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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

Esta

blis

h

Govern

S

tew

ard

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)