Capability Design & Data Sourcing

CAPABILITY
DESIGN & DATA
SOURCINGACCENTURE FINANCE & RISK

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 CLASSIFICATION 07
WHAT IS CAPABILITY DESIGN VS
DATA SOURCING
08
IDENTIFYING ADJUSTMENTS 09
CLEANSING PROCESS 10
WHAT IS THE TRANSFORMATION
PROCESS
11
TRANSFORMATION
DOCUMENTATION
12
WHAT IS THE DISTRIBUTION
PROCESS
13
DATA SET ORDERING & REPORT
DISTRIBUTION
14
WHAT IS DRIVING CHANGE 15
MANAGEMENT OF CHANGE FROM
SOURCE & REPORT
16
ACCELERATORS 17

SUMMARY
Copyright © 2017 Accenture. All rights reserved. 3
Accenture has developed a capability design and data sourcing methodology which provides a structured
framework for data processing, report population, and collection of reporting requirements.
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 processes increasing risk
• Lack of documentation outlining business logic and business
requirements
• Difficult to investigate adjustments and data inputs
• Manage changes from report to source
• Business logic and data requirement documentation
• Implementing process capabilities to enhance efficiency and
straight through processing
• Improving quality of information through the reduction of
adjustments
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.

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

FINANCIAL REPORTING PROCESS
CAPABILITY MODEL
Level 1
Level 2
Level 3
Level 3 – Sub Category
Accenture’s standardized regulatory reporting process capability model helps accelerate
assessment and change.
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
5Copyright © 2017 Accenture. All rights reserved.Note: Please see Legend slide for definition of acronyms

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

Descriptive element whose value is determined through
logic statement with multiple input elements.
Examples of Derived Elements
• Indicators
• Flags
• Identifiers
The use of one or many calculated, derived, referenced,
core data to filter and categorize information into target
outputs and desired results.
Examples of Aggregated Results
• Reporting line items
• Dashboard outputs
Measure element whose value is determined through
mathematical operation between multiple input
elements.
Examples of Calculated Data
• Dollar outcomes
• Statistical outcomes
• Metrics
DATA CLASSIFICATION
Copyright © 2017 Accenture. All rights reserved.
7
A collection of related sets / aggregated results that is
composed of separate elements and which is manipulated
into an overall report.
Examples of Data Sets
• FR Y-9C
• FFIEC 101
Specific types of data utilized for the enrichment of data for
reporting, calculations, or other derivations.
Examples of Reference Data
• Codes, and currencies
• Risk grades, product types
• Business unit details (e.g., line of business, legal entity)
Data requirements sourced from front office systems and
serve as inputs to calculated and derived elements or
aggregated results.
Examples of Core Data
• Dates
• Amounts
• Indexes
A
D
CB
E
GF H JI K
Data Sets Aggregated Data
Calculated Data Derived Data
Core Data Reference Data
+ +
+
Data FlowData Types Data Types
1
2
3 3
4 4
Data classification is a key component within the data flow to document logic and sourcing
efforts.
Legend: Report Data

WHAT IS CAPABILITY DESIGN VS DATA SOURCING?
A
D
CB
E
GF H JI K
+ +
+
Data FlowProcess Capabilities Data Sourcing
• Attestation
• Variance commentary
• Cross report rec
• Report preparation
• Aggregations
• Calculations
• Derivations
• Reconciliation
• Validations
• Data sourcing
DistributionTransformationCleansing
SourcetoTarget
Adjustments
Security/Audit/Control
TargettoSource
Investigation&Analytics
DataManagement
Change Management
• Report
decomposition
• Core & reference
Data identification
• Model validations
• Logic documentation
The most efficient way of designing process capabilities is from source to target whereas the
most effective way to document data sourcing is to use a target to source approach.
Legend: Report Data

Change
IDENTIFYING ADJUSTMENTS
• Adjustments made at the
report level, changing
the end result
• Lowest level of information
used as inputs to derived,
calculated, and aggregated
results
Types of Adjustments Explanation
Transaction Existing
Manual Upload
(New Transaction, New Aggregated)
Aggregated Existing
• Gaps in data sourcing efforts
• Multiple User Defined Tools to
develop the manual upload of
information
• Transactional information is not
available
• Transactional information is not
corrected
• Upstream investigation not
available (views into errors)
• Incorrect input at the source
• Incorrect transfer between systems
• Stale or unavailable reference
information to enhance information
• The manual population of
missing information within a
system
• Manual adjustments can occur
to existing data at the report
level
• Adjustments can occur to new
transactions
Challenges
Identifying and inventorying adjustments, where they occur, and the drivers/causes of the
adjustments help make the data cleansing process more efficient.
DistributionTransformationAdjustments

CLEANSING PROCESS
The cleansing process identifies adjustments and provides potential solutions for reducing these in the
future by improving data quality and sourcing efforts.
SolutionsAdjustment ProcessIdentifying Adjustments
• Report preparation• Sourcing / Validation
• Reconciliation
• Transformations
Transaction
(Record Level)
Aggregation
(Report Level)
Manual Upload
(Missing Data)
• Incorrect inputs
• Doesn’t reconcile
• Incorrect logic
• Missing components
• User tool errors
• Lack of investigative
capabilities
• Data not available
Types of adjustments
utilized
Impacts to process
capabilities
Typical corrections
made through the
process
• Sourcing / Validation
• Report preparation
• Inputs not available
• User tools outputs
• Data not available
Outputs
1
2
3
• Improve data quality
• Improve sourcing efforts
• Improve investigation &
analytics
• Dashboard (for monitoring
progress)
• Elimination of user tools
4 Potential Improvements for
Reducing Adjustments
ChangeDistributionTransformationAdjustments

WHAT IS THE TRANSFORMATION PROCESS?
In our experience, having correct inputs eliminates transformation errors and is the key factor to
accurate outputs.
• Order of operation and business
rules needed to complete a
calculation, derivation, or
aggregation
• Core and reference information
needed as inputs / variables to
preform a calculation, derivation,
or aggregation
Transformation Process Explanation
Inputs
Outputs
Logic
• Incorrect inputs and logic
• Operational process breakdowns
• User tools and manual manipulation
of information, aka User Defined
Tools
• Inputs not available
• Incorrect formula / Business rules
• Inadequate formula
documentation
• Incorrect sourced data
• Reference data lacks the detail
needed for enrichment
• Results of a calculation,
derivation or aggregation
Challenges

Inputs
Inputs to calculations and
derivations are not well
documented making it difficult to
investigate results and to make
changes.
TRANSFORMATION DOCUMENTATION
Outputs
In the future, formulas should be
stored, executed in a centralized
location and shared across
consumers where applicable.
Logic
Formulas are being documented
inconsistently, scattered across
different systems and in some
cases embedded in code
Data inputs and logic are currently being documented inefficiently. Organized sourcing efforts and logic
documentation can help create a dynamic reporting process.
Systems
Best
Practices
Industry
Standards
Core
Reference
Core
Reference
Reference
Reference
Calculated
Derived
Derived
Calculated
Aggregated Results
Aggregated Results
Aggregated Results
Calculated
Derived
Aggregated Results
Documented
Logic
Report
Report
Report
Decomposition

WHAT IS THE DISTRIBUTION PROCESS?
Defining clear roles and responsibilities in the distribution process can result in accurate results and
outputs.
A
D
CB
E
GF H JI K
+ +
+
Data FlowRoles Data Types
Report
Owners
Centralized
Manufacturing
Source
Systems
Coreand
Reference
Transformation
Logic
Report
Population
• Responsible for data sets
and aggregation definition
• Responsible for receiving
and retrieving calculated
and derived data
• Responsible for supplying
core and reference data
Legend: Report Data

Report DistributionData Set Ordering
DATA SET ORDERING & REPORT DISTRIBUTION
A self service ordering process creates ownership at the report level for dataset and aggregation
development.
Data specifications
(Name, purpose,
service level
agreements, summary
of aggregate
(calculations,
derivations, core and
reference data)
New report, change to
a report
A C DB
E GF
Aggregate
Calculations /
Derivations
Dataset
H
Reference /
Core Source
Decomposition: Investigating calculations
and derivations to identify input
requirements needed to generate results
Sourcing: The input of requirements from
applicable systems and tools
Model Governance and Sourcing
Send requested data
aggregate from the dataset
for consumption
+ +
Documentation: Document outputs, logic
and inputs for future reference and change
Report Owners
(Report preparation, variance
commentary, attestation)
Data Distributors
(Responsible for building,
producing the dataset)
Manufacturing Process
(Creating the data)
Ordering Data
(Aggregations)
Report Owners
(Dataset)
Maps dataset to report for the
completion of the reporting
process

WHAT IS DRIVING CHANGE?
• Changes to business rules and
logic needed to populate correct
outputs for calculations,
derivations, and aggregations
• Changes that occur at the
report level
• Examples: Change to a
report line item / aggregate
Types of Change Explanation
Reports
Data Requirements
Logic
• Communication of change
downstream is not available
• Lack of documented traceability and
transparency into logic calculations
and business needs for reporting
• Lack of model governance
• Logic and lineage information
used for investigation not available
• Minimal controls to quality
assurance and control outputs
• Aggregation logic not documented
• Inability to change quickly resulting
from regulatory updates
• Difficulties tracking upcoming
change
• Changes to the source of data
• Example: Parameters / data
specifications
Challenges
Changes occur at different levels, impacting roles and information across the process.

MANAGEMENT OF CHANGE FROM SOURCE
& REPORT
Data changes that occur should be communicated and updated across source systems, centralized
teams, and report owners.
Source Systems Centralized Team Report Owner
Change in Source
 Change in underlying
information
 Change in system
 Change in technical
information, aka field
name
 New available
information
Business
Updates
Change in Data Sourcing
New information might make
the existing transformation
change
Update data with validation
fields (e.g., new address, name
etc.)
Changes in Output
Updates transformations,
reconciliations, and validations
Update data with new instances
and mapping rules( e.g.
calculations)
Change in Distribution / Reporting
Have new information available
for new investigation and
analytics
Test accuracy of outputs
Change in New Report
Provide parameters, dataset, and
aggregations for distribution
Test accuracy of output
Change Management
Identify and source physical instances of
data to satisfy core & reference data
requirements. Code logic for new
calculations and derivations
Decompose calculations / derivations and
identify required core and reference data
inputs
Updating Data Sourcing
Update data with new required
physical instances
Update data with new modified
sourcing requirements
Change in Report
• New report
• Existing Report
• Change in data
requirements and
logic for report
population
Technical
Updates
Business
Updates
Technical
Updates

ACCELERATORS
AdjustmentsData Classification Change and
Implementation
A key component to help clients with
classifying and documenting
sourcing efforts
The process of identifying current
adjustments to future efforts
A proactive approach for managing
both business and operational
change enhances risk-awareness
for changes to source and target
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 reporting program.

REGULATORY REPORTING OFFERING OVERVIEW
ACCENTURE CONTACTS
Samantha Regan
Accenture Finance & Risk
1345 Avenue of the Americas
New York, NY 10105
samantha.regan@accenture.com
Aisha Kafati
800 Boylston Street, Suite 2300
Boston, MA 02199
aisha.kafati@accenture.com
Brian Stefka
5221 North O'Connor Blvd., Suite 1400
Irving, TX 75039
brian.stefka@accenture.com
Charles Babcock
New York, NY 10105
charles.babcock@accenture.com
Albert Janer
1345 Avenue of Americas
New York, NY 10105
albert.janer.becerra@accenture.com
Mark M. Lee
New York, NY 10105
mark.m.lee@accenture.com
Brittni Daniels
New York, NY 10105
brittni.daniels@accenture.com

LEGEND
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

.
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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. 20

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