This year, the focus goes beyond technology to mining business insights around how cloud enables strategic industry trends such as Open and Virtual Banking and Insurance, Security and Compliance, Data Analytics and AI/ ML, FinTech and RegTech, Surveillance and more through sharing of best practices and use cases. In sessions led by customers, partners, industry leaders and AWS subject matter experts, you’ll learn how AWS helps financial institutions to focus on the innovation and outcomes that truly drive business forward. Business stakeholders, market makers, and technology owners will all learn something new, valuable and actionable.

2. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Mining intelligent insights with ease:
AI/ML for Financial Services
Osemeke Isibor
Partner Solutions Architect
Amazon Web Services
C A P I T A L M A R K E T S
Jerry Cao
Technical Director
ChinaScope Data Services

3. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

4. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Breakthrough
advances
Optimization and
automation
New features
for existing products
“After decades of false starts, artificial intelligence is on the verge of a
breakthrough, with the latest progress propelled by machine learning.”
McKinsey Global Institute, Artificial Intelligence The Next Digital Frontier?
AI and ML enable innovation at scale…

5. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
…and could revolutionize Financial Services
The Economist, May 25, 2017
“AI could contribute up to $15.7
trillion to the global economy in
2030…. Healthcare, automotive
and financial services are the
sectors with the greatest potential
for product enhancement and
disruption due to AI.”
Sizing the prize: What’s the real value of AI for your business
and how can you capitalise? PwC report, June 2017
Immense opportunities… …but huge risks of disruption

6. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
The potential impact of AI/ML is enterprise-wide
Compliance,
Surveillance, and
Fraud Detection
Pricing and
Product
Recommendation
Document
Processing
Trading Customer
Experience
• Credit card/account fraud/
theft detection
• Anti-money laundering/
Sanctions
• Investigations optimization
• Sales practices/
transaction surveillance
• Compliance processes
optimization
• Regulatory mapping
• Enhanced customer
service through voice
services and chatbots
• Call center
optimization
• Personal financial
management
• Loan/Insurance
underwriting
• Sales/recommendations of
financial products
• Credit assessments
• Contract ingestion and
analytics
• Financial information
extraction
• Common financial
instrument taxonomy
• Corporate actions
• Portfolio management/
robo-advising
• Algorithmic trading
• Sentiment/news analysis
• Geospatial image analysis
• Predictive grid computing
capacity management
AI/ML use cases are gaining traction in Financial Services

7. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
But overall the industry has been relatively slow to invest
Source: McKinsey Global Institute, Artificial Intelligence The Next Digital Frontier?
An ambivalent response to AI
 Strong overall appetite for adopting AI
 History of digital investment and strong
foundation for integrating AI
technologies
 Large volumes of data to support model
training and development
× Comparatively low investment in AI – in
2017 only 52% of surveyed organizations
indicated the industry was making
substantial investments in AI
Source: PwC for The Clearing House, Artificial
Intelligence and Bank Performance

8. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Our mission:
Put machine learning in the hands of every
developer and data scientist

9. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Our deep experience with AI/ML differentiates our approach
Product
recommendation
engine
Robot-enabled
fulfillment
centers
New
product
categories
Amazon has invested in AI/ML since our inception, and we
share our knowledge and capabilities with our customers
20181995
Natural language
processing-supported
contact centers
ML-driven supply
chain and
capacity planning
Checkout-free
shopping
using deep learning

10. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
M L F R A M E W O R K S &
I N F R A S T R U C T U R E
A I S E R V I C E S
R E K O G N I T I O N
I M A G E
P O L L Y T R A N S C R I B E T R A N S L A T E C O M P R E H E N D L E XR E K O G N I T I O N
V I D E O
Vision Speech Language Chatbots
A M A Z O N
S A G E M A K E R
B U I L D T R A I N
F O R E C A S T
Forecasting
T E X T R A C T P E R S O N A L I Z E
Recommendations
D E P L O Y
Pre-built algorithms & notebooks
Data labeling (G R O U N D T R U T H )
One-click model training & tuning
Optimization (N E O )
One-click deployment & hosting
M L S E R V I C E S
F r a m e w o r k s I n t e r f a c e s I n f r a s t r u c t u r e
E C 2 P 3
& P 3 N
E C 2 C 5 F P G A s G R E E N G R A S S E L A S T I C
I N F E R E N C E
Reinforcement learning
Algorithms & models ( A W S M A R K E T P L A C E
F O R M A C H I N E L E A R N I N G )
The Amazon ML stack: Broadest & deepest set of capabilities

11. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
As a result, enterprises across industries run AI/ML on AWS

12. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Select Financial Services AI/ML customer stories
And financial institutions are accelerating AI/ML adoption
Capital One is using AWS’s Lex
capabilities to transform the
way it interacts with and serves
its customers. Conversational
interfaces enable customers to
ask their Alexa-enabled devices
about their account balances
and spending patterns and even
make payments, creating a
much more personal, engaging
relationship with the bank while
maintaining security.
Bloomberg, a leading provider
of financial news, uses Amazon
Polly for text-to-speech
conversion of news articles
from its website. Amazon Polly
enables Bloomberg to provide
automated audio capabilities in a
scalable way to meet growing
demand. The company has
found that people who use the
audio capability tend to use it a
lot, listening to two to three
articles per session on average.
Moody’s provides credit ratings,
research, tools, and analysis
that c​ontribute to transparent
and integrated financial
markets. Working with AWS
Machine Learning Solution Labs,
Moody’s was able to look inside
printed and scanned documents
and extract—with close to 100%
accuracy—the data inside
financial reports for better
analysis.

13. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Our Partners Can Help You Get
Started

14. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
AWS partners accelerate solution implementation
Consulting partners Technology partners
Support conducting workshops, building models,
developing PoCs and implementation plans, and
meeting security and regulatory obligations
Technology solutions to help customers
accelerate their implementation of Machine
Learning techniques to drive their business
Customers requiring support have several options depending on their specific needs

15. Data Intelligence Driving Financial Innovation
AI Based CHINASCOPE DATA FEEDS

16. About ChinaScope
Data Extraction
Data Normalized
Data Synthesis NLP
Knowledge Graph
Supply Chain
Mainland
Stocks
HK-Listed
Stocks
Unlisted
Comps
Quant
Fundamental
News
Since 2009,
ChinaScope has
persistently focused
on the extraction
and interpretation of
unstructured and
semi-structured data
relating to China

17. Data Products
17
Fundamentals • A shares, H shares, and companies listed on the NEEQ
SAM
Segment Analysis & Mapping
• Comparison of companies across markets not based on pre-determined
industry classification, but based on exactly what products and services are
provided and their contribution to total business volume.
• All product and services are linked together into a supply chain map.
Announcements • A-share announcements classified into 900+ event categories
SmarTag
Machine read
Chinese language news
• Automatic identification of company, people, products, industries, events,
and topics in news based on ChinaScope’s proprietary NLP algorithms.
• Sentiment scores on articles and entities within articles
Knowledge Graph
• China listed companies connecting over 200,000 companies through
relationships of supply chain, market competition, business transactions and
ownership structures.
1
2
3
4
5

18. SAM – Business Lines Normalized Across Companies
ChinaScope’s SAM (Segment Analysis Mapping) standardizes the disclosed business lines of 20,000 companies
listed in China and Hong Kong
Each business line is connected with the GICS industry classification, effectively creating further layers of
subcategories of GICS from 4 layers into 13 layers
Chinese companies on average operates in THREE different business lines, identifying the company with a single
industry classification can be misleading when it comes to risk assessment
18
原始披露
Original Disclosed
数库标准
(SAM， ChinaScope Standardized)
收入占比
消费电器
其他小家电
Other Small Home Appliances
41%
暖通空调
空调器
Air Conditioners
39%
机器人及自动化系统
工业机器人
Industrial Robots
11%
利息收入
小额贷款业务
Micro Credit Services
1%
Use Case : Midea Group’s SAM data
41%
39%
11%
1% 1%
7%
Midea Group's Revenue Structure by products
Other Small Home Appliances
Air Conditioners
Industrial Robots
Micro Credit Services
Others
Undisclosed
Note: Data goes back to 2007.
1

19. SAM Derivative - ChinaScope Supply Chain
The supply chain created by ChinaScope has developed the upstream and downstream relationship of an industry
into a multi-dimensional analysis tool.
19
M
Material
A
Appliance
E
Environment
T
Tertiary service
F
Further equipment
R
Reliance
D
Dealing
P
Primary
Use Case :
Note: Data goes back to 2007.
1

20. SmarTag：Machine Readable Chinese News
20Note: News goes back to 2008.
Flagship news analysis engine that deconstructs unstructured Chinese language text into structured machine-
readable metadata
Sentiment Analysis
ChinaScope takes a bilateral approach in producing sentiment scores, through polarity-based lexicon and
supervised machine learning. Sentiment scores are done on two levels:
 Article level: where a score is given for the entire article as a whole
 Entity level: where a score is given for companies and individual persons mentioned in the article
2

21. SmarTag：Machine Readable Chinese News
Event Tagging
ChinaScope tracks over 1,800 event categories in news, allowing for sentiment to be coupled with events provides
a rich mixture of company groupings that look at sentiment with various thematic dimensions
21
2
Note: News goes back to 2008.

22. SmarTag：Machine Readable Chinese News
SAM & Supply Chain Networks
SmarTag also identifies products and services and sector themes within articles that link up with ChinaScope’s
SAM and Supply Chain data schema
Used in conjunction with tagged company entities which have their own inherent SAM and Supply Chain tags can
help you track news sentiment across competitor themes and upstream/downstream verticals
22
2
Note: News goes back to 2008.

23. Example: LTM Daily Sentiment Analysis
23
 LTM Range: 1 Nov 2017 – 31 Oct 2018
 News Aggregation: 3:01 PM (D-1) to 3:00 PM (D-0)
 News Sources: Novel news from over 100 Chinese finance news sites
 Net Sentiment Score: LN[(Count Positive +1)/(Count Negative +1)] for constituent stocks in the CSI300 Index
 Position Weight of Constituent Stocks: Equal
 Index: China A-Shares CSI300
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
2,500
2,700
2,900
3,100
3,300
3,500
3,700
3,900
4,100
4,300
4,500
11/1/17
11/8/17
11/15/17
11/22/17
11/29/17
12/6/17
12/13/17
12/20/17
12/27/17
1/3/18
1/10/18
1/17/18
1/24/18
1/31/18
2/7/18
2/14/18
2/21/18
2/28/18
3/7/18
3/14/18
3/21/18
3/28/18
4/4/18
4/11/18
4/18/18
4/25/18
5/2/18
5/9/18
5/16/18
5/23/18
5/30/18
6/6/18
6/13/18
6/20/18
6/27/18
7/4/18
7/11/18
7/18/18
7/25/18
8/1/18
8/8/18
8/15/18
8/22/18
8/29/18
9/5/18
9/12/18
9/19/18
9/26/18
10/3/18
10/10/18
10/17/18
10/24/18
10/31/18
CSI300 Sentiment Linear (CSI300) Linear (Sentiment)
2

24. Example: LTM Rolling 3 Months Sentiment Analysis
24
 LTM Range: 1 Nov 2017 – 31 Oct 2018
 News Aggregation: 3:01 PM (D-1) to 3:00 PM (D-0)
 News Sources: Novel news from over 100 Chinese finance news sites
 Net Sentiment Score: LN[(Count Positive +1)/(Count Negative +1)] for constituent stocks in the CSI300 Index
 Position Weight of Constituent Stocks: Equal
 Index: China A-Shares CSI300
0.40
0.60
0.80
1.00
1.20
1.40
1.60
2,500.00
2,700.00
2,900.00
3,100.00
3,300.00
3,500.00
3,700.00
3,900.00
4,100.00
4,300.00
4,500.00
11/1/17
11/8/17
11/15/17
11/22/17
11/29/17
12/6/17
12/13/17
12/20/17
12/27/17
1/3/18
1/10/18
1/17/18
1/24/18
1/31/18
2/7/18
2/14/18
2/21/18
2/28/18
3/7/18
3/14/18
3/21/18
3/28/18
4/4/18
4/11/18
4/18/18
4/25/18
5/2/18
5/9/18
5/16/18
5/23/18
5/30/18
6/6/18
6/13/18
6/20/18
6/27/18
7/4/18
7/11/18
7/18/18
7/25/18
8/1/18
8/8/18
8/15/18
8/22/18
8/29/18
9/5/18
9/12/18
9/19/18
9/26/18
10/3/18
10/10/18
10/17/18
10/24/18
10/31/18
CSI300 Sentiment Linear (CSI300) Linear (Sentiment)
2

25. ChinaScope Knowledge Graph
Explore ChinaScope’s knowledge graph is based data extracted,
synthesized and normalized from company filings and news
 Covering 1,148,474 nodes and 3,764,490 relationships
 More than 50 types of relationships covering business cooperations,
transactions, ownership structures, personnel and supply chain.
What you can do with ChinaScope’s knowledge graph
 Discover relevant news to your portfolio through relationship pathway
mapping
 Discover risk and value relationships between companies and their
stakeholders through path finding algorithms
 Discover your risk profile with your clients by perform multi-dimensional
risk clustering analysis via community discovery algorithms
25
Listed
Compan
y
Shareholders
Subsidiarie
s
JV
Investment
Projects
Debt
Investmen
t
Guarantee
s
Related
Transactio
ns
Executives
Directors
Suppliers
Customers
Supply
Chain
Note: Data goes back to 2016.
3

26. ChinaScope Knowledge Graph
Use Case: Vertically integrate your portfolio through supply chain traversal analysis
26
3

27. 27
Trading Data Financial Data Fundamental Data Market Data
Split-Adjusted
Ratios
Capital Flows
Split-Adjusted Share
Prices (Backward
complex Rights)
Closing Prices
Split-Adjusted Share
Prices (Forward
complex Rights)
IPO
Suspension of
Listing
Date of Trading
suspension
Follow-on Offering
Stock Trading
Status
Public Information
on Stocks of
Abnormal
Fluctuations
Block Trading
Margin Trading
and Short Selling
Breakdown of
selling expenses
Breakdown of
administrative
expenses
Non-operating
income
Financial Expenses
Non-operating
expenses
Notes receivable
endorsed
Payroll payable
Financial
Statements
Interest-bearing
Liabilities
Non-recurring Items
Foreign Currencies
Allotment
Freezing of
Shares
Holding Reduction
Dividend
Intermediaries
Penalties
Asset Restructuring
Basic Information
Changes in Share
Capital
Industrial Changes
HK-Listed
A-Shares(1)
NEEQ
Fundamentals of Listed Companies（AHNEEQ）
Note: Initial data goes back to 1990.
(1) Includes CHINEXT stocks.
4

28. 964 Categories
Announcements
ChinaScope realized real-time automatic classification of A-share announcements. The categories at level III are similar to company
events which can be applied as factors for event-driven analysis;
The announcements in ChinaScope’s system date back to Year 1996.
28
109 Categories
11 Categories Level I
Level II
Level III Level III
Level II
Level III Level III
Restructuring
Restructuring
Program
• Restructuring Proposal
• Restructuring Agreement
• Restructuring Change
Case Real-time Automatic Classification of Announcements
Note: Data goes back to 1995.
5

29. ChinaScope AI Adoption in financial service
29
DAS(Data Automation
System)
NLP Analysis
Knowledge Graph
Applications
1. Announcement for
public companies
2. Research Report
3. Announcement for
Bond Issuer
4. SAM
5. Announcement for
public funds
6. IPO reports
7. Credit Reports
1. SmarTag
2. Sentiment Score
3. Classification for
announcement
4. Discover potential
investment targets
1. Unified Company ID
2. SAM relationship
3. Shareholder
relationship
4. Relationship with
news
1. iNews & SmarTag
2. SAM
3. Knowledge Graph
4. Fundamentals
5. Wechat Applets for
research report
6. NLP analysis services

30. Synergy between AWS and ChinaScope
30

31. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Ready To Start Building?

32. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Analyze the efficacy
of outbound call
campaigns
Determine
creditworthiness
based on customer
profiles
Predict the risk
of an accident
An ML data readiness evaluation uses an AWS Marketplace-accessible AMI to assess the
completeness and quality of financial institutions’ data to determine its value and drive
better decision-making.
Get started by performing an ML data readiness evaluation
Quantify predictive
potential in datasets
Identify
actionable
predictions from
datasets
Areas where ML data readiness analyses can help guide decision-making

33. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
ML Solutions Lab lets you leverage Amazon expertise
Companies have
numerous
opportunities for
Machine Learning
And are unable to
unlock business
potential
Brainstorming
Modelin
g
Teaching
But lack ML
expertise or scale
Leverage Amazon experts with decades of ML
experience with technologies like Amazon Echo,
Amazon Alexa, Prime Air, and Amazon Go
Amazon ML Lab
provides the
missing ML
expertise
Engage the ML Solutions Lab to harness the business value of your data

34. Thank you!
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Osemeke Isibor
Partner Solutions Architect
Amazon Web Services
Jerry Cao
Technical Director
ChinaScope Data Services