Robotic Process Automation (RPA) is certainly not a new concept. Yet, for whatever reason, it’s still surprisingly misunderstood. To truly understand the value proposition RPA offers companies, as well as debunk some misinformation, our panel of experts will: - Share ideas and approaches to leveraging RPA for both front and back office operations. - Discuss the practical applications of this emerging technology within the asset management industry. - Discuss why some RPA projects fail while others succeed.

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De-Mystifying Robotic
Process Automation
2017 NICSA
Midwest Regional Meeting
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Agenda
Welcome
• Lisa Shea, Co-Chair, NICSA Midwest Regional Committee
Panelists
• Amber Krueger, Moderator, US Bancorp Fund Services, LLC
• John Sjosten, Senior Manager, Deloitte & Touche LLP
• Randy Guy, Chief Technology Officer, FIS Global
• Andy Curtis, Data Analytics Manager, Northern Trust
Q&A
Roundtable Discussions
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Robotics & Cognitive
Intelligence
John Sjosten, Senior Manager, Deloitte & Touche LLP
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Digitization of white collar jobs via RPA & CI, and advances in data science have
sparked the Business 4.0 revolution
We are on the cusp of “Business 4.0”
BPM
Systems
Early RPA
Early cognitive
Widespread
RPA
Widespread cognitive
Ubiquitous global
horizontal MLPs
Business 4.0
• This revolution redefines what it means to
be a professional
• RPA has commenced deployment in most
large businesses
• RPA & Cognitive will be ubiquitous in
business by 2020
• Horizontal machine learning platforms
(MLPs) become ubiquitous by 2025
Industrial
revolution
1-3
4.0
2017 Within 10
years
$5bn 2020 RPA market 1
$31bn 2019 Cognitive spending 2
2nd most
important
strategic
priority
3.09
2.59
4.75
3.9
4.14
4.5
5.12
2.05
3.36
3.91
4.24
4.27
4.89
5.05
GBS model
Geo. scope
Analytics…
Increased func.…
Func. Proc.…
Automation
Contin.…
Today In ten yearsSource: Deloitte Global Shared Service Survey, 2015
Interest in
automation is
increasing at a
rapid rate
1 http://www.transparencymarketresearch.com/pressrelease/it-robotic-automation-market.htm
2 http://www.idc.com/getdoc.jsp?containerId=prUS41072216
3 https://www.slideshare.net/AIFrontiers/jeff-dean-trends-and-developments-in-deep-learning-research/8
Growing use of
Deep Learning at
Google3
What’s changed: Convergence of 20+ years of AI
research, Cloud Computing, Big Data and increased
computing power
Copyright © 2017 Deloitte Development LLC. All
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Robotic and cognitive automation solutions can increase
capacity and extend the capabilities of organizations
Criteria
 Rules-based, standard, repeatable processes
 Structured / digital inputs and outputs
 Human interaction with multiple systems
 Limited decision-making or interpretation
Criteria
 Unstructured inputs and outputs, including
documents, forms, handwriting, audio, etc.
 Customized, context-sensitive information
 Feedback through training or data to improve
algorithms over time (machine learning)
Sample Use Cases
 Opening e-mails and attachments
 Accessing web/enterprise applications
 Moving files and folders
 Extracting structured data from documents
 Filling in and validating forms
 Aggregating, validating/reconciling, transforming and
calculating data
Sample Use Cases
 Generating insights based on customer activity
 Developing fact-sheets and investment summaries
 Improving the effectiveness of electronic
communications monitoring
 Guidance through complicated workflow with the
use of intelligent agents
 Machine-learning-based exception management
and root-cause analysis
Cognitive
Intelligence
Robotic
Process
Automation
Software that can be configured to
undertake rules-based tasks, replicating
human action
Algorithms which can interpret, learn and
communicate, replicating human thought
Robotic Process Automation
(RPA)
Cognitive Intelligence
(CI)
By leveraging these capabilities, firms have begun to realize significant
improvements leading to enhanced quality, reduced cost and efficiency gains
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Development LLC. All rights reserved.
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Demo: Data Management & Reporting
What we will see
 Using RPA to automate the FR-Y9C report production process using Excel, internal document
management systems and databases
 Automating the data acquisition, reconciliation, aggregation, transformation and validation activities, and
using tools to generate exception reports and audit logs
 Eliminating up to 80% of manual processing, enabling analysts to focus solely on managing exceptions
and performing quality control checks
Securities
Lending
Security Master
& Pricing
Corporate
Actions
Regulatory
Reporting
Automating the report production process for
financial and non-financial reporting
Aggregating overnight loan reports from external
counterparties and balancing vs. stock record
Identifying discrepancies between data providers
or internal sources and generating exception
reports
Reconciling across pre- and post-payment data
Where this applies
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LLC. All rights reserved.
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Robotics, Machine Learning,
Cognitive Computing
Andy Curtis, Data Analytics Manager, Northern Trust
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WHAT DOES ARTIFICIAL INTELLIGENCE DO?
Value to Financial Services Industry
Smart Automation
(Learnt patterns)
AI Automation
(Cognitive ability)
AICapabilityLevels
Basic Automation
(Rules based)
Research
PoV & Prototyping
Production Rollout
• Document analysis – auto trade capture
• Transaction reconciliations
• Intelligent wealth advisory assistant
• Compliance surveillance & reporting
• Portfolio analytics and management
• Advanced client personalisation
Industry research and adoption is gaining pace as AI moves to mainstream
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WHAT ARE THE OBJECTIVES OF AUTOMATION?
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WHAT ARE SMART MACHINES?
Smart Machine
Automation
AdvancedBasic
• Technology is making great strides
in Smart Machines and Artificial
Intelligence
• The Fourth Industrial Revolution is
underway:
• Self-driving vehicles
• IBM Watson Jeopardy
champion
• Google DeepMind Go
champion
• Still a long way from true artificial
intelligent machine (i.e. the
Terminator)
• Basic process automation is the
simplest form of smart machine
automation
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ROBOTIC PROCESS AUTOMATION
Robotic Process Automation (RPA) refers to area of configuring “software” or “robots” to capture and
interpret existing applications in order to perform a repeatable set of activities in an automated fashion.
Features
• Virtual workers
• Faster setup
• Uses existing systems/applications
Benefits
• Quick wins and faster ROI
• Reduced risk and error rates
• Respond faster to business peaks and
troughs
• Enable users to do other cognitive tasks
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COGNITIVE + RPA DOCUMENT INJECTION
 Documents arrive to centralized ops center
 First several documents are unrecognized and must be “trained” via ML (classify + snap name/value)
 After training documents are auto classified, text and meta data extracted
 Passed on to RPA for business process completion
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ROBOTIC PROCESS AUTOMATION
Why Automation Projects Fail And How To Avoid This?
Randy Guy, Chief Technology Officer, Asset Management

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KEY THOUGHTS
Think strategic
and not tactical
RPA investment
continues to grow
“One size does not fit all”
– Don’t lock into a single
technology
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Top 5 Reasons RPA Projects Fail
Buying a tool in place of creating a strategy
Not setting up the proper organizational structures
Run as a project instead of a program
Process being automated is not well understood
Bringing in an RPA tool and ignoring what is in place

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Organization Initial Experience New Plan
Global telecom
organization
Automate Account Set-up, Operations and Closing
• RPA tool redundant with other in-house tools.
• Took many months to automate 2 FTEs
• Tool did not have the cognitive processing required
• Reviewed the process – got savings
through streaming the process
• Implementing cognitive solutions,
which will allow them to automate up to
80% of the process
Global
card provider
Automate Dispute processing
• IT purchased an RPA solutions – No prior business buy-in
• Tried to automate everything with workflows given by the
business, but not working as a team with the business – did not
automate the proper flows.
• Set up the proper organization and
realized savings of 1.1M.
Fitness product
provider
Automate fulfilment process
• Did not have expertise in automation – partner used for
fulfilment processing did not have the expertise in cognitive
solutions
• Got stuck with a process they felt could be automated, but did
not know how
• New partner: 30% discount on service
• Took on the risk of automation savings
• Partial automation solutions utilizing
cognitive solutions for processing
emails/ e- forms via NLP.
• Single screen interface for unifying
multiple applications and screens
• Leveraging BOTs to do the actual
processing.
Examples of RPA Failures and Turnarounds
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Expected cost based on project complexity
Complexity
Cost
BASIC
e.g. Update form from 1
app to another
HIGH
Judgement-driven
processes, e.g.
disputes management,
document management,
KYC, etc.
MODERATE
Standardized,
unstructured inputs: e.g.
Name, address change
form processing
$40K – $100K $100K – $175K $300K – $500K
Illustrative costs include software, resources and infrastructure costs.
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Questions
&
Roundtable Breakout Sessions
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