AWS Summit Singapore Opening Keynote

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Mai-Lan Tomsen Bukovec
Vice President and General Manager, Amazon S3, AWS
AWS Summit Singapore
Opening Keynote

Who are Builders?
I.T.
OPS
DATA
SCIENTISTS
DATA
PROFESSIONALS
CxO
BUSINESS
PEOPLE
DEVELOPERS

More than inputs / outputs
More than web content
Services and API-based
APPLICATION FOUNDATIONS
The “Application” is continuously evolving
APPLICATION CONTEXT
All data is relevant
Connected everything
Programmable everything
APPLICATION INTEGRATION
Responsive design
Smart connectivity
Notifications / push for all apps

APPLICATION CONTEXT
All data is relevant
Connected everything
Programmable everything
CONTINUOUSLY DELIVERED
FAST TIME TO MARKET
FAST FEEDBACK
Responsive design
Smart connectivity

Responsive design
Smart connectivity
FAST TIME TO MARKET
FAST FEEDBACK
APPLICATION CONTEXT
DEEP USER EXPERIENCES
FULLY-AWARE
ALWAYS ON

CUSTOMER SATISFACTION
CUSTOMER RETENTION
DIFFERENTIATION
APPLICATION CONTEXT
FULLY-AWARE
ALWAYS ON
FAST TIME TO MARKET
FAST FEEDBACK

APPLICATION CONTEXT
FULLY-AWARE
ALWAYS ON
FAST TTM
FAST FEEDBACK
CUSTOMER SATISFACTION
CUSTOMER RETENTION
DIFFERENTIATION

Digital Transformation in ASEAN

George Wang
SVPIT, Singapore Airlines

AWS Summit 2018
George Wang
SVPIT, Singapore Airlines

About Singapore Airlines
World’s Most
Awarded Airline
Top 50 Many Industry
Firsts

Digital Vision
Technology
Innovation
Service
Excellence

A Continuous Journey
Digitizing
The Core
Open
Innovation
Refactoring
Capabilities

Digitizing The Core
Personalization Operational Excellence Employee
Enablement

Refactoring Capabilities
Mindset, Skills
& Operating
Model
Technology Reimagined
Processes

ANALYTICS
AWS Services in Use Today
Amazo
n EC2
Elastic
Load
Balancing*
AWS
Elastic
Beanstalk
AWS
Lambda
Amazon
S3
Amazon
DynamoD
B
Amazon
RDS
Amazon
Redshift
AWS
Direct
Connect
AWS
CodeCom
mit
AWS
CodeDeplo
y
AWS
CodePipeli
ne
Amazon
EMR
Amazon
Route
53
Amazon
Cloudwatc
h
16x
Faster

Singapore Airlines - A Great Way to Fly

The Enterprise Runs on Cloud
RE-HOST

RE-HOST RE-PLATFORM

RE-HOST RE-PROVISIONRE-PLATFORM

RE-HOST RE-PROVISION RE-ARCHITECTRE-PLATFORM

RE-HOST RE-PROVISION RE-ARCHITECT
“Lift and Shift”
AWS Migration Hub
VMware on AWS
RE-PLATFORM

AWS Migration Hub
VMware on AWS
QUICK WINS
RE-PLATFORM

AWS Migration Hub
VMware on AWS
QUICK WINS
RE-PLATFORM
“Lift and Reshape”
AWS Database Migration Service
AWS Marketplace

AWS Migration Hub
VMware on AWS
QUICK WINS
RE-PLATFORM
AWS Marketplace
EASY OPTIMIZATIONS

AWS Migration Hub
VMware on AWS
”Pick and Choose”
Fully-managed services
SaaS
RE-PLATFORM
AWS Marketplace
QUICK WINS EASY OPTIMIZATIONS

AWS Migration Hub
VMware on AWS
SaaS
CLOUD BACK OFFICE
RE-PLATFORM
AWS Marketplace

AWS Migration Hub
VMware on AWS
SaaS
“Rewrite and decouple”
Microservices
Serverless
CLOUD BACK OFFICE
RE-PLATFORM
AWS Marketplace

AWS Migration Hub
VMware on AWS
SaaS
“Rewrite and decouple”
Microservices
Serverless
CLOUD BACK OFFICE MODERNIZE LEGACY
RE-PLATFORM
AWS Marketplace

Most robust, fully featured technology platform

LAUNCHED
MARCH 2018
LAUNCHED
MARCH 2018
LAUNCHED
MARCH 2018
Services launched in Singapore in the last 12 months
Amazon EC2
M 5 I N S T A N C E S
C 5 I N S T A N C E S
G 3 I N S T A N C E S
P 2 I N S T A N C E S
R 4 I N S T A N C E S
E C 2 E l a s t i c G P U s n o w
s u p p o r t O p e n G L 4 . 2
Amazon Aurora with
PostgreSQL
Amazon Aurora with
MySQL
AWS Snowball
AWS Server Migration
Service
AWS Elemental
MediaTailor
Amazon AppStream
2.0
AWS Step Functions
AWS Deep
Learning AMIs
AWS OpsWorks
Amazon Redshift
Spectrum
Amazon ElastiCache
for Redis
AWS CloudHSM
Amazon Glacier
Amazon Cognito
AWS Batch
Amazon Quicksight
AWS CodeStar
AWS CodeCommit
AWS CodeBuildAmazon Athena Amazon Lightsail
Amazon DynamoDB
Accelerator (DAX)
S D K s f o r P y t h o n a n d . N E T
S u p p o r t f o r T 2 I n s t a n c e s
Amazon RDS for SQL Server
H i g h A v a i l a b i l i t y s u p p o r t AWS Direct Connect
L i n k A g g r e g a t i o n
G r o u p e n a b l e d
A m a z o n R D S
M 4 i n s t a n c e s i n A W S G o v C l o u d f o r
M y S Q L , M a r i a D B , P o s t g r e S Q L , a n d
O r a c l e e n g i n e s

COMPUTE AND STORAGE FOR HYBRID / EDGE WORKLOADS
A W S
S n o w b a l l E d g e
AVAILABLE
TODAY!
• Petabyte-scale data transport device
• On-board storage and compute capabilities
• Capture and pre-process data in remote locations
• Securely transfer large datasets to the AWS Cloud

AVAILABLE
TODAY!
AW S S t orage
Gatew ay
TAPE GATEWAY NOW AVAILABLE IN SINGAPORE
• Hybrid virtual tape library: Amazon Glacier and
Amazon S3 for storage with local caching
• Supports leading backup applications
• Data encrypted and compressed in-transit and at-rest
• Virtually unlimited capacity archive

2 1 S T C E N T U R Y A R C H I T E C T U R E S
re:Imagined

AWS
Well-Architected
Framework
O P E R A T I O N A L
E X C E L L E N C E S E C U R I T Y R E L I A B I L I T Y
P E R F O R M A N C E
E F F I C I E N C Y
C O S T
O P T I M I Z A T I O N

AWS
Well-Architected
Framework
Principles
Stop guessing capacity needs
Test systems at production scale
Automate to ease architectural experimentation
Allow for evolutionary architectures
Drive your architecture using data
Improve through Game Days

AWS Well-Architected Framework at work in ASEAN

Principles of Security
WELL-
ARCHITECTED
SECURITY
Identity Detective
controls
Infrastructure
protection
Data
protection
Incident
response

Dance like no one is watching.
Encrypt like everyone is.

Load
Balancer
ENCRYPTED IN
TRANSIT…
Amazon Redshift
Amazon Glacier
RDS
S3
EBS
…AND AT REST
Fully managed
keys in KMS
Y o u r K M I
Imported keys
Fully
auditable
E B S
A W S
C l o u d T r a i l
Restricted
access
Ubiquitous Encryption
WELL-
ARCHITECTED
SECURITY
E F S

S e c u r i t y i s
e v e r y o n e ’ s j o b

BUILD SMARTER APPLICATIONS WITH
Machine Learning

Machine Learning has long
been the domain of experts

Machine Learning at Amazon: a long heritage
Personalized
recommendations
Inventing entirely
new customer
experiences
Fulfillment
automation
/ inventory
management
Drones Voice driven
interactions

RETAIL
Demand Forecasting
Vendor Lead Time Prediction
Pricing
Packaging
Substitute Prediction
CUSTOMERS
Recommendation
Product Search
Product Ads
Shopping Advice
Customer Problem Detection
SELLER
Fraud Detection
Predictive Help
Seller Search & Crawling
CATALOGUE
Browse-Node
Classification
Meta-data Validation
Review Analysis
Product Matching
TEXT
In-Book Search
Named-entity Extraction
Summarization/Xray
Plagiarism Detection
IMAGES
Visual Search
Product Image
Enhancement
Brand Tracking
THOUSANDS OF EMPLOYEES ACROSS THE COMPANY FOCUSED ON AI
Machine Learning at Amazon.com

COUNTERFEIT GOODS DETECTION
CUSTOMER ADOPTION MODELS
ITEM CLASSIFICATION
SALES LEAD RANKING
SEARCH INTENT
DEMAND ESTIMATION
CUSTOMER SUPPORT
DISPLAY ADS
The spark for hundreds of new smart
applications within Amazon.com

Customers running Machine Learning on AWS today

ASEAN Customers running ML on AWS today

FRAMEWORKS AND INTERFACES
AI for data scientists
KERAS
Frameworks Interfaces
APPLICATION SERVICES
AI for everyone P O L L Y R E K O G N I T I O N C O M P R E H E N DL E X R E K O G N I T I O N
V I D E O
T R A N S C R I B E T R A N S L A T E
PLATFORM SERVICES
AI for engineers
AMAZON
SAGEMAKER

AMAZON SAGEMAKER
Build, train, and deploy with Amazon SageMaker

AMAZON SAGEMAKER
Fully managed
hosting with
auto-scaling
One-click
deployment
Pre-built
notebooks for
common
problems
Built-in, high
performance
algorithms
One-click
training
Hyperparameter
optimization
BUILD TRAIN DEPLOY
Build, train, and deploy with Amazon SageMaker

Amazon SageMaker
Collect and
prepare
training data
Choose and
optimize
your ML algorithm
Set up and
manage
environments for
training
Train and
tune model
(trial and error)
Deploy model
in production
Scale and manage
the production
environment

737 SIMULATOR
SageMaker in Action

737 Flight Simulation
PIPELINE WITHOUT AMAZON SAGEMAKER

737 Flight Simulation
PIPELINE WITH AMAZON SAGEMAKER

W h e r e d o
y o u s t a r t ?

G e t s t a r t e d w i t h o u r a p p l i c a t i o n s e r v i c e s
POLLY REKOGNITION COMPREHENDLEX REKOGNITION
VIDEO
TRANSCRIBE TRANSLATE

BUILDING INSIGHTS WITH
Data / Analytics

6 0 , 0 0 0 + u n i q u e d a t a b a s e s
m i g r a t e d u s i n g D M S
Migrate
between
On-prem
and AWS
Migrate
between
Databases
Automated
schema
conversion
Data
replication
for zero
downtime
migration
A W S D A T A B A S E M I G R A T I O N S E R V I C E

A m a z o n A u r o r a
F a s t e s t - g r o w i n g
A W S s e r v i c e e v e r
MySQL and PostgreSQL compatible
Several times faster than standard
MySQL and PostgreSQL
Highly available and durable
1/10th the cost of commercial
grade database

Amazon Aurora: Fastest-growing AWS service ever
TENS OF THOUSANDS OF CUSTOMERS

DATABASE
REQUESTS
TIME
Unpredictable workloads are challenging

ON-DEMAND, AUTO-SCALING DATABASE FOR APPLICATIONS WITH
UNPREDICTABLE OR CYCLICAL WORKLOADS
A u r o r a S e r v e r l e s s
N o n e e d t o
p r o v i s i o n i n s t a n c e s
A u t o m a t i c a l l y
s c a l e s c a p a c i t y
u p a n d d o w n
S t a r t s u p o n
d e m a n d a n d
s h u t s d o w n w h e n
n o t i n u s e
P a y p e r s e c o n d
a n d o n l y f o r t h e
d a t a b a s e
c a p a c i t y y o u u s e

of what we consider
analytics is not analytics
80%
VALUE
WORK 20%
80%

INDEXING
DATA
ACQUISITION
DISCOVERY
SECURITY
STORAGE
GOVERNANCE
ACCESS
of what we consider
analytics is not analytics
80%

The modern data architecture is agile
USERS SOURCES MODELS APPLICATIONS QUERIES PROCESSING

10
9
8
7
6 5
4
3
2
1
1. Automated And Reliable Data Ingestion
2. Preservation Of Original Source Data
3. Lifecycle Management And Cold Storage
4. Metadata Capture
5. Managing Governance, Security, Privacy
6. Self-Service Discovery, Search, Access
7. Managing Data Quality
8. Preparing For Analytics
9. Orchestration And Job Scheduling
10. Capturing Data Change
T h e m o d e r n d a t a a r c h i t e c t u r e
i s c o m p l e x

T h e r e a l i t y i s m o s t
o f y o u r d a t a i s i n
s i l o s

W h a t d o e s t h e m o d e r n d a t a
a r c h i t e c t u r e l o o k l i k e o n A W S ?

A u t o m a t e d I n g e s t i o n
S3
Upload
AWS Storage
Gateway
Kinesis Snowball
Snowball Edge
Snowmobile
DynamoDB
Streams

P r e s e r v a t i o n o f
o r i g i n a l s o u r c e d a t a
S3 EFS EBS DynamoDB
RDS Aurora Redshift

New API to select and retrieve data within objects
Accelerate any application that processes a subset of
object data in S3
Improve data access performance by up to 400%
POWERFUL NEW S3
CAPABILITY TO PULL OUT
ONLY THE OBJECT DATA YOU
NEED USING STANDARD SQL
EXPRESSIONS
S 3 S e l e c t
b e t a
RUN QUERIES DIRECTLY ON
DATA STORED IN GLACIER
G l a c i e r
S e l e c t
Run queries on data stored at rest in Amazon Glacier
Any application can query Glacier data
Retrieve only what you need
Makes Glacier part of your data lake

L i f e c y c l e m a n a g e m e n t
a n d c o l d s t o r a g e
S3 Storage
Management
Glacier

M a n a g e g o v e r n a n c e ,
s e c u r i t y , p r i v a c y
Config CloudTrail
KMS Glacier Vault Lock

AWS Glue helps you get
the most out of your data
AWS
GLUE
Amazon
Re dshift
+ R e d s h i f t
S p e c t r u m
Amazon
Quic kSight
Amazon E MR
H a d o o p , S p a r k ,
P r e s t o , P i g ,
H i v e … 1 9 t o t a l
Amazon
At he na
Amazon
Kine sis
Amazon
Elast ic se ar c h
Se r vic e
S3 EFS EBS Glacier
Storage
Non-relational Databases
AURORA COMMERCIAL COMMUN ITY
Relational Databases
Amazon
DynamoDB
Amazon
Elast iC ac he
Analytics
Amazon
C loudSe ar c h
AWS Dat a
Pip e l ine
Available
TODAY!

AWS
GLUE
Amazon
Re dshift
+ R e d s h i f t
S p e c t r u m
Amazon
Quic kSight
Amazon E MR
H i v e … 1 9 t o t a l
Amazon
At he na
Amazon
Kine sis
Amazon
Elast ic se ar c h
Se r vic e
S3 EFS EBS Glacier
Storage
Amazon
DynamoDB
Amazon
Elast iC ac he
Analytics
Amazon
C loudSe ar c h
AWS Dat a
Pip e l ine

M a c h i n e
L e a r n i n g
AWS
GLUE
Amazon
Re dshift
+ R e d s h i f t
S p e c t r u m
Amazon
Quic kSight
Amazon E MR
H i v e … 1 9 t o t a l
Amazon
At he na
Amazon
Kine sis
Amazon
Elast ic se ar c h
Se r vic e
S3 EFS EBS Glacier
Storage
Amazon
DynamoDB
Amazon
Elast iC ac he
Analytics
Amazon
C loudSe ar c h
AWS Dat a
Pip e l ine

10
9
8
7
6 5
4
3
2
1 1. Automated And Reliable Data Ingestion
2. Preservation Of Original Source Data
3. Lifecycle Management And Cold Storage
4. Metadata Capture
5. Managing Governance, Security, Privacy
6. Self-Service Discovery, Search, Access
7. Managing Data Quality
8. Preparing For Analytics
9. Orchestration And Job Scheduling
10. Capturing Data Change
A W S i s t h e o n l y p l a c e y o u
c a n h a v e a c o m p r e h e n s i v e
m o d e r n d a t a a r c h i t e c t u r e

A W S i s t h e o n l y
p l a c e y o u c a n
h a v e a
c o m p r e h e n s i v e
m o d e r n d a t a
a r c h i t e c t u r e
Every possible analytics engine
Strong support for exploration
and discovery
Deep integration of security and
governance
Removal of the heavy lifting of
data analytics

Shifting analytics
80%
analytics
20%
prep

M o s t a n a l y t i c s h a p p e n o n A W S

ASEAN customers using AWS for Analytics

Axel Winter
Group CTO, Central Group

93
Cloud Native Enterprise
April 2018 – Axel Winter (CTO)
AWS Summit Keynote
Corporates can execute at startup speed!

94
Central Group is a global Retail and aspiring Technology
organization, driving a rapid transformation program
Central
Group
• Retail, Hotels, Restaurants, Real Estate, generating ~220’000 jobs
• Thailand, Vietnam, Malaysia, Germany, Italy, Austria, Denmark, others
• A family held corporation, Target 2018: $11.9B
• Partnering with JD.COM for Marketplace, E-Logistics and E-Finance
• Delivery centers in Bangkok, Budapest and Singapore
• Digital Education for Group Executives
• Rapid rollout of Unified Commerce Technologies
• Continuing to attract top talent building a core technology team
Acceleration
Partners
• Added subject matter expertise to accelerate journey and skill building
API Big Data OMNI Channel Client DNA Cloud

95
Fast time to market is achievable, when everyone shares the
vision, operating model and architecture for target state
Starting with …
▪ Lack of digital capabilities
▪ Diverse and immature platforms
▪ Slow renewal progress
Driving at speed…
▪ Digitize Strategy and Path
▪ Operating Model for Technology
and Business
▪ Real time decision making
▪ Attracting and retaining talent
Adoption of Data is reality, using
Rest API is reality, and using OMNI
Channel Commerce is driving
revenue.
Strategy
Operating
Model
Enterprise
Architecture
Organization
Architecture
OMNI Channel &
Customer Care
API
Big Data
Organization
Accelerators
Leverage &
Mature
Next Wave
Feb/Mar Apr - May Jun - Dec Jan - Today
2017 2018
Cloud Infrastructure

96
Our Cloud Native Architecture has delivered a complex yet
flexible holistic platform, enabling all delivery of all requirements
OMNI Channel Platform
API Gateway
ESB | DataPump
Authentication | Authorization
Traditional Backend
OMNI Point of Sale
Data Lake
Digital Gateways
Customer Care
UI Framework
• Integrated Framework
• Cloud Native
• CI/CD
• ERP Abstraction
• Data driven
• Extensible
• Rapid ability to adopt new revenue generating
features and ability to experiment
• Create new revenue generating capabilities for the business
• AWS reduces efforts by mix of open and pre-built services

97
Create a “Data Native Enterprise”, with a goal speed of execution
and adoption of innovation vs traditional Data Lake setups
Technologies used:
• AWS EMR, IAM, Kinesis, RDS, S3, Glacier, Elastic/SOLR
• Ranger, Atlas*, Lily, NIFI, Schema registry
• CG’s Data Lake is built completely on
AWS, extending ‘EMR’ – enabling
rapid build up of base data lake
platform with better capabilities
• Data Native strategy enabled:
Collection, Quality, Access,
Intelligence, and Evolution
• Design and spin up infinite cloud
resources reacting to everchanging
demands at lightening speed
• Enable agile analytics and data
science services on and offline across
the group
Needs:
• Multi-tenancy – many entities
• Governance built-in from day 1
• Rapid innovation adoption
• Multiple diverse user cases
• Real time capability
• Cost Management

98
Create a modern Application Integration Platform, with a Micro
Service and Abstraction Layer for traditional platforms
• Strategic Application Integration
Architecture, as a key enabler for
Central Group in creating a OMNI
Channel Retail Platform
• Selected key Open Source and AWS
Components providing a
MicroServices Layer, Abstraction of
legacy platforms, and API Portal
• CI/CD: Continues Development and
Continues Integration applied
Needs:
• Legacy APIs, Databases, and Files
• Provide 3rd party APIs
• Headless Architecture
• High Productivity
• ”Super” Scalability required
Technologies used:
• Apache Camel, AWS API GW, IAM, Lambda Transformer, Kinesis
• Cloud Watch, New Relic, Elastic Search + Kibana, Swagger

99
To automate infrastructure deployments and management,
codifying is key value enable to especially a cloud setup
REPRODUCIBLE INFRASTRUCTURE
Terraform enables DevOps experts to reuse the
same configurations in multiple environments to
reduce human mistakes and save time.
Terraform enables to safely and predictably create, change, and improve production infrastructure.
It is an open-source tool that codifies APIs into declarative configuration files that can be shared amongst team
members, treated as code, edited, reviewed, and versioned.

Compute - From server to serverless
ContainersVirtual
Machines
Serverless

Compute - From server to serverless
Virtual
Machines
EMULATES PHYSICAL
INFRASTRUCTURE
GREAT FOR RUNNING
EXISTING SOFTWARE
LARGE EXISTING
FOOTPRINT
VIRTUAL MACHINES

Broadest spectrum of compute instances
T 2 H 1 R 4 X 1 I 3 C 5 G 3 P 3X 1 eM 5 I 3 mD 2 F 1
Amazon
Lightsail
Burstable Big Data
Optimized
Memory
Optimized
In-
memory
High
I/O
Compute
Intensive
Graphics
Intensive
General
Purpose
GPU
Memory
Intensive
General
Purpose
V i r t u a l
P r i v a t e
S e r v e r s
Bare Metal
High I/O
Dense
Storage
FPGA
EC2 Elastic GPUs
Graphics acceleration for EC2
instances
EC2 Spot Instances
• Hibernation
• No Bid Pricing

From server to serverless
Containers
EVEN MORE COMPUTE
EFFICIENCIES
FAST STARTUP AND
TEAR DOWN
DEVELOPER AGILITY
CONTAINERS

E C S
Highly scalable, high
performance container
management system
A M a n a g e d P l a t f o r m
Cluster Management Container Orchestration Deep AWS integration

C u s t o m e r s a r e d o i n g a m a z i n g t h i n g s
i n p r o d u c t i o n w i t h c o n t a i n e r s

EKS
Platform for enterprises to run
production-grade Kubernetes-
grade installations
Managed and
upstream experience
Seamless, native integration
with AWS services
Gives back to
open-source community

B u i l d e r s w a n t t o b u i l d ,
n o t m a n a g e c l u s t e r s

No infrastructure
Manage everything at the container level
Launch easily, scale quickly
Resource-based pricing

Containers on AWS
Amazon EKS
AWS Fargate for ECS
AWS Fargate for EKS
Amazon ECS
COMING SOONPREVIEW

From server to serverless
Serverless
EXTREME DEVELOPER
PRODUCTIVITY
COMPLETE
ELIMINATION OF
INFRA CONCERNS
FOCUS MORE ON
BUSINESS LOGIC
SERVERLESS

Builders only want to
build business logic.

NETWORK
ALB
API Gateway
COMPUTE
Lambda
Containers/
Fargate
COMMUNICATIONS
SQSSNS
Kinesis
DATA
DynamoDB
S3
CloudFront
Aurora
Serverless
IDENTITY
Cognito
Cognito
Userpools
MONITORING &
AUDITING
CloudWatch
Cloud Trail
X-Ray
COORDINATION
ECS
Stepfunctions
DEVELOPMENT &
DEPLOYMENT
CodeStar
SAM
Cloud9
SYSTEMS
DEVELOPMENT
Mobile
IoT
S e r v e r l e s s a p p l i c a t i o n
a r c h i t e c t u r e s o n A W S

No server management
Flexible scaling
High availability
No idle capacity
B e n e f i t s o f
S e r v e r l e s s
A p p l i c a t i o n s

ASEAN customers doing serverless

AWS Summit Singapore – 4 April 2018

Who We Are and What We Do
Confidential: not meant for distribution beyond intended audience
A ‘next gen’ credit-based payment solution, delivering a better way to purchase
We are a digital credit card for use
at the checkout, online and offline
Our business is 2 fundamentally complex
engines bolted together -- real-time credit
assessment and a real-time transaction
engine
1 minute
to apply
2 clicks
to buy
30 days to 12 months
to pay back

Source: United Nations World Population Prospects, Haver
Analytics
Drivers for Retail Credit Growth
Smartphone Growth in the Country % of Population Under Age 30
The large population of millennials + high smartphone coverage + high demand for credit creates the perfect
storm for digital data based credit assessment and lending
0
20
40
60
80
100
120
2011 2012 2013 2014 2015 2016 2017
InMillions
Source: eMarketer, 2016
Total leverage – Debt to GDP (%)

Challenges
Process 20-30 MB of raw data per user
in real-time to create hi-quality credit
scores
Scalable services to build our risk models
Identity verification
model
Address verification
model
Credit scoring models
(A, B and C scores)
Income prediction
model
Self reported info
Demographic,
Employment…
Phone data
Device info, Apps, Data
usage, Call logs,
Contacts…
Environmental data
Application filling
behavior, Time of day…
Third-Party Data
Social media, Ecommerce
history, Bank accounts…
An extensible, hi-frequency transaction engine
A transaction engine that can easily
scale to handle ~100k txn requests per
day with ~50ms response time

Architecture – Business Layers
Machine learning Analytics and reporting
Big data
b_score TED MCO
Loan
& repayment
Lender
OCR CSS Ostrich APE Miners
Users
Merchants
Lenders
Ops team Internal APIs External APIS
Integration Layers
Payment channels
Banks and Ecom
Lenders

Technical Architecture
ActiveMQMySQL S3
Elasticache
Redis
VPC
Auto scaling
ActiveMQMySQL S3
Elasticache
Redis
Latency based
routing
Route 53
Zone 1a Zone 1b
Router
Lambda
API gateway
Lambda Lambda
Lambda Lambda
APE
rules
VPN
connection
VPC
Auto scaling
Zone 1a Zone 1b
Router
VPC
Auto scaling
Zone 1a Zone 1b
VPC
Auto scaling
Zone 1a Zone 1b

Results of Using AWS
Apr-16 May-16 Jun-16 Jul-16 Aug-16 Sep-16 Oct-16 Nov-16 Dec-16 Jan-17 Feb-17 Mar-17 Apr-17 May-17 Jun-17 Jul-17 Aug-17 Sep-17 Oct-17 Nov-17 Dec-17 Jan-18 Feb-18 Mar-18
Transactions/ month
Apr-16 May-16 Jun-16 Jul-16 Aug-16 Sep-16 Oct-16 Nov-16 Dec-16 Jan-17 Feb-17 Mar-17 Apr-17 May-17 Jun-17 Jul-17 Aug-17 Sep-17 Oct-17 Nov-17 Dec-17 Jan-18 Feb-18 Mar-18
Applications/ month
30%
CAGR
35%
CAGR
1 sRaw data collected per day
20 TB Total size of raw data DB
30 GB Time to process a credit score
Max transactions handled/ second100

What’s new in 2018?
Continuous migration from EC2 to Lambda
Instant scoring using a combination of Kinesis,
S3, ElastiCache and Lambda
Amazon ML deployment

G L O B A L S P O N S O R S
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BREAKOUT SESSIONS
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Exhibition
Showcase
Breakout Sessions are Colour Coded
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BREAKOUT SESSIONS
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MODERN BUILDERS
MOVE IT! MIGRATING TO AWS
FAST START WITH AWS
CUSTOMER INSIGHTS &
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Channel #1
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AWS Summit Singapore Opening Keynote

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