BigDataCloud Sept 8 2011 Meetup - Big Data Analytics for DoddFrank Regulations (Financial) by Shyam Sarkar

04/18/23 Copyright of Dr. Shyam Sundar Sarkar, AyushNet

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Big Data Analytics to the rescue of Dodd-Frank Financial Regulations

By

Dr. Shyam Sundar Sarkar, AyushNetE-mail: [email protected]


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About the Speaker

• 25 years of industry experience in Relational and No-sql databases with several patents and publications;

• Active developer of Hive, Hadoop ;

• Worked on Regulatory Compliance for Financial Services and Healthcare industries;

• Currently working on Big Data repository and Big Data Analytics for Dodd-Frank financial regulations;


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Events Preceeding Dodd-Frank Act

• Outsized, Unregulated OTC Derivatives Market (Bank of International Settlements Data) ;

• 2008 Collapse of Wall Street Banks (Lehman, Bear-Sterns, Morgan Stanley) ;

• Record number of Home Foreclosures (Mortgage crisis) ;

• Unprecedented US Govt. Support & Spending (Backstop banks and Money market funds, Bailout GM, AIG etc.) ;

• World-Wide Crisis and Recession ;


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Wall-Street Accountability and Consumer Protection Act of 2010

(Signed into law July 21, 2010)

• There are Titles (Sections) I to XV1 ;

• Focussing on Financial Stability :: Title I through VIII ;

• Focussing on Investor Protection :: Title IX ;

• Focussing on Consumer Financial Protection :: Title X ;

• Focussing on Mortgage Reform :: Title XIV ;

• Miscellaneous Provisions :: Title X1 - XIII, XV, XV1 ;


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IT and Dodd-Frank Implementations

• Title I (Financial Stability and Systemic Risk) :: Investment firms will be pushed to meet and agree on data symbology standards for large data storage ;

• Title IV (Regulation of Advisers to Hedge Fund and Others) :: Risk management and risk analytics capabilities will be in demand, as financial advisers will need to pinpoint potential systemic risks for a private fund ;

• Title V (Insurance) :: Insurers must move to enterprise risk managementor governance, risk and compliance (GRC) capabilities ; and consolidated financial reporting should be able to meet the new demands ;


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IT and Dodd-Frank Implementations (contd..)

• Title VI (Regulation of Banks and Saving Companies) :: Data warehousing, core systems and risk analytics, as well as maintaining clean, consistent data, will be imperative.

• *Title VII (Wall Street Transparency and Accountability) :: This law will radically change how broker-dealers, mutual funds, hedge funds and end users trade and clear OTC derivatives;

(1) Swaps trades will be guaranteed by the clearinghouse to eliminate

exposure to counter-party risk; (2) Cleared swaps contracts in the U.S. must be traded on a registered venue, either an exchange or a swap execution facility (SEF) ;


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• *Title VII (Wall Street Transparency and Accountability) contd.. ::

(3) Firms will need connectivity to the central clearinghouses and to multiple swap execution facilities ;

(4) Real-time data reporting will enable regulators to see how risk is shifting through the markets ;

• *Title VIII (Payment, Clearing and Settlement Supervision) ::

(1) This law takes title VII a step further by placing clearinghouses under the watch of regulators;

(2) The clearinghouses are classified as systemically important.

(3) An infrastructure is needed that can take swaps trades conducted on swap execution facilities and funnel them to the clearinghouses for monitoring ;


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• Title IX (Investor Protections) :: Enterprise wide risk management platforms, real-time advanced analytics, compliance systems with continuous monitoring of accounts, and surveillance systems can detect market manipulation and insider trading.

• Title X (Bureau of Consumer Financial Protection) :: This law establishes Consumer Financial Protection Bureau, or CFPB, to monitor the financial services industry for unfair and abusive practices and

(1) Set rules governing consumer financial products, including bank credit and debit cards, home mortgages, private student loans, payday loans, and overdraft loans;

(2) Banks need to install software for advanced Data management, Project management, Profitability measurement, Mortgage technology along with processes, Product inventories and Unintended consequences.


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• Title XIV (Mortgage Reform and Anti-Predatory Lending) :: Banks’ IT organizations will be tasked with responding to numerous reporting, data gathering, transaction processing and compliance issues.


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Straight-Through Processing

• Straight-Through Process is designed to facilitate automation from

(1) The front office (trading), through the middle office (risk management, confirmation & allocations) and on to the back office (clearing, payments and reporting) ;

(2) A true STP environment would allow all of these elements to be linked up electronically, without the need for any manual re-keying of data anywhere along the line ;

• An optimal business system to meet Dodd-Frank requirements will offer a high degree of automation to support a full straight-through-processing (STP) workflow ;


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Straight-Through Processing (Contd..)


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The Issue of Business Entity Identification

Source : Financial InterGroup


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Non-trivial Mapping of Business Entity Identifiers

Significant operational risk is created as the consequence of the failed / insecure interaction of manual (operations) and automated

(applications) with data

Data tagging at source and common data identifiers will minimize operational risk, lower costs, allow regulators access to individual firm and industry-wide

data for systemic risk analysis and lead the industry to Straight-Through-Processing


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US Regulators on Legal Entity Identifier

Federal Reserve, FDIC, FINRA Legal Entity Identifier: What Else Do You Need to Know?

June 17, 2011

“Confidentiality of the reference data, particularly ownership information, is likely to be a hurdle that must be discussed and addressed. The international

nature of the Legal Entity Identifier requires flexibility in confidentiality because of legal and cultural differences across borders”

Lack of global standard leads to much less transparency, inability to aggregate information efficiently and results in no audit trail ; LEI will allow Global Straight-

Through-Processing ;


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Unique Identifier Format

Source : Financial InterGroup


16Source : Financial InterGroup

Common Identifier System


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Cloud In The Horizon For Capital Markets

• The era of Big Proprietary Data centers is over for Capital Markets ;

• NYSE Technologies’ new, industry-specific Capital Markets Community Platform addresses many of Wall Street’s concerns about public clouds ;

• Straight-Through Processing with Common Identifier System and pipelined large data sets will need cloud based Hadoop Map Reduce, Hbase, Hive and BI tools ;


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Straight-Through Processing (STP) and Pipelined Map Reduce

Multiple rounds of Map / Reduce (Parallel algorithm can be structured) :

Multiple rounds of Map/Reduce lead to pipelined Map/Reduce ;

Straight-Through Processing for Dodd-Frank financial regulations can be implemented using multiple rounds of (pipelined) Map/Reduce phases

executed in parallel ;


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Matrix Definition Over Securities Trade(Millions or Billions of Rows and Columns)

(Identities) Buyer_1 Buyer_2 Buyer_3 Buyer_4

Seller_1(Obj_1) 1 0 1 0

Seller_1(Obj_2) 0 0 0 1

Seller_2(Obj_3) 1 1 1 0

Seller_3(Obj_4) 1 0 1 0

Seller_3(Obj_5) 0 0 1 0

…. ….. …. ….

Seller_n(Obj_m) 1 0 1 0


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Straight-Through Processing


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Straight-Through Processing and Multiple Rounds of Map/Reduce

Structure for Pricing from Market Data

The first stage in a Straight-Through Processing is equivalent to product (multiplication) over two structures and rule/policy application as follows ::

Map function = (Structure for Securities Trade) X

(Structure for Pricing from Market Data)

Reduce function = Rule/policy application on result of Map function ;

Structure

for Securities Trade

Reduce


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Straight-Through Processing and Multiple Rounds of Map/Reduce (Contd..)

• Next stage of Map Reduce will be another product of structures over the resulting structure from last stage and the structure created from Trading Portfolios ;

• This stage will have a Reduce phase with specific rules to apply ;

• Each stage of Map Reduce can run in parallel ;

• There will be many such stages for overall Straight-Through Processing involved in Dodd-Frank financial regulations ;

• It is possible to generate dashboard from the result of any intermediate stage ;

Hadoop Map/Reduce Hbase Hive

Pentaho or Jaspersoft

MySQLDashboard


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References

1. InformationWeek Dodd-Frank Cheat Sheet :

http://www.banktech.com/dodd-frank?cid=IW

2. Financial InterGroup : http://financialintergroup.com/

3. U.S. Congress website : http:// www.opencongress.org

Technology

BigDataCloud Sept 8 2011 Meetup - Big Data Analytics for DoddFrank Regulations (Financial) by Shyam Sarkar