How Deloitte Uses AI to Simplify Reporting and Increase Value

© 2018 Amazon Web Services, Inc. or its Affiliates. All rights reserved.
How Deloitte uses AI to
simplify reporting and
increase value
May 30, 2018 | 10:00 AM PT
© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.

Kris Skrinak, Machine Learning segment lead, Amazon Web
Services, Inc. (AWS)
Ryan Kurt, VP of Partnerships, Narrative Science
Sheetal Parikh, Senior Manager of Innovation, Deloitte LLP
Today’s speakers

• An overview of machine learning (ML) solutions offered through
AWS and the AWS Partner Network
• Featured AWS Machine Learning Partner: Narrative Science
• Case study: Deloitte
• Q&A / Discussion
Today’s agenda

Learning objectives
• How natural language generation (NLG) can solve problems around
internal reporting, operational efficiency, and regulatory compliance
• How Deloitte delivered transformative solutions both internally and to
clients on AWS while saving over $600K
• How to get started with NLG in your organization

Machine learning on AWS

Fulfillment & logistics
Search &
discovery
Existing products
New products
At Amazon,
we’ve been making
investments in
machine learning for
the last 20 years…

Our mission:
Put machine learning in the
hands of every developer and
data scientist

Source: McKinsey Global Institute, Artificial Intelligence The Next
Digital Frontier.
• Strong overall appetite for
adopting AI
• Top heavy in High Tech due to
expertise
• Opportunities exist in Health
Care, Education, Retail, and other
segments
• 3000+ startups today (up from 100
in 2011)
Market adoption: $46B market by 2020

A long heritage of machine learning at Amazon
Personalized
recommendation
s
Inventing
entirely new
customer
experiences
Fulfillment
automation
and inventory
management
Drones Voice driven
interactions

New: Amazon Rekognition Video
Object and activity
detection
Person
tracking
Face
recognition
Real-time live
stream
Content
moderation
Celebrity
recognition
Video analysis

Amazon Machine Learning Stack
Vision
Frameworks &
Infrastructure
AWS Deep Learning AMI
GPU
(P3 Instances)
MobileCPU
IoT (Amazon
Greengrass)
Platform
Services
Application
Services
Amazon
SageMaker
AWS
DeepLens
Amazon
Rekognition
Image
Amazon
Rekognition
Video
Speech
Amazo
n
Polly
Amazon
Transcribe
Language
Amazon
Translate
Amazon
Comprehend
Amazo
n
Lex
Amazon Machine
Learning
Amazon Spark &
Amazon EMR
Amazon
Mechanical Turk
TensorFlow GluonApache MXNet Cognitive Toolkit Caffe2 & Caffe PyTorch Keras

Customers running machine learning on AWS
today

The AWS Competency Program is designed to
highlight APN Partners who have demonstrated
technical proficiency and proven customer success
in specialized solution areas. Attaining an AWS
Competency allows partners to differentiate
themselves to customers by showcasing expertise in
a specific solution area.
W H AT IS TH E AW S
C OMPETEN C Y PR OGR A M?
The AWS Machine Learning Competency

Narrative Science:
Using Natural Language
Generation (NLG) to generate
stories from data
Ryan Kurt, VP of Partnerships, Narrative Science

17
Data
Analyze Communicate
Act
Natural Language Generation (NLG)

“By 2018, 20% of all business content will
be authored by machines.”
Gartner: Predicts our Digital Future

in Funding
$43M
(and 17 more pending)
17 Employees
130Patents Issued Enterprise
Customers
100 Chicago, Wash. D.C.,
New York, Seattle
Enterprise Customers IncludePartners Include

20
Information Consumers
Give them the actionable
information that they need.
DRIVE
VALUE
Data Analysts
Allow them to focus on the
most high-value analysis.
DRIVE
EFFICIENCY
Your people are your most valuable resource

21
Interactive data discovery Standardized reporting
The Narrative Science Product Portfolio
DYNAMIC NARRATIVES
Integrate interactive stories in
existing data and analytics tools to
quickly communicate insights from
decentralized data discovery work
QUILL
Automate comprehensive and
customized narrative reports for
standardized or customer-facing
communication and reporting

OUT-OF-BOX
NARRATIVES
EXTENSIBLE
API
REAL-TIME
AUGMENTED
ANALYTICS
Dynamic Narratives: Interactive, Integrated Narratives
Go from data to
narratives, immediately.
Uncover insights not
obvious when looking at
visualization alone.
Easily integrate into BI &
analytics platforms.

24
DRIVE
VALUE
DRIVE
EFFICIENCY
Fuel innovative product
development and increase adoption
of BI & analytics tools by making data
and visualizations easier to consume
Reduce time finding and sharing data
insights by automating the
interpretation and communication of
data-driven information
Why Dynamic Narratives?
“Narrative Science is turning data from all
our partners into compelling stories. We can
now provide real-time analysis and
automated big data reporting, putting the
power of that data into customers’ hands.”
“Narrative Science offers us opportunities to
more efficiently sift through large amounts
of data and bring out insights more quickly.”

Narrative Science Quill: Customer Case Study
25
A large financial institution has an ERM team of 50+ that must produce thousands of risk reports on all
counterparties they purchase or sell loans to / from. These reports explain the health of the lending parties,
covering dozens of types of loans, and are inherently valuable from a risk-management perspective.
Desired State:
• Automate manual processes
• Increase employee
retention
• Decrease time to market
Existing Workflow:
• Manually write due diligence reports
• Report on 6,000+ mortgage lenders
• Each report is 5 – 10 pages long
• Takes 30% of analyst’s time
Pain Points:
• Resource constraints
• Lack of employee engagement
• Inability to scale
• Inconsistent reporting

Data Input & Narrative Output
Data points include:
• Financial group data
• Peer group data
• Historical data
26
Input Output

27
Outcomes and KPIs
Quill enabled $1,500,000 in annual savings by automating counterparty risk reports.
CUSTOMIZABLE &
scalable narratives
available on-demand
CONSISTENT
and comprehensive
reporting that objectively
states the facts
REALLOCATION
of resources to high-value
activities

Headline Verdana Bold
Deloitte and Narrative Science
US Innovation
May 2018

Deloitte’s Catalyst Fund
Cloud
Blockchain
AR / VR
Automation
Internet of ThingsArtificial Intelligence
NLG
NLP
Enabling Deloitte and our clients to more easily tap the power of innovation
Bring viable PoCs to production and
support scaling across Deloitte’s clients
Define impactful use cases and execute
Proofs of Concept (PoCs) for clients
Scan for technology providers and
explore relationships with key players
SENSE & DISCOVER GRADUATE1 2 3INCUBATE
SUPPLY
Existing capabilities are
fragmented across start-
ups and marquee firms
DEMAND
Our clients increasingly
recognize the need to
digitally transform

Trust and Disrupt
Deloitte Cloud practice delivers
industry-specific, secure platforms built
for speed and agility.
Via advanced Consulting relationships,
Deloitte guides clients through the
entire cloud transformation.
Successful implementation with a UK rail provider:

Copyright © 2017 Deloitte Development LLC. All rights reserved. 31
16 PoCs completed across all four Deloitte business
lines
4 use cases in production internally, helping our Tax,
Audit, and Consulting practices serve their clients better
Internal Experimentation Client Proofs of Concept
Since 2016, Deloitte has been working to prototype and scale
various NLG solutions
Deloitte’s Consulting and Advisory businesses are
bringing NLG solutions to market for their clients
Deloitte & NLG
Across Target industries:
Across Target functions:
Federal Pharmaceuticals
Hospitality FSILife Sciences
Operations
Supply Chain
Finance Transformation
Compliance
9 client PoCs
SynTax for GES Tax – creates customized tax
return commentary for thousands of tax clients
Actuarial EDGE – transforms actuarial data
into automatic summary memos to save
thousands of hours in the actuarial practice
Audit FluxAnalysis — automatically
produces a trial balance variance analysis
workpaper for use by Deloitte’s audit practice
Profit & Loss Flash Reporting— generates
dashboard commentary to support executive
reporting in Deloitte’s finance department

The Future of Finance Functions
Decision
Support
Finance Reporting
& Planning
Data Processing
Strategic
Advice
Finance Reporting
& Planning
Data Processing
Business Insight
& Decision Support
Traditional View Emerging View
Finance organizations are shifting their focus to more analysis and value-add activities

The Case for Natural Language Generation in Finance
Finance spends the majority of its time creating
and updating reports, despite wanting to spend
more time on strategic business-facing activities.
0%
10%
20%
30%
40%
50%
60%
70%
Creating and
updating
reports
Interacting and
communicating
with the
business
Current vs Ideal Time Spend
from Deloitte Survey of over 600 finance professionals
Current time
spent
Preferred time
spent
“We are drowning in information but starved for knowledge” – John Naisbitt
74% of firms
want to be
“data
driven”…
…but only 29% say they
connect analytics to actions.
The vast amount of data collected can be mined to reveal insights for clients and internal stakeholders

Identifying Value and Prioritizing Use Cases for NLG
Feasibility
Requirements
Value
Considerations
Automate Existing Communications Create New Content and Communications
Data
Reports are driven by data and that data is
structured (e.g. .xls, SQL tables, JSON)
The organization has unexplored and
underutilized stores of structured data
Logic
The current production process intent
follows a repetitive and consistent set of
logic
The organization has a vision for insights it
would like to glean from its information
Audience
The reporting function serves a broad
audience that requires personalized
content
Key decision-makers would benefit from
automatic, real time explanations of data
Frequency
Resource constraints limit the turnaround
time from data to insights and reporting
rate
Insights should be automatically delivered
every month, every week, or on demand
Consistency
Existing reports require review to meet
regulations or internal compliance
standards
The organization could take a proactive
rather than reactive approach to
compliance
Timespend
The organization spends time on
repetitive, aspects of researching and
writing reports
The organization doesn’t have the time or
resources to dedicate to communications
Insights
There isn’t enough time to deliver
advanced and personalized insights to
stakeholders
The insights found by the organization are
not communicated to the decision makers
in terms that they can understand

Applications of NLG in Finance Functions
Flash
Reports
Profit
and
Loss
Variance
Reports
Supplemental
Schedules
Sales &
Margin
Reports
KPI
Reports
Earnings
Reports
Spend
Reports
Staffing
Analysis
Fraud Risk
Assessments

A Practical Example of NLG in Finance
At a Fortune 100 company, the global finance organization has automated
monthly financial reporting packages for its emerging markets regions.
Using the NLG platform, the
organization could draft reports in
minutes rather than weeks.
C-Suite
FP&A
Leaders
Ad-Hoc
Requesters
Executive
Report
Management
Report
Operational
Report
C-Suite
FP&A
Leaders
Ad-Hoc
Requesters
Analysts
Prior to using NLG, the client needed to engage more
than 40 analysts around the world, which created
multiple hand-offs and a monthly web of complexity.
Current State Process Process Enhanced with NLG

Leaders at the firm were experiencing information
overload and needed a way to view insights from
large quantities of data in a timely manner that would
call their attention to specific areas of the business
Context
Summary
Tailored narrative
resulted in a
revamped flash
reporting tool with
variance
commentary
Generated ~$600K
savings in Reporting
function, freed group
to focus on more
value added
activities
Identified customers
purchasing habits
Piloted across pricing
and profitability
function, resulting in
net savings of ~12
FTEs
Worked with business
leaders to define the key
business questions,
opportunity and approach
1
Utilized NLG technology to
generate easy-to-read
narratives at scale while also
transforming data
2 3
Identify Key Business
Questions
Transform Data via NLG
Layered in additional insights
and provided specific
recommendations resulting
in insightful data points
Customize Reporting Data Driven Insights
Automated functionality of
outputs, maximized
individual productivity and
operational efficiency
department-wide
4
Data overload slowed business leaders from
making data driven decisions
Dashboards were not sufficiently tailored
thus unable to provide meaningful insights
Objective was to improve report narratives
in new areas without adding headcount
Strong relationships with external vendor
simplified the effort of launching the
initiative
Approach
Results
Profit and Loss Reporting | Case Study
Although dashboards were available,
none were able to produce the
necessary commentary and necessary
tailor without significant manual effort
Finance was able to provide automated and
actionable insights via Natural Language
Generation (NLG) technology to the business by
partnering with an external vendor

Thank you!
Sheetal Parikh – shparikh@deloitte.com

Q & A

Learn more about Narrative Science on AWS
• narrativescience.com/Partners/Partner-Network/AWS
Learn more about machine learning on AWS
• aws.amazon.com/machine-learning/featured-partner-solutions/
Try AWS for free:
• aws.amazon.com/free/
Next steps and further information:

Thank you!

How Deloitte Uses AI to Simplify Reporting and Increase Value

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