Drive innovation in Financial Services with Amazon EC2 - CMP204 - New York AWS Summit

© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Drive Innovation in Financial Services
with Amazon EC2
Rob Chen
Head of Infrastructure Solutions, EC2
AWS
C M P 2 0 4

Agenda
Key Trends in Financial Services
High Performance Computing
AI & ML
Hybrid & AWS Outposts
Summary

Key Trends in Financial Services Industry
New forms of risk are
constantly emerging.
Consumers have higher
expectations and more
choices.
Data is revealing go-to-
market and cost-saving
opportunities.
Security Data Competition

Forces driving cloud adoption in the industry
Constant pressure on margins and
resource scarcity
Rising data volumes and need for cost-effective
analytics to enable AI/ML
Regulatory oversight and ever-expanding
reporting obligations
Recognition of superior security and data
privacy in the cloud
Ongoing competition
from new entrants
Legacy processes and infrastructure
hindering innovation

Financial modeling has grown more onerous
Compute-intensive calculations
More granular risk factors
Wider range of scenarios
More historical data
Broad regulatory requirements
Comprehensive Capital Analysis and
Review (Banking/Dodd Frank)
Solvency Capital Requirements
(Insurance/Basel II)
Fundamental Review of the
Trading Book (Insurance/Basel III)
Diverse risk analysis models
Market risk
Credit risk
Liquidity risk

And on-premises grids are often inadequate
Data center capacity is limited, resulting
in simulation backlogs or inadequate
risk calculations
Financial instruments require flexible
compute resources for development
and testing.
Limited capacity, which results in long
runtimes for simulations
Regulatory and market fluctuations
require flexible compute capabilities
Large upfront investments and
maintenance required to run
on-premises grids
Standardized hardware offers limited
grid and compute types

Cloud is an attractive option for Grid Computing
Why are Financial Services institutions increasingly shifting
grid computing to the cloud?
Virtually unlimited
compute
and storage
resources
Various
instance types
for specific types of
workloads
Cost
optimization
with a variety
of pricing structures
Enhanced
security
and compliance
Expanded
big data
capabilities
for analysis
and business
intelligence
Automation
capabilities
for scaling and
provisioning
resources
Management
consoles
to provide
enterprise-wide
control and
visibility

Broadest and deepest platform choice
metal
metal
metal
metal
metal
metal
metal
metal
metal
metal
metal
metal
metal
Categories Capabilities Options
Choice of processor
(AWS, Intel, AMD)
Fast processors
(up to 4.0 GHz)
High memory footprint
(up to 12 TiB)
Instance storage
(HDD and NVMe)
Networking
(up to 100 Gbps)
Accelerated computing
(GPUs and FPGA)
Bare Metal
Size
(Nano to 32xlarge)
Amazon EBS
Elastic Inference
Elastic Graphics
+ + =
Compute intensive
Memory intensive
Burstable
Storage (High I/O)
Dense storage
GPU compute
Graphics intensive
General purpose
200+
instance types
NEW
NEW
NEW

Enhanced Network for HPC and Machine Learning
Elastic Fabric Adapter for HPC
Best for scaling large HPC and ML workloads
C5n
Most elastic and scalable
HPC network
P3dn
Fastest machine learning
training in the cloud
Custom Intel® Xeon®
Scalable processor
NVIDIA V100
Tensor Core GPUs
Up to 100 Gbps network bandwidth

Comprehensive portfolio of high-performance
storage options
Block storage File storage Object storage
Amazon EBS Amazon EFS Amazon S3
Elastic, high performance
block storage
at any scale
Petabyte-scale, elastic file storage
sharable across applications, instances
and servers
Low cost, highly scalable cloud
storage with
99.999999999% durability

Bankinter: Intraday Credit Risk Simulation
Javier Roldán
Director of Technological Innovation
”
“ The challenge
The bank needs to run 5 million credit risk simulations to evaluate the
financial health of Bankinter’s clients.
The solution
Implementing simulations in parallel on a grid of Amazon EC2 instances to
obtain the result in a very short time period.
The result
The bank has brought down the average time for running simulations from
23 hours to 20 minutes and estimates it would spend 100 times more in
hardware alone if it chose to exit the cloud.
With AWS, we now have the power to decide
how fast we want to obtain simulation results.
More important, we have the ability to run
simulations that were not possible before due
to the large amount of infrastructure required.
98% decrease in calculation time
and savings of 100x vs. on-premises
Key benefits

TIBCO’s GridServer solution runs on AWS
Nelson Petracek
Global Chief Technology Officer, TIBCO
“ By running TIBCO GridServer on AWS, financial institutions can take advantage
of on-demand cloud computing with an almost infinite capacity for
computation-intensive applications.
TIBCO’s solution can analyze large volumes of data in connection with risk,
trading, financial models, and more. For example, the GridServer software can
turn risk reports for stock traders from a six-to-eight-hour process to a 15-
minute intraday cycle, enabling traders to make more educated decisions.
GridServer will also supplement investment banks’ capacity for daily
Fundamental Review of the Trading Book (FRTB) calculations, which will
significantly increase the need for large-scale grid computing environments
when the requirement goes into effect in 2019.
With the TIBCO Connected Intelligence platform
of solutions, companies can build a system that
automatically allocates cloud resources on
demand. GridServer embeds an AWS
architecture that is highly scalable, both in
terms of speed and throughput, and is ideal for
industries that require high-volume
calculations, such as financial services,
healthcare, and oil and gas.
Accelerated go-to-market timelines
for products and services

Why Grid Computing or HPC on AWS?
Virtually unlimited infrastructure enabling
scaling and agility not attainable on-premises
Flexible configuration options quickly iterate resource
selection and ensure cost optimization
Instant access to latest technologies with no lengthy
procurement cycles or big capital investments
Better ROI
Faster time
to results

AI and ML are the next wave in digital innovation
Compliance, Surveillance,
and Fraud Detection
Document
Processing
Pricing and Product
Recommendation Trading
Financial institutions are increasingly investing in AI/ML thanks, in part, to the availability of cost-
effective, easy-to-use, and scalable cloud-based AI/ML services.
Customer Experience
• Credit card/account
fraud detection
• Sales practices/
transaction surveillance
• AML/Sanctions
• Investigations optimization
• Regulatory mapping
• Common financial
instrument taxonomy
• Contract ingestion
and analytics
• Financial information
extraction
• Corporate actions
• Loan/Insurance underwriting
• Sales/recommendations of
financial products
• Credit assessments
• Portfolio management/
robo-advising
• Algorithmic trading
• Sentiment/news analysis
• Image analysis
• Grid computing scheduling
• Enhanced customer
service through chatbots
• Call center optimization
• Personal financial
management
Core processing Client facing

Run AI/ML on Amazon EC2 P3 Instances
One of the fastest, most powerful GPU instances in the cloud
Performance
• Up to 1 PetaFLOPs of computations performance per
instances
• Train models in hours instead of days
• Scaling out with multiple P3 reduces training time further
and enhances team productivity
Cost
• Offer flexible pricing options including On-Demand,
Reserved, and Spot Instances
• Up to 70% discount from On-Demand Instances
• Can save 50% for training ML models
Versatile
• Support for all ML frameworks including TensorFlow,
MXNet, PyTorch, Caffe2, and CNTK
• Can be used to train different model types (CNN, RNN &
GANs)
Ease of Use
• Integrated with Amazon SageMaker – End to end workflow
management service
• Alternatively, quickly get Started with AWS Deep Learning
AMIs
Broad Availability
• Available at-scale across eight Regions worldwide
• Significant adoption by the ML Community
• Most popular platform for running TensorFlow

P3 Instances Details
Instance Size GPUs
GPU Peer to
Peer
vCPUs
Memory
(GB)
Network
Bandwidth
EBS
Bandwidth
On-Demand
Price/hr*
1-yr RI
Effective
Hourly*
3-yr RI
Effective
Hourly*
P3.2xlarge 1 No 8 61
Up to
10 Gbps
1.7 Gbps $3.06
$1.99
(35% Disc.)
$1.05
(60% Disc.)
P3.8xlarge 4 NVLink 32 244 10 Gbps 7 Gbps $12.24
$7.96
(35% Disc.)
$4.19
(60% Disc.)
P3.16xlarge 8 NVLink 64 488 25 Gbps 14 Gbps $24.48
$15.91
(35% Disc.)
$8.39
(60% Disc.)
Note: P3 instances are also supported via Spot Instances with up to a 70% discount
Framework Support
P3 instances and their V100 GPUs supported across all major frameworks (such as TensorFlow, MXNet,
PyTorch, Caffe2, and CNTK)

New larger P3 size – P3dn.24xlarge
Optimized fordistributed ML training
• One of the most powerful GPU instances available in the cloud
• 100 Gbps of networking throughput
• 96 vCPU using AWS custom Intel Skylake CPUs and 768 GB of system memory
• Based on NVIDIA’s latest GPU Tesla V100 with 32 GB of memory
Instance size GPUs GPU memory
GPU
peer to peer
vCPUs CPU type
Memory
(GB)
Network
Bandwidth
Amazon EBS
Bandwidth
Local instance
storage
P3.2xlarge 1 x V100 16 GB/GPU No 8 Broadwell 61 Up to 10 Gbps 1.7 Gbps NA
P3.8xlarge 4 x V100 16 GB/GPU NVLink 32 Broadwell 244 10 Gbps 7 Gbps NA
P3.16xlarge 8 x V100 16 GB/GPU NVLink 64 Broadwell 488 25 Gbps 14 Gbps NA
P3dn.24xlarge 8 x V100 32 GB/GPU NVLink 96 Skylake 768 100 Gbps 14 Gbps 2 TB NVMe
Latest NVIDA V100 GPU with 32-GB
memory for large models and higher
batch sizes
96 Skylake vCPUs with support for
AVX-512 instructions for pre-
processing of training data
100 Gbps of networking throughput
for large-scale distributed training &
fast data access

Scaling Performance using Distributed Training
-
5,000
10,000
15,000
20,000
25,000
30,000
35,000
40,000
45,000
50,000
1 2 4 8 16 32 64
Images/Second
Number of GPUs
Training using P3 instances
(ResNet-50, ImageNet Images/Second)• Using single P3 instances, with Volta GPUs,
customers can cut down training times of
their machine learning models from days to
a few hours
• Using distributed training via multiple P3
instances, high-performance networking and
storage solutions, customers can further cut
down their time-to-train from hours to
minutes.
• Example – We have been able to train
ResNet-50 to Top1 validation accuracy of
76% in 47 mins using 8 P3.16xlarge
instances.
https://aws.amazon.com/blogs/machine-learning/scalable-multi-node-deep-learning-training-using-gpus-in-the-aws-cloud/

Enhancing Fraud Protection With Machine Learning
“We’ve recognized over the past number of years the
importance of leveraging machine learning to
enhance the user experience, as well as to help us
make more informed decisions around engaging with
our customers"
Nitzan Mekel-Bobrov, Ph.D.
Managing Vice President of Machine Learning
Capital One

Fueling Fast Product Innovation
Amazon EC2 P3 instances enable Intuit’s data
scientists to train new machine learning models “in
hours instead of days, or even weeks”
Models that Intuit agents rely on—like one that helps
them troubleshoot customer problems—can be
trained in as little as 30 minutes

Some Apps Still Need to Run Close to On-Premises Assets
Traditional Enterprise
Applications
Low Latency Applications Local Data Processing
Applications

Customerslookingfor hybrid solutionsfor integrationand consistencyacross
environments
Run workloads on-premises
Run workloads on the cloud
Consistent Experience
Tight Integration across all environments- cloud, on-premises,
and edge
Seamless connectivity and unified management and control
Run workloads at Edge locations

AWS-designed, secure and performant
infrastructure powered by Nitro
Managed and monitored just the same as any EC
Region with a cloud control plane
Single point of service and support
Build your applications once and deploy anywhe
Introducing AWS Outposts

Customer Benefits
Build Once Deploy
Anywhere
Managed as-
a-service model
▪ Extend AWS on-premises or
to the connected edge
location
▪ Consistent developer
experience
▪ Same AWS SDKs, CLIs,
deployment tools and
services
▪ Automatically monitored,
updated and patched as part of
AWS Region
▪ Focus on innovation instead of
infrastructure procurement and
upgrades
Consistent AWS
Experience
▪ Avail the same
performance, security and
experience as in the EC2
Region
▪ Same pricing model
▪ Same operational model

AvailableinTwoOptions
VMware SDDC-as-a-service
VMware APIs and services to
leverage existing skills,
automation, and governance
policies
Same APIs, services, and features
as in the AWS Cloud
Amazon EC2 and Amazon EBS
with support for services
including Amazon RDS, Amazon
ECS, Amazon EKS, Amazon EMR,
Amazon SageMaker
Native AWS VMware Cloud on AWS

Get Started in Three Steps
Order
Order via the AWS Management
Console or API
Connect
Connect to power
and network
Launch
Use standard AWS APIs or AWS
Management Console to launch EC2
instances, Amazon RDS instances, SM
instances, and so on

Step 1: Order
• Choose your desired site
• Choose from a wide catalog of SKUs with a broad range of Nitro-based EC2 instances
and capacity
General purpose (M5)
Compute optimized (C5)
Memory optimized (R5)
Graphics Optimized (G4)
i3en and others coming soon
Local Instance Storage(C5d, M5d, R5d)
GP2 EBS Volumes

Step 2: Connect
View your order status and customize your schedule for delivery
AWS installation team delivers and installs the Outpost at your site
42U rack - 24’’W x 48’’D x 80’’H
Same hardware rack as in your EC2 Region optimized for power
and performance
Fully integrated dual top of rack (TOR) switch with 1/10/40/100
Gbps uplink ports
Single phase AC (200 V to 240V, 50Hz or 60Hz),
Three phase AC (415V to 480V, 50Hz or 60Hz),
-48V DC

Step 3: Launch

Launch Local AWS Resources
• Amazon EC2 and Amazon EBS
• Amazon VPC
• Amazon Relational Database Service (Amazon RDS)
• Amazon Elastic Container Service (Amazon ECS)
• Amazon Elastic Kubernetes Service (Amazon EKS)
• Amazon SageMaker
• Amazon FSx for Lustre and Windows
• Amazon Managed Streaming for Kafka
(Amazon MSK)
• Amazon ElastiCache

Use the same AWS APIs & tools in the AWS Region
EC2 Auto Scaling Groups
AWS CloudFormation
Amazon CloudWatch
AWS CloudTrail
AWS Elastic Beanstalk
AWS Cloud9
and so on …

Operations & Maintenance
Operated and managed as part of AWS Region
No more software lifecycle management, no more
downloads, upgrades, application downtime; no more
complex software compatibility matrices
Full visibility and control with detailed health metrics,
alerts, and governance tools
Fast and easy replacement for upgrade and maintenance
Single point of support for quick resolution

Bringing the SAME AWS Experience On-Premises
• Same Functionality– Fully featured AWS services
• Same Performance – Same EC2 instances and EBS volumes
• Same Infrastructure – Same secure Nitro system with AWS-designed hardware and
virtualization, no more ‘software lifecycle’ management
• Same Deployment Path – Same deployment pipeline as in the cloud
• Same Programming Interface – Standard AWS CLI and SDK
• Same Monitoring & Automation – Same metrics, reporting, operational tools

Cloud is enabling transformation and innovation in the industry
Customer
experience &
digital channels
Financial institutions are increasingly focused on leveraging the cloud to transform existing businesses and
bring innovative new solutions to market
Grid &
high-performance
computing
Data lakes &
agile analytics
Core systems
transformation
AI/Machine
Learning
Blockchain
& DLT
Widespread
adoption today
Gaining traction

FSIs are aggressively moving grids to the cloud
Cloud-enabled grids are providing burst capacity and the ability to quickly run large compute jobs and complex
simulations.
Compute-intensive calculations
• More granular risk factors
• Wider range of scenarios
• More historical data
• Real-time pricing and valuation capabilities
Broad regulatory requirements
• Comprehensive Capital Analysis and
Review (Banking/Dodd Frank)
• Solvency Capital Requirements
(Insurance/Basel II)
• Fundamental Review of the Trading Book
(Insurance/Basel III)
Diverse risk analysis models
• Market risk
• Credit risk
• Liquidity risk
Elastic cloud-based compute grids comprising multiple compute types
+ +
=

Transform your
risk practices
Compute capacity as catalyst
for modernizing risk functions
Actuarial productivity
Improve risk insight
Optimize reserving levels
Respond to regulatory changes
Store new models &
output files in the cloud
Support for running models more often;
storage for increasingly complex and larger
models and
more numerous output files
Consolidated single source of truth
Move HPC grid to cloud
CAPEX to OPEX
Grid modernization
Variable use to an on-demand,
elastic utility
Augment grid, or replace grid
Moving HPC grids to AWS provides burst capacity to actuaries and data scientists leading to
faster model runtimes, enabling more analysis, and improving risk insights
Cloud-enabled grids offer cost and business benefits

These are the most popular grid computing use cases for
the cloud
CCAR/FRTB & other
risk-based capital
regimes
Trading and Pricing
models Valuation Hedging strategies Back-testing

Accenture and AWS help FIs calculate risk
“ Accenture’s Risk Calculation for cloud solution (ARC) provides a
framework that enables financial institutions to adopt AWS for
compute-intensive risk calculation use cases while helping control
costs and retain full data security and confidentiality.
Some key use cases include:
• Fundamental Review of the Trading Book Internal Model Approach (FRTB
IMA) calculations—Value at Risk (VaR-ES) and Standardized Approach (SA)
• Increased stress-testing demands through both increased range of
scenarios run and the frequency of calculation
• Periodic calibration and validation of calculations—that is, selection of
market risk stress period, profit and loss (P&L) attribution
Accenture and AWS help
financial organizations worldwide
stay compliant through cloud agility, improved
enterprise architectures,
and transparent controls which support much
needed computation headroom (capacity and
capability) and agility.
Enhanced ability to meet regulatory
requirements while reducing costs

Talanx: Insurance Solvency II Model Simulation
Achim Heidebrecht
Head of group IT at Talanx
”
“ The challenge
Producing precise, high-quality, and time-sensitive Solvency Capital
Requirements and Minimum Capital Requirements using a complex Monte
Carlo simulation for the quarterly and annual company reports without
creating bottle-neck in internal IT.
The solution
With AWS, 4 distinct environments set up in 4 Virtual Private Clouds (VPCs)
using at peak 550 Amazon EC2 instances and 280-TB storage.
The result
75% reduction in calculation time and about 8 million
euros in annual savings.
Using AWS we are already seeing a
75% reduction in calculation time, and €8m in
annual savings, when running
our Solvency II simulations, while
still complying with our very strict
data policies.
75% reduction in calculation time
and €8 million in annual savings
Key benefits

MAPFRE: Insurance Solvency II solvency check
Guillermo Diez Perellón
Director of IT Architecture
for MAPFRE
”
“ The challenge
The company is required to perform a monthly solvency check
to test its risk under worst-case scenarios. Running these
calculations requires high performance computing (HPC)
machines that are used only a few times a month.
The solution
To gain compute capacity while maintaining data privacy,
the company established an Amazon VPC to move its data
to Provisioned IOPS Amazon EBS and share the data to the
Amazon EC2 cluster.
The result
By using Amazon EC2 the company achieved cost savings of €820,000 over
three years compared with its on-premises solution.
We asked AWS to increase the capacity
and number of sessions and help us with
the configuration. Now, the system is
working very well.
€820,000 in savings over 3 years
Key benefits

Aon Benfield: Actuarial risk modeling & hedging
Peter Phillips
Managing Director,
Aon Benfield Securities
“ The challenge
When Aon first launched its financial modeling tool, PathWise, it
used a broadband HPC processor in a colocated data center, but found it
needed a more scalable service than the colocation facility could provide.
The solution
To gain the scalability and cost savings it needed, Aon moved its infrastructure
to AWS and depreciated its colocated data center.
Aon built a front end on AWS for its processing solution, automatically running
GPU instances on Amazon EC2 using Amazon EBS in an Amazon VPC for
security.
The result
By moving its infrastructure to AWS, Aon became 500 times more cost efficient
for its clients, and reduce a 10-day process to 10 minutes.
Using AWS helps us reduce a 10-day
process to 10 minutes. That’s
transformative: it broadens our ability to
discover.
€820,000 in savings over 3 years
Key benefits
”

Use cases in FI when apps need to run close to on-premises assets
Workload Migration
Shift legacy workloads from on-premises data centers to a hybrid solution to standardize governance across organization
Trading Platforms
Develop low latency trading platforms for handling trades, market data, and services close to trading partners according to
regulatory requirements
Data Aggregation and Local Data Processing
Aggregate and deliver financial/market data and feeds for banking, wealth management, and financial clients in a secure
environment
Digital Currencies
Develop digital currencies which have significant security and low latency requirements for transacting data in the cloud

And the choices poor
$
Optimize for cost
Utilization is high but users may
wait a long time to access the system
Optimize for availability
Usage remains low which
drives up costs

Matching demand to capacity is challenging
Attempting to match variable computing demand to static on-premises compute grids is
extremely difficult, and adding capacity is time- and capital-intensive
Time
Capacity
Actual demand for computing
Server
acquisition
Server
acquisition
Server
acquisition
Project
delay
Project
delay
Project
delay
Demand exceeds
capacity
Unused
IT resources

What if you could add 100,000 cores of your choice to your compute grid as you needed
them, remove them when you are done, and only pay what you used?
The solution: On-demand elastic compute power

Connect to Local On-premises Networks
Connect to on-premises networks with a (new) Local Gateway
(LGW)
Attach one or more VPCs to LGW to propagate the VPC CIDRs
to the LGW
Create a Virtual Interface (VIF) on LGW to connect to the local
network
Exchange network information between LGW and local
gateway using dynamic (BGP) or static routing

Drive innovation in Financial Services with Amazon EC2 - CMP204 - New York AWS Summit

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Drive innovation in Financial Services with Amazon EC2 - CMP204 - New York AWS Summit