The document appears to be a slideshow presentation given by Raghu Raman from FINRA at a public sector summit. The presentation discusses FINRA's use of artificial intelligence and machine learning to detect fraud and model risk in the financial sector. It also describes how FINRA is using Amazon Web Services and cloud computing to analyze large amounts of market and trading data to protect investors and ensure market integrity. Finally, the presentation provides examples of how AI can help FINRA employees like Jamie more efficiently analyze the large volumes of unstructured documents FINRA receives each year.

1. P U B L I C S E C T O R
S U M M I T
Washington DC

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S U M M I T
The Future of Fraud Detection
and Risk Modeling with AI/ML
Raghu Raman
Senior Director
FINRA
2 9 6 6 8 9

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S U M M I T

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Regulating Stock Trading
FINRA, BOX, Cboe, IEX, NASDAQ and
NYSE Fine Instinet, LLC for Market
Access Rule Violations
https://www.finra.org/newsroom/2018/finra-box-cboe-iex-nasdaq-and-nyse-fine-
instinet-llc-market-access-rule-violations

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Regulating the Brokerage Industry
Finra suspends former UBS broker who
traded excessively in elderly
clients' accounts
https://www.investmentnews.com/article/20181126/FREE/181129970/finra-suspends-former-
ubs-broker-who-traded-excessively-in-elderly

7. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R
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of storage
30+pbevents per day
135 Billion
Up to
Monitoring 99% Equities &
65% Options in the US
Reconstructing
Trillions
of Market Nodes & Edges
Investor
protection
Market
integrity

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S U M M I T 8
Extracting insights from big data

9. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R
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Benefits of the Cloud
Reduce cost $
Increase scale and elasticity
Innovate faster
Continuous technology refresh
Improve security
Increase availability and recoverability
Automate everything

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Remember Brad?
How do we make things better for him?

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What does the data say?

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Rule Based Inference

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Machine Learning Based Inference

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The thousand words…
14

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A picture instead…
15

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Buy Side Gaming
16
Positives Negatives

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Happy Brad!

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Remember Jamie?

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Jamie’s challenges
• High volumes of unstructured content
• ~1M documents each year from stock brokers and investors to be
reviewed
• Documents contain incredibly useful information but mining is a
challenge
• Overlooking important information
• Risk-based approach itself presents risks
• Numerous features of interest
• Finding information about the Who, What, Where, When and How
is labor intensive

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Solution: Jamie’s Workbench

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S U M M I T 21
Text Analytics Platform (TAP)
Internal
Reference Data
Documents
Forms
Text Analytics Platform (TAP)
External
Reference Data
Individuals
Organizations
Classifications
Summaries
Relationships
Internal ID-s
Similar docs
Document Preparation
Entity Extraction
Entity Resolution
Entity Coreference
Document Classification
Document Summarization
Document Similarity

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Architecture <bucket>/input
Data
Preparation
Entity
Matching
Amazon Comprehend
<bucket>/matched

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S U M M I T
You can do it too
Evolve technology constantly for business value
Allow curiosity driven research than executing to a detailed
plan
Business Experts and data Scientists operate as one
Approach data with humility and allow it to reveal its story

24. Thank you!
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R
S U M M I T
Raghu Raman
Raghu.raman@finra.org

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