This document discusses Accenture's regulatory reporting framework and offerings around data quality management. It provides an overview of Accenture's high-performance financial reporting framework, which aims to consolidate frameworks, processes, and technology to create efficiencies across reporting functions. It also summarizes Accenture's regulatory reporting offerings, including data quality management, capability design, target operating models, and regulatory reporting vendor implementation support. Finally, it covers key aspects of data quality management, such as issue classification, management processes, governance structures, root cause analysis, and issue prioritization. The goal is to help financial institutions improve data quality, reporting accuracy and efficiency.

1. FINANCE & RISK
DATA
QUALITY
MANAGEMENT
CLEANER DATA
BETTER REPORTING

2. TABLE OF CONTENTS
2Copyright © 2017 Accenture. All rights reserved.
SUMMARY 03
HIGH PERFORMANCE FINANCIAL
REPORTING FRAMEWORK
04
FINANCIAL REPORTING PROCESS
CAPABILITY MODEL
05
ACCENTURE REGULATORY
REPORTING OFFERINGS
06
DATA QUALITY MANAGEMENT 07
ISSUE MANAGEMENT PROCESS
AND COMPONENTS
09
GOVERNANCE STRUCTURE 10
GOVERNANCE STRUCTURE AND
INTERACTION MODEL
11
ROOT CAUSE ANALYSIS 12
ACCELERATORS 14
DATA QUALITY ISSUE
CLASSIFICATION
08
ISSUE PRIORITIZATION 13

3. SUMMARY
Identifying data quality issues and incorporating a process to investigate, prioritize and resolve these
creates a more efficient and accurate reporting environment.
Accenture offers an internal and external Regulatory Reporting Framework with a wide variety of offerings to
create efficiencies within the process and that help clients address new and complex challenges with solutions
that focus on data quality, data sourcing, and centralizing capabilities.
Copyright © 2017 Accenture. All rights reserved. 3
With the new and the ever complex external reporting
landscape, financial institutions are faced with a
variety of challenges such as:
Consequently, institutions should consider strategic
solutions that can help them effectively manage these
associated costs by:
• Manual intervention affecting straight through processing
• Lack of documentation on reporting requirements
• Operational errors (data input)
• Redundant sourcing efforts and multiple aggregation points
• Structuring operating model around data quality
• Identifying and solving issues with real business impact
• Setting up controls around issue management
• Prioritizing and remediating issue

4. Note: Please see Legend slide for definition of acronyms
HIGH PERFORMANCE FINANCIAL
REPORTING FRAMEWORK
Consolidating frameworks, processes, and technology create efficiencies across reporting functions.
External / Internal Reporting
• Same data can be used to populate reports across
the regulatory landscape and for internal purposes
reducing redundant activities
MIS / Shareholders
• With more accurate reporting results, financial results
can be produced faster
Functions
• Cross function transparency can be created allowing
for the dynamic integration of new systems
BrokerDealer
AssetManagement
Insurance
CapitalMarkets
Bank
Reporting Regulatory (External)
OCC
FDIC
NCUA
FRB
SEC
CFTC
CFPB
FHFA
FINRA
NAIC
ESFS
Basel
Functions
Risk
Treasury
Finance
Human Resources
Control
Line of Business
External Data (Market)
Legal
Workflow and Requirements
• Straight through processing of information across
all systems to help meet reporting needs
Organization
• Overarching controls and governance to help manage
risk, with well communicated roles and responsibilities
Technology
• Seamless user experience to investigate, manage
risk, and create new information and data
BENEFITS OF CONSOLIDATION
Internal Management Information System (MIS)
and Shareholders
Informational
Sr. Management
Operations
Board Actuals Forecasting
Support Organization Technology
Workflow ModelingPolicy
Training
RCSA
Procedures
Resource Location
Audit
Business Rules Performance Metrics
Calculators Aggregation
Web Services Reporting Engine
Technical Rules Batch Management
Process Workflows and Requirements
Sourcing and
Validation
Reconciliation Adjustments Transformation Distribution
Data
Management
Investigation and Analysis
Security / Audit / Controls
Copyright © 2017 Accenture. All rights reserved. 4

5. FINANCIAL REPORTING PROCESS
CAPABILITY MODEL
Level 1
Level 2
Level 3
Level 3 – Sub Category
Data Quality impacts key areas of the reporting process slowing efficiency,
increasing risk, and making it difficult to audit reporting.
CORE WORKFLOWS AND REQUIREMENTS SUPPORTING WORKFLOW
AND REQUIREMENTS
Security / Audit / Control
Investigation and
Analytics
Sourcing and Validation AdjustmentsData Management TransformationsReconciliation Report Preparation
CannedEntitlementsData Sourcing Controls TransactionMetadata Management Formulas Drill Down
Data Masking Capital ManagementTechnical Aggregated
Capital Thresholds and
Materiality
Quality Assurance
Report Changes and
Maintenance
Internal
Quality ControlExternal
Ad HocBusiness Derivation Rules Regulatory Thresholds
SOX PerformanceData Accuracy Aggregation Rules Escalation Criteria
Operations MarketRule Confirmation Escalation Tracking
Retention and Archiving Operational DashboardManual UploadLineage and Traceability Error Resolution
RiskModel Approval
RCSAControls
Model Definition and
Enhancement
Break Thresholds
Versioning DerivationsResolution
Restatement
Data Quality Pro Forma
ControlsAdjustment ApprovalData Approval Cross Report
Metrics
Model Governance
Security ReportsSourcing Adjustment InventoryData Governance Calculations
Mapping to General
Ledger
Data Mapping
Validation Rules Research Variance Commentary
AuditDatasets Report Submission
AnalyticsException Tracking Aggregation Attestation
Policy and ProceduresCapital Results Narrative
Copyright © 2017 Accenture. All rights reserved. 5Note: Please see Legend slide for definition of acronyms

6. ACCENTURE REGULATORY
REPORTING OFFERINGS
Accenture has developed a set of eight key regulatory offerings to tackle current challenges in
the regulatory reporting space.
Copyright © 2017 Accenture. All rights reserved. 6
DESCRIPTIONVALUEPROPOSITION
Regulatory
Reporting
Dashboard
Standard dashboard
components
measuring the
effectiveness of data
processing and
measurement of data
improvement over
time.
User Defined Tool
Assessment
Approach
Develop an approach
for the assessment
of manual tools for
determining a tactical
and strategic
elimination strategy.
Integrated
Reporting Tool Set
Implementation
Framework outlining
the standard process,
capabilities, and
technology
components required
to streamline reporting
and centralize finance
data for straight
through processing.
Data Quality
Management
Develop standard
data quality
management
operating model
(framework) for the
identification,
investigation, and
resolution of data
quality issues.
Capability Design
& Data Sourcing
Development and
design reporting
processes and
capabilities. Document
key steps for data
processing, populating
reports and collecting
key reporting data
attributes.
Target Operating
Model and
Governance
Create target
operating model and
governance structure
for regulatory
reporting capability to
allow for clear roles
and responsibilities,
ownership, process
documentation, and
governance.
Regulatory
Reporting Vendor
Implementation
Support the client’s
implementation of
regulatory reporting
tools through vendor
selection, business
and functional
requirement gathering,
tool configuration, and
testing/go live support.
• Increase automation
• Improve report
accuracy
• Improve data quality
• Reduce time to
change
• Reduce manual data
• Increase automation
• Improve report
accuracy
• Improve staff usage
• Reduce manual data
• Increase automation
• Improve report
accuracy
• Improve staff usage
• Reduce manual data
• Improve data quality
• Improve report
accuracy
• Reduce regulatory
actions
Improve governance
• Increase data
oversight
• Reduce risk
• Improve time
to report
• Increase ownership
• Improve
governance
• Reduce redundant
processes and
controls
• Increase automation
and functionality
• Improve report
accuracy
• Reduce manual
controls
Financial
Reporting
Robotics
Identification of
manual processes in
the key reporting
capabilities.
Assessment and
implementation of
Robots into the
regulatory reporting
operating model
• Increase
automation and
functionality
• Increase
productivity and
speed to report
• Increase security
and controls

7. WHAT IS DATA QUALITY
MANAGEMENT?
Data quality assurance preferred practice standards dictate the usage of specific capabilities to
deliver data quality.
Copyright © 2017 Accenture. All rights reserved. 7
Relevance:
Users current and potential future
needs around the data
Usability:
The ease of which data is
understood and accessed by users
Comparability:
Databases are consistent over time
and use standard agreements
Accuracy:
Information reflecting what it’s
designed to measure
Timeliness:
The release of current and updated
data within specific time constraints
Issue ManagementDimensions
Data Quality Issue Drivers
Classification– The quality of
data is classified as either an
incident, exception or an issue
Issue Management Process –
Tracking and workflows to
remediate data issues
Governance – Roles and
responsibilities for managing
and resolving issues
Root Cause Analysis –
Establishing the underlying
cause of data issues
Prioritization – Evaluating the
various ways data quality
impacts the organization and
organizing remediation efforts
Issue drivers support
the issue management
process
Data Drivers:
Data input or operational error at
the source
System Drivers:
System Processing and
transformation errors
Reporting Drivers:
Downstream / external reporting
issues / manual processing

8. • A result from a quality business or
technical rule testing failure
• Developed business or technical rule
failing or observed defect for a
specific data element at a specific
time in a given system environment
within the data element’s lifecycle
• An exception is the result of a
defined, structured testing
process
DATA QUALITY ISSUE
CLASSIFICATION
• An identified systemic problem with
one or more data elements,
informed by data quality exceptions
(testing observations) or data quality
incidents (user observations)
• Data quality issue will require root
cause analysis, remediation
planning and execution to resolve
the underlying Exceptions / Incidents.
• A defect observed during normal
usage of data
• An observation of a single defect, for
a specific data element, at a specific
time, in a given system environment,
within the data element’s lifecycle
Copyright © 2017 Accenture. All rights reserved. 8
Data Quality
Exception
Data Quality
Issue
Data Quality
Incident

9. Organizational structure that
governs different aspects of
data quality management
ISSUE MANAGEMENT PROCESS AND
COMPONENTS
Copyright © 2017 Accenture. All rights reserved. 9
Interaction Model
Issue PrioritizationRoot Cause Analysis
Governance Structure
Communication in resolving
issues (e.g. project scheduling,
remediation process)
Analyzing, standardizing and
correcting operational data
(e.g. financial, etc.)
The process of establishing
issue drivers and process for
remediation
Impacts Issue Analysis
Legend:
Impacts Entire Cycle
Issue Management Components
The issue management process consists of a series of interdependent and continuous steps to
remediate identified issues.
Issue Management Process

10. Roles, responsibilities as well as remediation action steps for managing data quality issues should be
clearly outlined.
GOVERNANCE STRUCTURE
Copyright © 2017 Accenture. All rights reserved. 10
Integrated Issue Leads (IILs)
Strategic Sourcing
(Data)
Systems Product
Issue Management (Issue Drivers)
Data Input System Driven
Reporting /
Operational
Working Groups (Remediation)
Functional Technical Operational
Chief Data Officer
• Prioritize issues for analysis
• Put in place tollgates for issue progression
• Assign issue(s) to IIL and provide analysis target timeline
Roles and Responsibilities
• Manage issues inline with target completion dates
• Update issues as required and manage to target dates
• Update RAG (Red-Amber-Green) rating and communicate status
• Identify working groups
• Investigate root cause of Issue
• Investigate business Impact
• Conduct resolution
• Communicate ongoing status
• Support issue management during analysis and remediation
Structure
Organizational
structure that
governs different
aspects of data
quality management
Interaction Model
Issue Prioritization
Root Cause
Analysis
Governance
Structure
Communication in
resolving issues (e.g.
project scheduling,
remediation process)
Analyzing,
standardizing and
correcting
operational data
(e.g. financial, etc.)
The process of
establishing issue
drivers and process
for remediation

11. Communication between issue management function and key decision makers creates transparency
into project scheduling and the remediation processes.
GOVERNANCE AND INTERACTION MODEL
Copyright © 2017 Accenture. All rights reserved. 11
Single-direction communication
#
Chief Data Officer
Integrated Issue
Lead
Issue Management Working Group
Draft Issue
Validate Adequacy
1
2
3 Root Cause
Analysis
Impacted Party
Analysis
4
Prioritize Issue
Identify
Recommended
Solutions
5
6
Monitor Remediation
Post – Close Benefit
Confirmation
7
8
Monitor
Responsible for tracking
issues to resolution
Analyze
Responsible for analyzing & prioritizing
issues
Validate
Responsible for validating
issue & parties affected
Draft Issue
Validate Adequacy
Root Cause Analysis
Impacted Party Analysis
Prioritize Issue
Identify
Recommended Solutions
Monitor Remediation
Post – Close Benefit Confirmation
Multi-direction communication
Process Step
Integrated Issue
Lead
Organizational
structure that
governs different
aspects of data
quality management
Interaction Model
Issue Prioritization
Root Cause
Analysis
Governance
Structure
Communication in
resolving issues (e.g.
project scheduling,
remediation process)
Analyzing,
standardizing and
correcting
operational data
(e.g. financial, etc.)
The process of
establishing issue
drivers and process
for remediation

12. Root cause analysis targets the source of the issue which is then remediated by the working groups.
ROOT CAUSE ANALYSIS
Copyright © 2017 Accenture. All rights reserved. 12
Target
• Booking Errors
• Manual Processes
• Inefficient User Tools
• Multiple Amendments
Data Drivers
• Issue Validation
• System Issues
• Determine Root Cause
• Identify Recommended Solution
System Error
• Downstream Issues
• External Reporting
• Regulatory Submission
• Data Warehouse Issues
Reporting
Root Cause Analysis
Source
Functional Technical
• Systems Connectivity
• Data Errors
• Data Sourcing
• Process Improvement
• Streamline Processes
• Elimination of User Tools
Operational
• Risks / Controls
• Accuracy / Completeness of Data
IssueDriversWorkingGroup
Organizational
structure that
governs different
aspects of data
quality management
Interaction Model
Issue Prioritization
Root Cause
Analysis
Governance
Structure
Communication in
resolving issues (e.g.
project scheduling,
remediation process)
Analyzing,
standardizing and
correcting
operational data
(e.g. financial, etc.)
The process of
establishing issue
drivers and process
for remediation

13. ISSUE PRIORITIZATION
Copyright © 2017 Accenture. All rights reserved. 13
Data quality issues should be prioritized by severity, complexity and business impact.
What are the ramifications to the organization today?
High: Requires immediate remedial attention
Medium: Represents issues that need corrective action
Low: Represents negligible financial, legal, or regulatory
What do I get out of fixing the issue?
Business Value
• Required for assessing prioritization of analysis efforts
• Operational efficiency
• Stronger risk management & controls
• Address regulatory & compliance needs
What is the business
benefit?
Objective Impact Quantification
• Financial & dollar impact
• Regulatory implications
• Operational cost
Issue Prioritization
Issue Severity
Value &
Complexity
Business Impact
Organizational
structure that
governs different
aspects of data
quality management
Interaction Model
Issue Prioritization
Root Cause
Analysis
Governance
Structure
Communication in
resolving issues (e.g.
project scheduling,
remediation process)
Analyzing,
standardizing and
correcting
operational data
(e.g. financial, etc.)
The process of
establishing issue
drivers and process
for remediation

14. ACCELERATORS
Accenture’s experience with elimination planning and user defined tools (UDT) has resulted in many accelerator
assets to help firms quickly organize and execute their data quality management program.
Copyright © 2017 Accenture. All rights reserved. 14
Accenture’s StrategyData Quality Framework Issue Prioritization
The data quality framework can help
detect, identify and address data
quality issues in an efficient and
decisive manner.
Assess strengths, challenges and
identify root causes by analyzing the
source. Define options to boost
efficiency and effectiveness.
Accenture helps to prioritize and take
diagnostic approach to data issues
management.

15. ACCENTURE CONTACTS
Brian Stefka
Senior Manager
Regulatory Reporting Lead
Dallas, Texas
brian.stefka@accenture.com
Albert Janer
Consultant
Accenture Finance & Risk
New York
albert.janer.becerra@accenture.com
Charles Babcock
Manager
Regulatory Reporting Lead
New York
charles.babcock@accenture.com
Samantha Regan
Managing Director
Global Regulatory and Compliance
Lead
New York
Samantha.Regan@accenture.com
Robimon Varughese
Senior Manager
Accenture Finance & Risk – RPA Lead
New York
robimon.varughese@accenture.com
For more information, contact us or visit www.accenture.com/RegulatoryInsights for our latest thinking.

16. LEGEND
Copyright © 2017 Accenture. All rights reserved. 16
CFPB: Consumer Financial Protection Bureau
CFTC: U.S. Commodity Futures Trading Commission
ESFS: European System of Financial Supervision
FDIC: Federal Deposit Insurance Corporation
FHFA: Federal Housing Finance Agency
FINRA: Financial Industry Regulatory Authority
FRB: Federal Reserve Board
NAIC: National Association of Insurance Commissioners
NCUA: National Credit Union Administration
OCC: Office of the Comptroller of the Currency
RCSA: Risk Control Self Assessment
SEC: U.S. Securities and Exchange Commission
SOX: Sarbanes-Oxley Act

17. .
Accenture, its logo, and High Performance Delivered are trademarks of Accenture.
ABOUT ACCENTURE
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delivery network —Accenture works at the
intersection of business and technology to help
clients improve their performance and create
sustainable value for their stakeholders. With
more than 401,000 people serving clients in
more than 120 countries, Accenture drives
innovation to improve the way the world works
and lives. Visit us at www.accenture.com
DISCLAIMER
This presentation is intended for general
informational purposes only and does not take
into account the reader’s specific circumstances,
and may not reflect the most current
developments. Accenture disclaims, to the fullest
extent permitted by applicable law, any and all
liability for the accuracy and completeness of the
information in this presentation and for any acts
or omissions made based on such information.
Accenture does not provide legal, regulatory,
audit, or tax advice. Readers are responsible for
obtaining such advice from their own legal
counsel or other licensed professionals.
Copyright © 2017 Accenture All rights reserved. 17