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© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Américo de Paula
Solutions Architecture Manager - LATAM
Mining intelligent insights:
AI/ML for Financial Services
Breakthrough
advances
Optimization and
automation
AI and ML enable innovation at scale…
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? June 2017
…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
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
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
But overall the industry has been 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
What is preventing the industry from moving ahead?
AI/ML expertise is
rare
Building and
scaling AI/ML
technology is hard
Deploying and operating
models in production is
time-consuming and
expensive
A lack of cost-effective,
easy-to-use, and
scalable AI/ML services
AWS offers a range of solutions to make AI/ML more accessible
PollyLex Rekognition
Deep Learning FrameworksMachine Learning Platforms Amazon AI/ML Services
Usability/simplicity:
leverages AWS AI/ML expertise
Greater control:
customer-specific models
Amazon ML
Spark & EMR
Kinesis
Batch
ECS
Customization of
offerings at scale
More personal and
efficient customer
interactions
Operational
efficiencies
Novel investment/
trading opportunities
Benefits for Financial Services Institutions
and others...
Our deep experience with AI/ML differentiates our services
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 customers20171995
Natural language
processing-supported
contact centers
ML-driven supply
chain and
capacity planning
Checkout-free
shopping
using deep learning
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
AWS Machine Learn ing Stack
Frameworks &
Infrastructure
AWS Deep Learning AMI
GPU(P3 Instances)
MobileCPU
(C5 Instances)
IoT
(Greengrass)
Vision:
Rekognition Image
Rekognition Video
Speech:
Polly
Transcribe
Language:
Lex Translate
Comprehend
Apache
MXNetPyTorch
Cognitive
ToolkitKeras
Caffe2
& CaffeTensorFlow Gluon
Application
Services
Platform
Services
Amazon Machine
Learning
Mechanical
Turk
Spark &
EMR
Amazon
SageMakerAWS
DeepLens
Fraud.net is running AI/ML on AWS to predict financial crime
“Amazon Machine Learning helps us reduce complexity and make sense of emerging fraud patterns.
We can see correlations we wouldn’t have been able to see otherwise and answer questions it would
have taken us way too long to answer ourselves.
”Fraud.net is the world’s leading
crowdsourced fraud prevention
platform, aggregating and
analyzing large amounts of
fraud data from thousands of
online merchants in real time.
The platform protects more
than 2 percent of all U.S. e-
commerce.
- Oliver Clark, CTO, Fraud.net• To address its scalability needs, Fraud.net chose AWS to host its customer
platform, relying on services including DynamoDB, Lambda, S3, and Redshift
• Recently, Fraud.net started using Amazon Machine Learning, which helps its
developers build models and enables the use of APIs to get predictions for
applications without having to deploy prediction generation code
• Fraud.net can now easily launch and train new machine-learning models to target
evolving forms of fraud
• Using AWS, Fraud.net can maintain its fast application response times of under
200 milliseconds and save its customers about $1 million a week through fraud
detection and prevention
BuildFax uses Amazon ML to help insurers avoid losses
“Amazon Machine Learning democratizes the process of building predictive models. It’s easy and fast
to use and has machine-learning best practices encapsulated in the product, which lets us deliver
results significantly faster than in the past.
”BuildFax aggregates dispersed
building permit data from across
the United States and provides it
to other businesses, especially
insurance companies, and
economic analysts. The
company also tracks trends like
housing remodels and new
commercial construction.
- Joe Emison, Founder & Chief Technology Officer, BuildFax
• BuildFax’s core customer base is insurance companies, which spend billions of
dollars annually on roof losses
• The company initially built predictive models based on ZIP codes and other general
data, but building the models was complex and the results did not provide enough
differentiators
• BuildFax now uses Amazon Machine Learning to provide roof-age and job-cost
estimations for insurers and builders, with property-specific values that don’t need
to rely on broad, ZIP code-level estimate
• Models that previously took six months or longer to create are now complete in four
weeks or fewer
@dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Adam Wenchel, VP of AI
@dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Remember the infrastructure Built on AWS ML takes time, and technical debt
Democratize AI, responsibly Maximize scarce experts’ productivity There is a lot more than the ML model
Machine Learning @ Capita l One
@dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
The road to production is long and arduous
Prepare for the journey
@dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Model selection and Hyper-
parameter Tuning
GPU Optimization Self-service
Production is a hard place Continuous Monitoring Rapid ML model refit/deploy
Capital One AI: The Road to Production
@dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Machine Learning Technology @ Capital One
Built on AWS
@dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Amazon AI AI Platforms AI Engines & InfraAI Services
Capital One AI: Democratizing ML in a Well-Managed Way
Experiment
ManagementLogging Versioning Reproducibility Monitoring
Optimize &
ScaleModel Optimization GPU saturation
Data/Compute
coordination
Capital O
ne
@dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Organizing for Success
You want a Data Science Team, then what ?
@dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
People
Machine Learning is inherently Interdisciplinary
Physical
ScientistsAnalysts
Architects/
Integrators
Computer
Scientists
@dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
People
Physical
ScientistsAnalysts
Architects/
Integrators
Computer
Scientists
Used to solving
problems by
applying ML to
sensors data
Machine Learning is inherently Interdisciplinary
@dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
People
Machine Learning is inherently Interdisciplinary
Physical
ScientistsAnalysts
Architects/
Integrators
Computer
Scientists
Used to solving
problems by
applying ML to
sensors data
Understand
Business
Problem
Framing
@dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
People
Machine Learning is inherently Interdisciplinary
Physical
ScientistsAnalysts
Architects/
Integrators
Computer
Scientists
Used to solving
problems by
applying ML to
sensors data
Understand
Business
Problem
Framing
Understand
Computer
Logic and ML
Science
@dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
People
Machine Learning is inherently Interdisciplinary
Physical
ScientistsAnalysts
Architects/
Integrators
Computer
Scientists
Used to solving
problems by
applying ML to
sensors data
Understand
Business
Problem
Framing
Understand
Computer
Logic and ML
Science
Can put
everything
together
@dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Capital One AI: How We Organize for Success
Create a positive work environment for ML
talent
Centralize new technologiesAttract and motivate the right talent
@dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Opportunity: Transform Financial Services
Increase efficiency, creating value for our
customers
New ways of empowering customers to
take control of their financial livesCreate amazing customer experiences
Nima Najafi
Scotiabank Senior Manager
Data Science & Model Innovation
Opt imiz ing Payments
Col lect ions wi th Conta iners
and Machine Learn ing
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Who is Scot iabank?
• Third-largest bank in Canada
• On Top 10 List Of World's Strongest Banks
• Providing services in North America, Latin America, the
Caribbean and Central America, and Asia-Pacific
• 88,000 employees
• $907B assets (as at July 31, 2016)
• Dedicated to helping its 23 million customers become better
off through a broad range of advice, products, and services,
including personal and commercial banking, wealth
management and private banking, corporate and investment
banking, and capital markets
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Business problem:
Credit card portfolio has been growing in the past
years, requiring more effective collections to handle
the growing volume.
Bank strategy:
Grow digital presence in order to better serve our
customers and play a leading role in transforming the
banking sector for the digital age.
What were we t ry ing to so lve?
Acquisition
Account management
Collections
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Why star t wi th payments co l lect ions?
Quarter All Credit Cards Other
2017 Q2 2.21 2.47 1.96
2017 Q1 2.18 2.42 1.97
2016 Q4 2.16 2.37 1.97
2016 Q3 2.08 2.29 1.91
2016 Q2 2.04 2.20 1.89
2016 Q1 1.99 2.16 1.83
US delinquency rates
In Canada, the average consumer
balance for credit cards grew by
2.07% from Q2 2016 to Q2 2017.
Canadian delinquency rates
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
What came before deep learn ing?
• Models were more basic (logistic regression/linear
regression)
• Process was manual and time consuming
• Complete data sources weren’t readily available
• Less accurate predictions
• Additional opportunities for financial savings
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Deep learning model
Written in Python
Takes ~300 credit bureau and internal variables as inputs
Hyper-parameters (learning rate, batch size, #layers, #npl, #epochs, activation
functions, ...) were selected via grid search
10-fold cross validation with stratification
Percentage of charge-offs captured in bottom 10%
Out-of-time
datasetDeepLearni.ng model
Incumbent (in-house)
modelLift
Nov-14 54.5% 45.3% 20.3%
Jan-15 55.0% 46.2% 19.1%
May-15 55.4% 46.8% 18.2%
Aug-15 50.1% 42.1% 19.0%
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Results!• Create/orchestrate/terminate the application from Amazon EC2
Container Registry (Amazon ECR) to Amazon EC2 Container Service
(Amazon ECS)
• Synchronize the data between Amazon Simple Storage Service
(Amazon S3) and GitHub LFS
• Secure the cluster through IAM roles (cluster only accepts traffic from
Scotiabank IPs)
• Application is fully containerized and versioned
• Changes to the cluster infrastructure are managed by pull requests only
• Rapid development and traceability through “everything through code”
paradigm
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Success storyamzn.to/FSV305-WSJ
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
A Li t t le About Al ly…
…A digitally disruptive, diversified financial services firm
with a full suite of financial products and services
including banking, auto finance, and mortgage offerings.
Beyond its services, Ally is known for its unique culture,
straight forward approach and customer-centric business
philosophy.
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Al ly Business L ines
• Full-spectrum provider
• New, used, leasing
• Floor plan and
commercial services
• Dealer online services
• Auto-focused insurance
business
• Direct banking platform
• Deposit products:
savings, checking, CDs,
IRAs
• Mobile banking
• Credit card
• Mortgage
• Financing for mid-market
companies in technology,
healthcare, retail, and
automotive
• Digital portfolio
management platform
• Ally Invest
Auto Finance Ally Bank Corporate FinanceWealth Management
& Online Brokerage
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
* Solved through fine-tuning utterances
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Technology @ Work
Amazon
Alexa
Amazon Developer Portal
Voice Interaction Model
Ally Interactive API
(AWS Lambda)
Transactions
Accounts
Welcome
Transfers
Rates
CurrenSee
Amazon
CloudWatch
AWS X-Ray
AWS IAM
AWS KMS
Shared
Services
AuthN
Token Secure
Gateway
Ally
Microservices
Enrollment APIs
Step Up Auth
AuthN APIs
Banking APIs
Notify Services
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
The Al ly Ski l l
"ALEXA, OPEN ALLY”
“GET MY BALANCE” “HOW MUCH DID I SPEND LAST MONDAY?”
“ALEXA, TRANSFER $10 FROM MY
ALLY BANK [PRODUCT NAME] ACCOUNT
TO MY [ELIGIBLE EXTERNAL ACCOUNT]”
TO VERIFY IT'S YOU, TELL ME YOUR
6-DIGIT PASSCODE
“WHAT'S TODAY’S RATE FOR A 12 MONTH HIGH YIELD CD”
“CONVERT $300 INTO CURRENSEE”“ALEXA, ASK ALLY FOR HELP.”
Américo de PaulaSolutions Architecture Manager
Worldwide | N. America | LATAM | UK/IR | EMEA | APAC | Japan | China