Data Governance approach to improve data quality, transparency and sustainability

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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