Intermediate Holding Company - Data Governance Approach

1. Intermediate Holding Company – From Data Quality to Data Governance
June 2014
Stephanie Baruk sbaruk@chappuishalder.com

2. 2
• Macro and idiosyncratic capital stress testing scenario
generation process
• Forecasting models (losses for credit risk and
operational risk, pre‐provision net revenue, financial
statements…)
• Basel III pro‐forma estimation
• Regulatory capital calculation under Basel III
• Model documentation and validation
• Documented processes and controls across
components of CCAR and DFAST processes
Capital Planning and Stress Testing1
• Counterparty legal identifiers and mapping of
counterparties across families
• Exposure measurement and aggregation across
entities on a daily basis, both on an IHC and
consolidated US basis
• Parallel calculation of derivatives exposure using
Current Exposure Method and of securities
finance exposure using pre‐determined haircuts
that depart from industry practices
• Definition of the data collection process (eligible
collateral, eligible guarantees, eligible credit and
equity derivatives, other eligible hedges, etc.)
• Reporting at the IHC and consolidated US level
Single‐Counterparty Credit Limits 2
• Cash flow projections for IHC and branch based on
dynamic analysis over short and long‐term horizons
• Stress testing analytics and models and impact analysis
across the business and legal entity levels
• Liquidity buffer calculation using daily internal and
external cash flows
• Sensitivity analysis of stressed liquidity ratios to key
assumptions related to macro‐economic factors, P&L and
balance sheet items, reputational events, etc.
• Monitoring of limits on funding concentrations
Liquidity Management3
• Expansion of regulatory, risk and financial
reporting systems to cover Fed requirements
such as:
• Risk‐based capital reporting for
institutions subject to the advanced
capital adequacy framework
• Country Exposure Report
• CCAR report
• Banking organization systemic report
• Consolidated financial statements…
Reporting Requirements 4
Data
&
Systems
Intermediate Holding Company
Key data and systems requirements

3. 3
From Data Quality to Data Governance
Our Approach
 Assess impacts of data quality issues (data
errors mainly due to incorrect manual
inputs or system issues and data gaps)
 Identify and prioritize remediation actions
to be performed and define roles and
responsibilities
 Perform remediation actions on potential
weaknesses regarding:
 Availability and sourcing
 Ownership
 Completeness
 Compliance to IHC requirements
 Missing and outliers values
 Extreme values
 Inconsistencies…
 Elaborate recommendations to mitigate
risks regarding data quality (controls to be
performed, process to be improved, etc.)
II – Remediation Actions
 Define and circulate the methodology and
templates for data requirements
collection across the different work
streams of the IHC program
 Coordinate with the different work
streams to gather data requirements and
quality assessment
 Challenge the data gap assessment
provided by each work stream by
performing:
 Data profiling and performance tests
(see next slide)
 A cross‐analysis to identify overlaps or
inconsistencies
 Consolidate and homogenize all analyses
and results
 Map all data elements with key criteria
(ID, format, source, owner, frequency,
etc.) and identify interdependencies
 Take ownership on common data
I – Requirements Identification
 Set up the data governance framework
ensuring sustainability of the gains
achieved through data remediation
 Perform a new data quality assessment
after remediation actions
 Identify market best practices in terms of
data management tools
 Assess costs and benefits of the solutions
available:
 Identify pros and cons of each
solution
 Build the business case
 Build a data repository with consistent
information across the organization
Data Requirements
and Quality
Assessment
Remediation Plan
Data Management
Tools
III – Monitoring & Solutions

4. 4
Data Quality Assessment
Zoom on performance tests
Test Definition Test Purpose Test Nature
Availability
Completeness
Compliance
Validity
Acceptability
Accuracy
Consistency
Relevance
Twisting
• Is the data easily available in the systems?
• Is it available at the appointed time?
• Has all available data been taken into consideration?
• Is the entire perimeter covered? All relevant variables taken?
• Are there any missing values or outliers?
• Are volumes significant enough to threaten the data’s validity?
• Is the data set correctly adjusted to deal with extreme values?
• Are the adjustments on missing values and outliers valid?
• Are the adjustments acceptable? Do they generate distortions?
• Is it acceptable to replace missing values by “0”?
• Does the data set reflect a real‐world fact or event?
• Are the indicated trends accurately reflected in the state of the
economy?
• Is variable evolution consistent with other internal sources?
• Is it consistent with the nature of products? Geographic region?
• Are the hypotheses used for transforming data relevant?
Pertinent?
• Are they justified? Necessary?
• Does the transformation generate significant distortions?
• Does it twist reality? Deform results?
Data Selection &
Data Check
Data
Cleansing
Data
Profiling
Data
Transform
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5. MONTREAL
202 – 1819 Bd Rene
Levesque O.
Montreal, Quebec,
H3H2P5
PARIS
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Neuville
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NIORT
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79000 Niort, France
NEW YORK
1441, Broadway
Suite 3015, New York
NY 10018, USA
SAN FRANCISCO
388 Market Street
San Francisco, CA 94111
SINGAPORE
Level 25, North Tower,
One Raffles Quay,
Singapore 048583
HONG KONG
905, 9/F,
Kinwick Centre 32
Hollywood Road,
Central, Hong Kong
LONDON
Palladia
60 Lombard street
London EC3V 9EA, UK
GENEVA
Rue de Lausanne 80
CH 1202 Genève, Suisse