The document discusses how financial institutions can turn regulatory compliance data into opportunities for competitive advantage. It provides examples of how anti-money laundering (AML) and customer data used for compliance can also power initiatives like cross-selling, improving the customer experience, and strategic capital planning. The document recommends a balanced approach between meeting regulatory requirements and building a flexible data architecture that allows data to be reused across business units.

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16. Turn Enterprise Data Challenges into Opportunities
Meet Regulatory Standards and Leverage Compliance Data for a Competitive Edge
June 2015

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Agenda
• Introduction
• Industry Data Challenges
• AML Data as an example
• Data Architecture Design & Implementation Approach
• Beyond Compliance - what is the value proposition?

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Introduction
 Regulators have increased scrutiny on the financial services industry-fines
have been in the millions
 The financial services industry is focusing considerable time and expense on
capturing data to meet regulatory requirements
 Clients have often addressed these data challenges as one-off projects with
the objective to comply with a single regulation
 We recommend implementation of a data program focused on a flexible data
architecture that can be leveraged for revenue and competitive advantage

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Industry Data Challenges
 Regulatory burden – regulations will change and are unpredictable
 Disparate data – fragment and functional silos
– Data quality collection from different system results in different levels of quality
– Inconsistent and duplicate data across source systems
– SME domain knowledge is not also available
 Limited Master Data
– Lack full understanding of client relationships
– Unable to report financials by ultimate parent (top customers)
– Unable to report financials by customer
– Limited ability to identify cross-sell / up-sell opportunities
 Inefficiencies in risk management/reporting
 Inability to view complete risk exposure
 Structured and unstructured data makes normalization difficult
Challenges common to everybody in the industry

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Anti Money Laundering (AML) Data an
Example

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Anti Money Laundering (AML) Data an Example
Disparate Data - Inconsistent data
• Different payment formats in applications performing the same function such
as US ACH, SEPA, US Domestic (Fedwire), and International high value EFTs
result in the need to customize ETL programs that write data to AML
monitoring applications
• Global/Regional ATM and non-standard branch operation procedures result in
difficulty acquiring data required
• This has resulted in our client missing critical location, reference, and party
information
AML Data Programs
• Compliance programs focus on the specific data required by a tool to perform
monitoring and case management as opposed to the holistic use of data
The data requirements to comply with AML monitoring regulations is
extensive. Firms often focus on the data required by the AML tool.

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Solution Implementation
Document Data Requirements
• Document business requirements to capture the data required to ensure regulatory
compliance
• Utilize the current state of AML data and document the functional mapping requirements
to accurately place data in the AML monitoring record
Develop Roadmap to Accumulate Required Data
• Develop a roadmap to acquire critical data in order to ensure regulatory compliance
• Utilize the roadmap to design a data architecture focused regulatory compliance
and data reuse
The investment in AML compliance programs are significant. The program’s
focus should be a balance between meeting regulatory compliance and
developing a data architecture that allows for re-use of the data

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Value of a Roadmap
Roadmap
Outcomes
Project Execution Plans
with Steps & Milestones
Execution Options and
Recommendations
Project Budgets and
Resource Plans
Project Success Criteria
System Tool Strategy
Business
Goals
User
Needs
Regulatory/
Compliance
Project
Budget
Vendor
Management
Technology
Infrastructure
Roadmap
Drivers Roadmap
Goals
Retention of
Institutional Knowledge
Resource Allocation &
Utilization
Organizational Change
Project Prioritization
Project Rationalization
Program Management
Office & Governance
A roadmap will provide a firm information on the prioritization, business
impact, process and technology dependencies. The outcomes are a clear
project direction and project portfolios that are supported by business and
technology management.

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Design and Implementation Approach

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Data Strategy Building Blocks
Building Blocks To High Quality Enterprise Data
Governance &
Stewardship
Metrics
Architecture
Strategy
A sound Data Strategy is enabled by a clearly defined and effective Governance and Stewardship
structure, Metrics to monitor and measure data quality, and Conceptual Architecture diagrams that senior
business staff can leverage to make informed decisions. A clear mandate from senior executives to
execute on a strategy to achieve and maintain high quality enterprise data is a critical success factor.
Technology systems and
interfaces supporting
enterprise processes
Measurements to baseline data
quality and monitor improvement
Policies, procedures, and operating
model to manage and execute
Data strategy aligned to business strategy
including mandate for high quality data

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Governance and Stewardship Best Practices
Discover Data to
be Governed
•Collect data
across LOBs
•Discover
Business and
Tech sources
•Merge and
Validate
Standardize
Business
Context
•Data Dictionary
•Define Data
Elements
•Define Values
and Aliases
•Prioritize Data
Create a Logical
Data Model
•Conceptual
Model
•Illustrate
Business Data
•Illustrate
Relationships
Define Data
Rules
•Define
Relationships
•Define Constraint
Rules
•Define Rule
Exceptions
•Define Derivation
Rules
Establish Source
and Users
•Locate Data
Sources
•Prioritize &
Select Sources
•Locate Users
•Know how users
use data
Cleanse,
Maintain and
Measure
•One-time Data
cleansing
•Establish Quality
Maintenance
Practices
•Monitor Quality
for Adherence
Governance
Structure &
Preparation
Goals &
Principles
Roles and
Responsibilities
Process
Our approach focuses on the following key areas of our Data Governance and Stewardship Framework
EstablishGovernSteward
Metrics
Data Quality Stewardship
Metadata
Model Definition

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Next Generation Enterprise Data Architecture

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Leveraging Data for Revenue and
Competitive Advantage

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Beyond Compliance – What is the Value Proposition?
• To achieve compliance there is a minimum standard:
- Data governance, integrity; repeatable, automated processes
• To maintain compliance – pursuit must be strategic,
sustainable and incorporated into the culture
• The Cost of Compliance drives consideration to exit
business or product lines, increases total cost of ownership
and may restrict acquisition
• Turn Data into a Profit Center
– Engage the Business or Front-Office Stakeholders
– Enable the Call Center to support and grow the relationship
– Develop analytics and reports for broad usage

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Beyond Compliance – Cases and Opportunities
Customer Data: Master Customer Data required for KYC
Compliance: Party data allows compliance with AML regulations, suitability,
Cross-Border and FACTA
Opportunity: Improve cross-selling, total view of the relationship; increase
customer satisfaction. A single customer golden data source may reduce breaks.
Transaction Data: Client and Employee Surveillance
Compliance: Aggregated transaction data may identify inconsistent or unsuitable
activity for the customer segment. Identify activity out of the norm for an
employee’s job type
Opportunity: Bring the client advisor or marketing into the “case”. Improve
revenue and customer satisfaction by addressing a change in customer needs
Capital Planning: Projected Revenue & Risk for CCAR/DFAST
Compliance: Enterprise business, finance, risk data to ensure management
understanding and sufficient capital levels under stressed conditions
Opportunity: Passing Fed reporting requires high-quality unified reproducible
enterprise data. Leverage stress/scenario data for strategic decisions

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Key Considerations to Leverage Compliance Data
• Identify golden source as single truth – reduce
redundancy -> increase consistency
• Fix “dirty data” at the source. Define rules to detect and
prevent
• Globalize toward enterprise data architecture/dictionary
• Communicate data standards to increase adoption
• Shorten project lifecycle while driving owner-operated
reporting
• Push data visualization and self-reporting tools to users
• Track usage and data capitalization (ROI)