics
• Machine Learning is an
application of Artificial
Intelligence (AI) that allows
computers to learn without
being explicitly programmed to
do so.
• It’s the product of established
statistical theory and more
recent developments in
computing power.
• The volume, variety, velocity
and veracity of data is
increasing at an exponential
rate. Banks need to make use
of the wealth of data they own.
• Machine Learning allows banks
to very quickly draw valuable
insight from their data, reducing
risks, automating processes
and improving customer
engagement.
• With falling profit margins,
increasing customer
expectations and increasing
competition from Fintechs
(financial technology firms),
banks need to cut costs and
improve their offering.
• The ability to extract value from
such vast amounts of data has
never been cheaper or more
effective.

3. HOW DOES IT LEARN?
MACHINE LEARNING ALGORITHMS ARE CATEGORISED AS BEING SUPERVISED
OR UNSUPERVISED. THE FORMER CAN APPLY WHAT HAS BEEN LEARNED IN THE
PAST TO NEW DATA. THE LATTER CAN DRAW INFERENCES FROM DATASETS.
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3
Feedback
Training Data
Collect and prepare relevant data to
support analysis. If the learning objective
includes “expert” judgment, also collect
the historical “right answers.”
Algorithms
Algorithms learn to recognise patterns in
training data. Teach the programme how
to know when it is doing well or poorly,
and how to self-correct in the future.
Trained Machine
Machine is now trained and ready to
spot patterns in real world examples
in order to drive business value
Supervised Learning
What? Output variable specified. Algorithm learns
mapping function from input to output
Why? To make predictions
Example: Predicting credit default risk
Unsupervised Learning
What? Output variable unspecified so algorithm looks
for structure in data
Why? To describe hidden distribution or structure of
data
Example: Customer segmentation and product targeting
Determine Objective
Decide what you would like the
machine to handle that has
previously been done based on
expert knowledge or intuition.
OR

4. MACHINE LEARNING AS A SOLUTION
MACHINE LEARNING OFFERS SOLUTIONS TO SOME OF THE MOST IMPORTANT
CHALLENGES FACED BY THE BANKING SECTOR TODAY.
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4
2017 Financial
Services
Challenges
Cost
Reduction
Recruit/
Retrain
Talent
Regulatory
Compliance
Customer
Engagement
Security
CompetitionThrough unsupervised learning techniques,
banks can segment their customers and
offer a personalised, targeted product
offering.
Customer Segmentation
Machine Learning offers
significantly improved fraud, AML
(Anti-Money Laundering) and credit
risk detection possibilities.
Fraud & AML Detection
Compliance through automated reports,
stress testing solutions, and behavioral
analysis of emails and phone recordings to
determine suspicious employee behavior.
Compliance
Investment in Machine Learning offers
banks the speed and agility they need to
compete with tech-savvy Fintech firms and
to make use of Big Data.
Big Data & Agility
Combined with Robotics, Machine
Learning offers the ultimate automation
potential with many back office risk,
finance and regulatory reporting
processes contenders for automation.
Cognitive Automation
Digital skills are in short supply in FS.
Algorithms can evaluate CVs of
successful employees and search for
and identify online candidates with
similar traits and experience.
Natural Language Processing

5. Judgement Based
COGNITIVE AUTOMATION (1/2)
MOST BANKS HAVE GROWN ORGANICALLY, MEANING THEY HAVE A WEB OF
OVERLY COMPLEX PROCEDURES BUILT ON MULTIPLE LEGACY PLATFORMS.
DEVELOPMENTS IN ROBOTICS AND MACHINE LEARNING MEAN AUTOMATION OF
THESE PROCESSES IS NOW MORE FEASIBLE AND POWERFUL THAN EVER.
Copyright © 2017 Accenture. All rights reserved.
BusinessImpact
Nature Of Work
Rules Based
TransformationalTactical
Foundation
Simple, ad-hoc, project level
automation that can undertake
simple rule-based actions of a
single task within an application
when prompted (e.g. macros).
Robotic Process Automation
Also rule-based, but robots can
respond to external stimuli and
have their functions reprogrammed.
They can open and move
structured data between multiple
applications, from legacy systems
to third party APIs (application
program interfaces).
Cognitive Automation
Self-learning, autonomous
systems driven by Machine
Learning and Natural Language
Processing (NLP) that can read
and understand unstructured
information and instruct a
computer to act.
Understanding the Automation Landscape

6. COGNITIVE AUTOMATION (2/2)
ACCORDING TO A 2013 STUDY BY OXFORD ACADEMICS, ABOUT 54% OF
FINANCIAL INDUSTRY JOBS ARE AT HIGH RISK OF BEING AUTOMATED.1
COGNITIVE AUTOMATION HAS THE POWER TO AUTOMATE MANY F&R
PROCESSES, IN PARTICULAR RISK AND REGULATORY REPORTING.
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Cognitive Automation In Action – Document Processing Example
1 42 3 5
Open Email Classify according
to type
Comprehend &
extract relevant
information
Validate information
against rules
Populate data into
Enterprise Resource
Planning system
Machine
Learning & NLP
Machine
Learning & NLP
Robotics
Machine
Learning & NLP
Robotics
Process&Technology
• Robotics can be thought of as the ‘hand’ work and cognitive the ‘head’ work – together they form a powerful alliance and can
automate even those processes that involve comprehending unstructured text or recognising voices, and making subjective decisions
• Benefits of cognitive automation include:
 Reduce headcount and associated operational costs
 Decreased cycle times for processes that can operate 24 hours per day (e.g. risk/regulatory reporting)
 Improved accuracy – reduction of human error
1. “Which finance jobs are safe from robots and automation?”. Silicon Angle, May 31,, 2016. Access at: http://siliconangle.com/blog/2016/05/31/which-finance-
jobs-are-safe-from-robots-and-automation/

7. DEEP DIVE 1: FRAUD DETECTION
FRAUD COSTS THE FINANCIAL INDUSTRY $80BN PER YEAR.1 WITH
REGULATIONS EVOLVING IN RESPONSE TO THE FINANCIAL CRISIS, AND
TECHNOLOGY DEVELOPING AT AN EXPONENTIAL RATE, BANKS SHOULD INVEST
IN THE LATEST SOFTWARE TO REDUCE THEIR EXPOSURE TO RISK.
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Method Human Involvement AccuracySpeed
Machine
Learning
Traditional
Detection
Machine Learning
Summary
 Lower fraud losses
 Lower operational
costs
 Improved customer
service
 Reduced
reputational risk
 Reduced
regulatory risk
• Algorithms analyse historical transaction data
for each customer to understand their individual
spending patterns. They can therefore spot
subtle anomalies that indicate fraud.
• Algorithms self-learn, meaning they quickly adapt
to new means of fraud, and can stay ahead of
fraudsters.
• Rely on pattern matching against recognised
past fraud types. Transactions then assessed
based on general rules, such as whether the
customer is buying abroad.
• Humans to identify trends and manually update
their models to account for changes in fraudulent
activity.
• Low
• Automatic -humans to
maintain the
algorithmic models.
• High
• Preventive over
corrective, meaning
higher rates of fraud
detection and fewer
false alarms.
• High
• Real-time, automatic
reviews of
transactions using
vast amounts of data
from multiple
sources.
• High
• Requires significant
manual analysis and
review, with regular
updates to fraud
systems.
• Medium
• Often corrective over
preventive with
limited use of data,
meaning lower
detection success
rates.
• Medium
• More human
involvement, often
using audit trails to
identify fraud.
• Less computing
power.
Credit Card Fraud Detection Scenario
1. “Using machine learning and stream computing to detect financial fraud,” IBM Research. Access at: https://www.research.ibm.com/foiling-financial-fraud.shtml

8. DEEP DIVE 2: CREDIT RISK
MACHINE LEARNING ALLOWS FOR PROACTIVE RISK MANAGEMENT, REDUCING
EXPOSURE TO CREDIT RISK WHILST ALSO OFFERING A FASTER, MORE
EFFICIENT PROCESS TO CUSTOMERS.
Copyright © 2017 Accenture. All rights reserved.
Credit
Risk
Workflow
Origination Appetite &
Limit
Setting
Virtual advisors can understand customer
questions and instantly provide well-informed
responses, improving customer service levels.
Automation of labour intensive processes, e.g.
risk, finance and regulatory reports, to cut
costs and improve speed of output.
A machine learning–enhanced EWS allows automated
reporting, portfolio monitoring, and recommendations
for potential actions, including an improved approach for
each case in workout and recovery.
Supervised learning algorithms learn from past
events in a data-driven manner. They can
incorporate vast amounts of internal and
external information to more accurately predict
potential scenarios, allowing for better risk
planning.
• More accurate, instant credit default
likelihood prediction based on both
quantitative and qualitative data.
• Removes requirement for manual fact
checking, approvals and complex workflows.
• Real time credit decisions could allow for
instant, self-service credit applications.
Virtual Advisors
Robotics & Cognitive Automation
Early Warning System (EWS)
Stress Testing
Credit Default Prediction
Credit
Analysis
&
Decision
Loan
Admin/
Reporting
Monitoring

9. DEEP DIVE 3: TRADING FLOORS
THE AUTOMATION OF INVESTMENT ADVICE AND TRADES UTILISING A VAST
ARRAY OF INTERNAL AND EXTERNAL DATA HAS ABILITY TO SIGNIFICANTLY
IMPROVE PERFORMANCE ON TRADING FLOORS AND CUT OPERATING COSTS
Copyright © 2017 Accenture. All rights reserved.
Machine
Learning on
Trading Floors
Performance
• Algorithms autonomously evolve and search
for new patterns in data, making real-time
high-frequency trading decisions to exploit
volatility in stock.
The Opportunities We See:
Automated Trades
• Deal orders, execution and settlement of trades
and the analysis and monitoring of risk
automated, significantly reducing costs.
Compliance
• Undertake behavioral analysis by reviewing trade activity
for each employee alongside mining chat-logs and emails
to identify suspicious activity.
Robo-Advisors
• Provide algorithm-based portfolio management
advice without the requirement for financial
planners.
• Anticipate changing investments needs as
client circumstances change, improving
customer service levels.
• Ability to utilise external data such as stock prices,
Google™ searches and news articles to strengthen
pre-trade predictions.
• Continuous and real-time, resulting in the ability to
prevent non-compliant activity.
 Automated Investments – Cost Saving
 Improved Investment Decisions
 Enhanced Operational Speed and Accuracy
 Navigation of Large Amounts of Data
 Reduction in Human Error
 Lower Compliance Risk
 Enhanced Customer Experience

10. DEEP DIVE 4: FRONT OFFICE
UNSUPERVISED AND SUPERVISED LEARNING TECHNIQUES ALLOW BANKS TO
TRULY UNDERSTAND THEIR CUSTOMERS AND PROVIDE THEM WITH A
PERSONALISED SERVICE WITH TARGETED PRODUCT OFFERINGS.
Copyright © 2017 Accenture. All rights reserved.
Customer Segmentation BenefitsProduct Targeting
• Through cluster analysis, an
unsupervised learning technique,
banks can discover distinct groups
in their customer base and see
similarities over several dimensions.
• Unlike supervised learning, they do
not need to define what
characteristics the computer should
be looking for.
• This way, banks can segment in
ways traditional analytics would not
allow.
• Customer segmentation discoveries
can be used to build predictive,
supervised models.
• Algorithms produce personalised
views of the most suitable products
for each customer, helpful for cross-
selling and up-selling.
• Since algorithms learn, they
recognise changes in customer
preferences in real-time and
therefore automatically adjust
product recommendations.
• Personalised, improved customer
offerings.
• Speed of service - banks recognise
change in behavior and respond in a
timely manner.
• Revenue can increase from
successful identification of cross-sell
and up-sell opportunities.
• Automated – reduced human
involvement.

11. APPLICATION CHECKLIST
THE FOLLOWING PURPOSE, PROCESS AND LOCATION CHECKLIST CAN BE USED
TO HELP YOU UNDERSTAND WHETHER MACHINE LEARNING CAN BE
SUCCESSFULLY APPLIED TO A PROCESS.
Copyright © 2017 Accenture. All rights reserved.
Location: Front, Middle
& Back Office
Purpose: Prediction?
Purpose: Segmentation?
Process: Big Data?
Process: Digital?
Process: Repetitive &
Judgement Based?
Checklist Why?
Supervised learning: Algorithms spot trends in historical data and use this to make
predictions based on new data.
Unsupervised learning: Machine Learning can spot differences and similarities not
visible to the human eye between each data point and make sensible groupings
based on these characteristics.
Processes that involve the use of paper and physical contact between people are
not applicable to Machine Learning.
Algorithms thrive off large datasets, offering better results. They also have the
computing power to analyse big data at speed.
Algorithms learn and improve from each repetition, and the automation of such
processes offers huge cost saving potential.
The advent of tools such as Natural Language Processing and Speech
Recognition mean that Machine Learning can be applied to processes with and
without customer/client interaction.

12. HOW TO GET MACHINE LEARNING RIGHT
AS MACHINE LEARNING IS ENJOYING A MOMENT OF RENAISSANCE, THERE ARE
IMPLEMENTATION CHALLENGES A BANK SHOULD CONFRONT TO BE
SUCCESSFUL.
Copyright © 2017 Accenture. All rights reserved.
• Older generations and less tech savvy
customers prefer human interaction to
communication with robots. An
education/marketing piece may be
required to highlight the benefits to the
customer.
• Judgement currently often trumps insights
in firms – a cultural shift will therefore be
required.
• Democratisation of use of analytics required
– there should be incentives to encourage
data sharing between business divisions.
• Introducing Machine Learning to a business
requires a shift in skillset requirements from
operational management to analytics and data
science.
• Banking data is often poor quality and
inaccessible as it is stored in siloes on
multiple legacy systems.
• Algorithms thrive off easily accessible,
large data sets. The integration of data
sources, ideally onto to a cloud platform, is
therefore key.
• Some self-learning models cannot be
traditionally validated and therefore may
be deemed insufficient by the regulator.
Thorough research into regulatory
requirements is recommended
ahead of implementation.
• There is a vast array of new and evolving Machine Learning
technologies. A thorough consultation process with digital
specialists is recommended ahead of any purchase.
Talent
Customers
Regulatory
Data
Tools
Culture
Machine
Learning
Challenges

13. HOW CAN ACCENTURE HELP?
ACCENTURE HAS BOTH THE BUSINESS EXPERIENCE AND THE TECHNOLOGICAL
KNOW-HOW REQUIRED TO HELP OUR CLIENTS IDENTIFY PROCESSES THAT CAN
BENEFIT FROM MACHINE LEARNING TECHNOLOGIES, AND HELP IMPLEMENT
THEM.
Copyright © 2017 Accenture. All rights reserved.
• We are the world’s largest system integrator of IBM technology and an
IBM-Technology Premier Business Partner
• We have multi-faceted relationships with specialist AI firms such as
Mighty AI, Inc.
• We have several Fintech Innovation Labs bringing together disruptive
innovators and corporates to help shape the future of industry
Innovation
Alliances
We extend our technology
and business capabilities
through a powerful alliance
ecosystem of market
leaders and innovators
FS Knowledge
We invest heavily in our
own innovation centres to
create applications tailored
to our clients’ needs
Our solutions are
embraced by 84% of the
top 50 banks worldwide*
• Accenture has been positioned as one of five leaders in the Gartner
Magic Quadrant for Business Analytics Services Worldwide (Feb 2017)
• Collette is our Digital Mortgage Advisor providing subjective advice to
customer questions**
• Accenture Finance & Risk provides clients with integrated offerings to
improve management of internal complexity, regulatory requirements
and capital decisions, and to enable long-term profitability
* Accenture Finance Services Index:
https://www.accenture.com/gb-en/careers/financial-
services-index
** Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select
only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner's research
organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this
research, including any warranties of merchantability or fitness for a particular purpose.

14. MACHINE LEARNING IN BANKING
Contacts
Matt Baker (matt.baker@accenture.com)
Darius Ansari (dariush.ansari@accenture.com)
Anaita Tejpal (anaita.tejpal@accenture.com)
Lilian Okorokwo (lilian.u.okorokwo@accenture.com)
Disclaimer
This presentation is intended for general informational purposes only and does not take into account the reader’s specific circumstances, and
may not reflect the most current developments. Accenture disclaims, to the fullest extent permitted by applicable law, any and all liability for the
accuracy and completeness of the information in this presentation and for any acts or omissions made based on such information. Accenture
does not provide legal, regulatory, audit, or tax advice. Readers are responsible for obtaining such advice from their own legal counsel or other
licensed professionals.
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