staying ahead of the curve: leveraging big data platforms and data … · 2019-07-27 · leveraging...

STAYING AHEAD OF THE CURVE: LEVERAGING BIG DATA PLATFORMS AND DATA ANALYTICS TO ENSURE COMPLIANCE

NATHAN GREENE, MODERATOR, PARTNER, SHEARMAN & STERLING LLPKEN C. JOSEPH, MANAGING DIRECTOR, DUFF & PHELPS, LLCSIMON LORNE, VICE CHAIRMAN AND CHIEF LEGAL OFFICER, MILLENNIUM MANAGEMENTCHRISTINA K. McGLOSSON, ASSOCIATE DIRECTOR, COMMODITY FUTURES TRADING COMMISSION

May 17, 2018

Shearman & Sterling LLP 2Leveraging Big Data Platforms and Data Analytics to Ensure Compliance | May 17, 2018

AGENDA

BIG DATA PLATFORMS, DATA ANALYTICS AND COMPLIANCE 03• Key Big Data Concepts 05

• Data Sets Available to Compliance Teams 06

• Varieties of Artificial Intelligence 07

• AI and Big Data at the Regulators 09

• Challenges in Adapting to the New Landscape 10

• Risks of Non-Adoption of New Technologies 12

• Quick Big Data Checklists 14

• Quick AI Checklists 15

QUESTIONS 16

RESOURCES 17


BIG DATA PLATFORMS, DATA ANALYTICS AND COMPLIANCE


“Technology and AI will free man from all

mundane chores and allow him to spread his

horizons and elevate to a higher form of

creative thinking which otherwise would not

have been possible.”

-- Technology industry CEO


KEY BIG DATA CONCEPTS

SPEED & SCALE DATAFICATION BIG DATA SKILL SETS

A big data strategy leverages three principles (the so-called“three V’s”):

• increasing volumes of data that can be incorporated into models,

• increasing variety of types and breadth of available data, and

• increasing velocity at which data can be processed.

In the past much data could not be digitized. No more.

• In 2000 only 25% of stored data was digitized; today it’s 98%.

• Migration from structured and “scrubbed” data sets to natural data sets.

• Data scientists

• Data sourcing

• Data security

• Domain experts

• Computing

Note: The authors credit these taxonomies to Citi’s 2015 Big Data in Investment Management White Paper. See our resources pages.


DATA SETS ALREADY AVAILABLE TO COMPLIANCE TEAMS

CUSTOMER FACING INTERNAL PUBLIC

• Account statements, transaction histories

• Email, phone, chat logs

• Customer contracts

• Web traffic

• Customer files

• CRM software

• Trade or other transaction records

• Email, phone, chat logs

• Calendars

• Payment and receipt ledgers

• Office keycard activity

• Employee internet activity

• Geolocation data for firm devices

• SEC filings

• Market activity

• News feeds

• Court filings

• Various social media activity (e.g., LinkedIn)

• Political contribution databases


VARIETIES OF ARTIFICIAL INTELLIGENCE

ASSISTED INTELLIGENCE

(SOMETIMES CALLED

“AUTOMATION”)

AUGMENTED INTELLIGENCE

(SOMETIMES CALLED “AI”)

AUTONOMOUS INTELLIGENCE

(SOMETIMES CALLED

“MACHINE LEARNING”)

Computers take on

automation of

existing processes,

including the

replacement of

repetitive and

standardized tasks

previously done by

humans

Where humans and

technology learn

from each other,

empowering humans

to do things they

couldn’t do before

Computers make all

the decisions and

can program

themselves (learn

and improve from

experience)

Note: The authors credit various writings by PwC’s Anand Rao. See our resources pages.


“Modern artificial intelligence is much feted.

But its talents boil down to a superhuman

ability to spot patterns in large volumes of

data.”

-- The Economist


AI AND BIG DATA AT REGULATORS (EXAMPLE: THE SEC)

INNOVATION OR

NECESSITY?

AI / BIG DATA AT THE SEC WHERE IS IT GOING?

• Oversight responsibility is massive: over 4,000 public companies and 28,000 registrants

• Keeping pace with sophistication of market participants and practices

• Reduced budgets and staffing demands risk-based approach to oversight

• Analytical methods allow review of previously opaque disclosures

• Machine learning and text analytics

• Data sources

• “Teaching the machine”

• Limitations (and need for humans)

• Data in enforcement actions

• CFTC Big Data Program

• Investment Adviser Examinations

• Financial Reporting Misconduct

• Insider Trading

• Market Oversight and Surveillance

• Public Company Reviews


CHALLENGES IN ADAPTING TO THE NEW LANDSCAPE

• Compliance staffs need to be able to appropriately react to and support or scrutinize new business use cases for big data and AI

• Will need to think broadly how new data and AI functions interact with traditional regulatory paradigms – e.g. auditability, discerning “intent” in automated systems, when “data” crosses lines into MNPI, antitrust, unlawful discrimination, etc.

Keeping Up With Front-of-House Uses

• Compliance teams need to stay plugged in as firms bring on new technology functions and vendors (how will “know your vendor” compliance processes change and adapt?)

• Data sourcing and “lineage” diligence will explode in importance, as will assessments of when PII and other sensitive data has been sufficiently scrubbed

Diligence of Data Vendors and Service Providers


CHALLENGES IN ADAPTING TO THE NEW LANDSCAPE

• Do firms need to invest in a new technology system requiring custom integration, or might the new system be built on a foundation of existing systems?

• Staff experienced in applying Big Data analytics and AI to business functions (front office, compliance, etc.) will be required – rise of the “technologist”

Systems and Staff

• There will always be resistance to new technological developments

• Especially so when there is a perception of “replacing the human” or “unleashing the demon”

Human Resistance


RISKS OF NON-ADOPTION OF NEW TECHNOLOGIES

It’s an arms race …New data sets, machine learning and natural language processing are being adopted by competitors (and regulators)

Regulator expectations for firms (and themselves) are data-driven

… Do you want regulators to know your data better than you do?


“By far the greatest danger of Artificial

Intelligence is that people conclude too early

that they understand it.”

-- AI researcher


• Regulatory

• Regulated data (e.g., PII, health, nat’l security sensitive)

• Data security (including cyber)

• Cross-jurisdiction data transfer rules

• Public sector data

• Antitrust, MNPI, etc.

• Web scraping (CFAA and site terms of use)

• Protected classes (e.g., data on children)

• Discriminatory impact

• Is being a data seller a regulated activity?

• Contracting

• Chain of ownership diligence and reps (“data lineage” /

“consents”)

• Data security reps and terms

• Regulatory compliance reps

• License terms

• Intellectual property

QUICK BIG DATA CHECKLISTS

(For Lawyers)


• Regulatory

• Auditability (“My AI did it” is not an excuse; consider

GDPR’s right to an AI explanation)

• Discriminatory impact

• Redundancy and business continuity

• What level of accuracy is required?

• Too much capability? Will you be criticized for not using

features or use cases?

• Can a machine be a fiduciary?

• HINT: There’s data inside (see data checklists prior slide)

• Contracting

• Vendor diligence

• Cutting through jargon

• Support and customization expectations

• License terms, including who has the keys

• Indemnification (and capital behind it)

• Intellectual property

QUICK AI CHECKLISTS

(For Lawyers)


QUESTIONS?


RESOURCES


European Commission: Artificial Intelligence for Europe (April 2018), available at https://ec.europa.eu/digital-single-market/en/news/communication-artificial-intelligence-europe

Google’s Solution to Accidental Algorithmic Racism: Ban Gorillas, The Guardian (Jan. 2018), available at https://www.theguardian.com/technology/2018/jan/12/google-racism-ban-gorilla-black-people

ESMA, EBA and EIOPA Final Joint Committee Report on the Use of Big Data by Financial Institutions (March 2018), available at http://www.eba.europa.eu/documents/10180/2157971/Joint+Committee+Final+Report+on+Big+Data+%28JC-2018-04+%29.pdf

Financial Stability Board, Artificial Intelligence and Machine Learning in Financial Services (Nov. 2017), available at http://www.fsb.org/2017/11/artificial-intelligence-and-machine-learning-in-financial-service/

AI AND BIG DATA RESOURCES


AI Everywhere & Nowhere Parts I-III, available at http://usblogs.pwc.com/emerging-technology/ai-everywhere-nowhere-part-1-tempered-excitement/, http://usblogs.pwc.com/emerging-technology/ai-everywhere-nowhere-part-2-ai-is-ui-ubiquitous-intelligence/, http://usblogs.pwc.com/emerging-technology/ai-everywhere-nowhere-part-3-ai-is-aaai-assisted-augmented-autonomous-intelligence/

Some Financial Regulatory Implications of Artificial Intelligence, Larry D. Wall, Federal Reserve Bank of Atlanta (Sept. 2017), available at https://www.philadelphiafed.org/-/media/bank-resources/supervision-and-regulation/events/2017/fintech/resources/some-financial-regulatory-implications-of-artificial-intelligence.pdf?la=en

UK Information Commissioner’s Office (Sept. 4, 2017), Big Data, Artificial intelligence, Machine Learning, and Data Protection, available at https://ico.org.uk/media/for-organisations/documents/2013559/big-data-ai-ml-and-data-protection.pdf.

SEC Staff Speech (June 21, 2017), The Role of Big Data, AI and Machine Learning in Assessing Risk: A Regulatory Perspective, available at https://www.sec.gov/news/speech/bauguess-big-data-ai.


http://usblogs.pwc.com/emerging-technology/ai-everywhere-nowhere-part-1-tempered-excitement/

http://usblogs.pwc.com/emerging-technology/ai-everywhere-nowhere-part-2-ai-is-ui-ubiquitous-intelligence/

http://usblogs.pwc.com/emerging-technology/ai-everywhere-nowhere-part-3-ai-is-aaai-assisted-augmented-autonomous-intelligence/

https://www.philadelphiafed.org/-/media/bank-resources/supervision-and-regulation/events/2017/fintech/resources/some-financial-regulatory-implications-of-artificial-intelligence.pdf?la=en

https://ico.org.uk/media/for-organisations/documents/2013559/big-data-ai-ml-and-data-protection.pdf

https://www.sec.gov/news/speech/bauguess-big-data-ai


Forbes, The Complete Beginner’s Guide to Big Data in 2017 (March 14, 2017), available at https://www.forbes.com/sites/bernardmarr/2017/03/14/the-complete-beginners-guide-to-big-data-in-2017/#3d9fbf2c7365.

Whatever Happened to the DeepMind AI Ethics Board Google Promised, The Guardian (Jan. 2017), available at https://www.theguardian.com/technology/2017/jan/26/google-deepmind-ai-ethics-board

Speech of Commissioner Kara M. Stein (Oct 28, 2016), A Vision for Data at the SEC, available at https://www.sec.gov/news/speech/speech-stein-10-28-2016.html; Speech of Commissioner Kara M. Stein (April 14, 2015), The Dominance of Data and the Need for New Tools: Remarks at the SIFMA Operations Conference, available at https://www.sec.gov/news/speech/2015-spch041415kms.html.

Stanford University: One Hundred Year Study on Artificial Intelligence (AI100), (Aug. 2016), available at https://ai100.stanford.edu/sites/default/files/ai100report10032016fnl_singles.pdf


https://www.forbes.com/sites/bernardmarr/2017/03/14/the-complete-beginners-guide-to-big-data-in-2017/#3d9fbf2c7365

https://www.sec.gov/news/speech/speech-stein-10-28-2016.html

https://www.sec.gov/news/speech/2015-spch041415kms.html

https://ai100.stanford.edu/sites/default/files/ai100report10032016fnl_singles.pdf


FTC Report, Big Data: A Tool for Inclusion or Exclusion? Understanding the Issues (January 2016), available at https://www.ftc.gov/system/files/documents/reports/big-data-tool-inclusion-or-exclusion-understanding-issues/160106big-data-rpt.pdf.

Citi, Big Data in Investment Management (2015), available at https://www.cmegroup.com/education/files/big-data-investment-management-the-potential-to-quantify-traditionally-qualitative-factors.pdf.

Analytics Without Limits: FINRA’s Scalable and Secure Big Data Architecture Parts 1 and 2, available at https://aws.amazon.com/blogs/publicsector/analytics-without-limits-finras-scalable-and-secure-big-data-architecture-part-1/ and https://aws.amazon.com/blogs/publicsector/analytics-without-limits-finras-scalable-and-secure-big-data-architecture-part-2/.

LabCFTC, the CFTC’s focal point for its efforts to promote responsible FinTech innovation and fair competition, available athttp://www.cftc.gov/LabCFTC/Overview/index.htm.


https://www.cmegroup.com/education/files/big-data-investment-management-the-potential-to-quantify-traditionally-qualitative-factors.pdf

https://aws.amazon.com/blogs/publicsector/analytics-without-limits-finras-scalable-and-secure-big-data-architecture-part-1/

https://aws.amazon.com/blogs/publicsector/analytics-without-limits-finras-scalable-and-secure-big-data-architecture-part-2/

http://www.cftc.gov/LabCFTC/Overview/index.htm

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staying ahead of the curve: leveraging big data platforms and data … · 2019-07-27 · leveraging...

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