Big Data Analytics for Dodd-Frank

Big Data Analytics For
Dodd-Frank Financial
Regulations

By

Dr. Shyam Sundar Sarkar, CEO
RiskCompute
E-mail: shyam.sarkar@riskcompute.com

06/20/12 All Rights Reserved by Dr. Shyam 1
Sarkar of RiskCompute, CA

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 ;


Evolution of Financial Regulations
• Following Sox, firms rushed to implement a robust control
environment and the onus was placed squarely on compliance.

• In the US, regulators are again cracking down on financial
services to ensure firms not only have a secure control
framework but also that they can prove their framework is
relevant to all the risks experienced in the organization;

• Risk factors aren’t just financial reporting any more -- now
the issues are broad risk management and governance; the
types of controls and activities senior stakeholders want to
have more related to operational risk or compliance;

Bottom-up Vs. Top-down Approach
•In the initial years of Sox, response was process- and controls-
driven and was built from the bottom up;

• Because the program was very much based on process-level
controls, it helped companies comply but did not take a top-
down, risk-based view;

• Historically, financial services industries have built a more
bottom-up approach as opposed to a top-down, risk-focused
approach, which usually means having more controls;

• Post-financial crisis is a good time to challenge whether that
bottom-up approach is right in terms of control frameworks;

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 ;


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 changes 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 must be traded on a registered
venue, either an exchange or a swap execution facility (SEF) ;

IT and Dodd-Frank
Implementations (contd..)
• *Title VII (Wall Street Transparency and
Accountability) contd.. :: Firms need connectivity to the
clearinghouses and to multiple swap execution facilities ;
Real-time data reporting will enable regulators to see how risk
is shifting through the markets ;
• *Title VIII (Payment, Clearing and Settlement
Supervision) :: This law takes title VII a step further by
placing clearinghouses under the watch of regulators; The
clearinghouses are classified as systemically important. An
infrastructure is needed to take swaps trades conducted on
swap execution facilities and funnel them to the clearinghouses
for monitoring ;

Current Timeline for Implementation

Source: Ernst and Young LLP

Computational Model for Systemic Risk
Analysis (Office of Financial Research)


Big Data, Big Process and Process Mining
• Big Process is enterprise-wide business process transformation
(not just improvement) program that is driven by top executives;
• Big Process analysis embraces Big Data Analytics;
• Big Process mining provides an important bridge between
data mining/data analysis and business process modeling and
analysis providing techniques to more rigorously check
compliance and regulations (Dodd-Frank, SOX, Basel II/III);
• Big Process mining is an enabling technology for Continuous
Process Improvement (CPI), Business Process Improvement
(BPI), Total Quality Management (TQM), and Six Sigma, widely
used in Financial Services Processes and Regulations;

Big Process and Big Data Analytics

Business Processes, Software Systems Implementing
Trading Exchanges, Business Processes,
Supports,
Machines, Repositories, Databases,
Controls
Organizations Metadata, Communication
Systems...

Records, Events, Transactions
Model Analyzes, Specifies
(Volume and Velocity)
and Configures

Declarative Model for Big Data Analytics
Processes, Process Discovery Systems Storing and
Compliance Analyzing data generated
Rules/Policies,
Privileges, Conformance at high velocity and high
Specifications volume from Big
Process Enhancements Processes


Business Intelligence and
Process Analytics
Business Intelligence and Big Data
Analytics (Dashboards, KPIs)

Big Process Analysis (Six Sigma, TQM, BPI,
BAM, Dodd-Frank, SOX, Compliance)

Streaming Process Metadata
Data Mining Management
Volume & Velocity
Process Model
Discovery Conformance Adjustment


Straight-Through
Processing
• Straight-Through Process is designed to automate
(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 allows all of these elements to be
linked up electronically, without the need for any manual re-
keying of data anywhere along the line ;
(3) 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 ;

Straight-Through Processing


The Issue of Business Entity Identification

Source : Financial InterGroup

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


US Regulators on
Legal Entity Identifier

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 ;
ISO 17442:2012, Financial services – Legal Entity Identifier
(LEI), is aimed at meeting the data collection and analysis needs
of both national and global regulators in their responses to
problems arising from the world financial crisis.
An example LEI : F50EOCWSQFAUVO9Q8Z97


ISO Standard for LEI
ISO 17442 describes a 20-character alphanumeric code, as well
as additional elements for reference data attributes. Key attributes
of the standard include the following:
• Enables unique identification of global entities requiring an
LEI
• Defines robust open governance of the issuance and
maintenance of the LEI scheme
• Defines an LEI that contains no embedded intelligence
• Can be applied worldwide to support the financial services
industry
• Leverages the expertise of ISO/TC 68 in defining and
maintaining identifier standards
• Is persistent
• Defines a scheme that is scalable and free from assignment
limitations.

Unique Identifier Format


Common Identifier System


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 ;

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 ;

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


Straight-Through
Processing


Straight-Through Processing and
Multiple Rounds of Map/Reduce

Structure for Structure for
Reduce Securities Trade Pricing from
Market Data

The first stage in a Straight-Through Processing is equivalent to
matrix multiplication 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 ;

Straight-Through Processing and
Multiple Rounds of Map/Reduce (Contd..)
• Next stage of Map Reduce will be another matrix
multiplication over the resulting matrix from last stage and
the matrix 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 ;
• It is possible to generate dashboard from the result of any
intermediate stage ;
Pentaho or
Hadoop Map/ Jaspersoft
Hbase Hive Dashboard
Reduce
MySQL

Map Reduce


Hadoop Architecture


Tools for Data Exchange


Pipeline


Implementation with Hadoop Eco-system


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

4. Office of Financial Research:
http://www.treasury.gov/initiatives/wsr/ofr/Pages/default.aspx

5. Office of Financial research Working Paper Series:
http://www.treasury.gov/initiatives/wsr/ofr/Pages/ofr-working-
papers.aspx


Q/A

E-mail: shyam.sarkar@riskcompute.com


Sessions will resume at
11:25am

Page 34

Big Data Analytics for Dodd-Frank

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