Machine learning (ML) services on AWS create opportunities to automate and accelerate labor- and time-intensive processes, analyze large datasets, discover business insights, and mitigate security and market risks. Attend this talk to learn real-world ML use cases that financial services enterprises have built and deployed in the AWS Cloud, such as modernization of omni-channel call centers, analysis of unstructured datasets, automated fraud detection in payments, and predictive analytics for market risk mitigation.

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John Kain, AWS FSI Business Development Capital Markets
SIBOS 2019
Machine learning in Financial Services
Real-world use case

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Please remember that past performance may
not be indicative of future results

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Data
every 5 years
There is more data than
people think
15
years
live for
Data platforms need to
1,000x
scale
>10x
grows

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40% of digital transformation initiatives
supported by AI in 2019
—IDC 2018
InnovationDecision
making
Customer
experience
Business
operations
Competitive
advantage
Data is the centerpiece for Digital Transformation

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More (and better) customer data
Ideation and
experimentation
Rapid product developmentContinuous deployment
Enhanced customer
experience
Easy provisioning of resources
Cloud technologies are accelerating this transformation

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conversational chat bots | call transcription | intelligent routing | sentiment analysis
VoC analytics | text-to speech | multilingual omni-channel communication
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 X
recommendation technology used by Amazon.com | context-aware recommendations
sentiment analysis | VoC analytics | predict business outcomes
P E R S O N A L I Z E C O M P R E H E N DR E K O G N I T I O N
I M A G E
R E K O G N I T I O N
V I D E O
Making machine learning accessible without data scientists
F O R E C A S T

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One-click
model training
and deployment
Train once
run anywhere
10x
better algorithm
performance
2x
performance increases from
model optimization with Neo
70%
cost reduction for data
labeling using Ground Truth
75%
cost reduction for inference
with Elastic Inference
REDUCE COSTS IN C R E A SE P E R F O R M A N C E IMPROVE EASE OF
USE
AMAZON SAGEMAKER
And increasing the effectiveness of data science teams

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We’re on the cusp of a new age in Financial Services
Streamlined payments
What consumers see:

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Compliance, surveillance,
and fraud detection
Document
processing
Pricing and product
recommendation
Trading and
analytics
AI/ML creates the next edge for Financial Institutions
Customer
experience
• Account opening/
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
mobile apps and
chatbots
• Call center
optimization
• Personal financial
management

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Accelerating
investigation timelines
FINRA uses Amazon Comprehend to process and review millions of
documents with unstructured data, helping flag records of interest that
should be reviewed by human investigators.

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Preventing fraudulent
attacks in real-time
Using Amazon SageMaker, NuData Security prevents credit card fraud by
analyzing anonymized user data to detect anomalous activity before a
fraudulent transaction occurs. With SageMaker, NuData reduced machine
learning development time by 60%, simplified their machine learning
architecture by 95%, and worked with a large bank to passively block
nearly 100% of fraudulent attempt traffic within the bank’s consumer
friction tolerance.

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Enforcing
compliance at scale
Coinbase uses machine learning models on Amazon SageMaker to help
with fraud prevention, identity verification, and large-scale compliance.
Using Amazon SageMaker reduced model training times from 20 hours
to 10 minutes.

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Fueling product
innovation
Using Amazon SageMaker, Intuit developed machine learning models
that can pull a year’s worth of bank transactions to find deductible
business expenses for customers. Using SageMaker, Intuit reduced
machine learning deployment time by 90%, from 6 months to 1 week.

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Improving customer
communications
FICO uses Amazon Polly to power a range of voice applications that
improve the customer experience.

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“Go as far as you can see;
when you get there,
you’ll be able to see
farther.”
J.P. Morgan

16. Thank you!