Table of Contents
Why do Banks need a Single View of Customer? .................................................4
How do Banks typically approach establishing a Single View of Customer? .............7
What is a Master Data Management (MDM) Solution? ..........................................8
Which MDM Architecture Style is most suited for a Bank?.................................... 10
How can the key challenges in implementing MDM be addressed? ....................... 12
What makes Banking MDM implementations uniquely challenging? ...................... 15
Conclusion .................................................................................................... 16
In the past, the management of a bank’s customer (person/client/counterparty) data
was largely at a tactical level driven by the need to comply with KYC, MiFID, AML,
Basel II and a host of other regulations. Retail and Commercial banks today have
realized that they need to get much more strategic in their use and management of
customer data, if they are going to be able to continue to drive growth, improve
customer experience, better manage operational risk and exposure, as well as drive
operational efficiencies. Although most banks and financial institutions are already
moving from a product-centric view to a customer-centric one, they are challenged
with incomplete, incorrect, and fragmented customer data. Banks undergoing
mergers and acquisitions have experienced additional fragmentation of customer
data, as well as increased complexity in their customer platforms that cross lines of
business, corporate functions, and geographies.
Banks with a fragmented view of customer information face the following challenges:
1. Lower Rate of Revenue Growth – The lack of a 360 view of the customer
across channels, across products, across regions and across lines of business
results in a barrier to realizing value from customer-centric cross-sell and up-
2. Higher Operational Risk and Exposure – The difficulty in managing the
security and privacy of customer information results in increased reputational
risk; an inability to aggregate loan and credit exposure for a customer; as
well as inefficient front-line response to service customers and manage risk
3. Higher Operating Costs – Customer information applications are typically the
most expensive applications in the bank. Duplication of functionality and data
increases operating costs.
4. Impacts to Customer Experience - Through inconsistent channel support, the
inconsistent application of business rules and incomplete data synchronization,
customer experience is getting negatively impacted.
For most banks, successfully achieving a Single View of Customer is not a simple
task and will require both strategic and tactical approaches. Tactical remediation
approaches to improve customer data and applications can help to deliver value in
the short term, but a structured, well conceived solution strategy (people, process,
technology), combined with a solid business case that executives will support and
fund is required to deliver sustained long term value.
The purpose of this paper is to present these approaches.
Why do Banks need a Single View of Customer?
Most Banks have business growth strategies that require an integrated view of the
customer to realize. For example, today most banks, irrespective of their market
segments are focused on creating customer intimacy and moving towards
relationship based banking. However, at the same time, increased M&A activities
further contribute to the challenges of achieving this by increasing the fragmentation
of customer data. It is not uncommon for banks to have their customer information
in 10’s of databases and 100’s of versions of 1000’s of spreadsheets in each line of
business. Clearly an approach is needed to manage this.
The three major business drivers for a Single View of Customer are:
1. Revenue Growth — Examples include increasing cross-sell and up-sell rates,
and increasing customer retention through better customer experience.
2. Cost Reduction — Examples include reducing reconciliation requirements,
improving the speed and effectiveness of processes such as order to cash,
reducing the need to maintain multiple redundant customer data stores, etc.
3. Risk Management — Examples include compliance with cross-industry or
industry-specific compliance and risk management regulations, such as Know
Your Customer and Basel II.
1. Revenue Growth and Customer Experience
Banks have long recognized that customer-centric solutions help to achieve cross-
functional business imperatives that are aimed at bolstering customer profitability.
Banks’ reliance on electronic client information has generally faced challenges with
scalability and integration between different line of business applications such as
retail, wealth, credit card, insurance and private banking.
Achieving a holistic view of customer information generally takes one of the following
• Single View of Customer - Create “golden” customer records by reconciling
names, addresses, emails, phone numbers, and other data from disparate
sources for a single view of a customer.
• 360-degree customer view - Expanding the Single View of Customer to
include the customer’s products across lines of business (checking, mortgage,
IRA, auto loan, etc.)
• Extended customer view - Expanding the 360-degree customer view to reflect
the bank customer’s network of people, business, and product relationships
can help customer teams pursue long-term, multi-generational value and
support wealth management growth strategies.
Banks are turning to technology solutions, such as Master Data Management (MDM)
to help achieve a single view of customer. One of the largest banks in Europe
integrated its centralized customer information file (CIF) with a transaction MDM
customer hub to support the real time approval of wealth management and credit
product sales applications. The implementation not only created additional sales
opportunities by providing information from all lines of business for up selling and
cross selling their products, but also helped with choosing and recommending the
right product for their customer, resulting in higher customer satisfaction. This
solution also helped reduce operational costs for application processing, and reduced
credit risk by providing a unified view of the customer’s relationship with the bank.
Consistent and improved customer experience has been one of the greatest
challenges for some banks due to increased M&A activities. Mergers and acquisition
activities have increased substantially in the last decade, especially during the global
recession of 2008.
One of the largest banks in North America had over 90 acquisitions resulting in the
increase of their assets to 100’s of billions of dollars. The acquired banks had
different business processes, data governance models, and technologies, making it
very difficult to produce an integrated customer view. To address these challenges
the bank chose a MDM solution to help reconcile customer master data across the
acquired entities, thereby realizing a single integrated view of customer.
2. Cost Reduction and Operational Efficiency
Banks are continually facing challenges with regards to reducing operational costs,
and costs associated with addressing regulatory requirements. Achieving a Single
View of Customer can help contain these costs. It is not uncommon for a single
business unit to use 10’s of applications, and 100’s of spreadsheets to: reconcile
regulatory compliance requirements, process credit applications, for internal or
external reporting and to manage credit risk data. Enabling a single view of the
customer can reduce operational costs for application processing.
A leading Canadian bank had major challenges in the approval of new applications in
their wealth management group even if the applicant was an existing customer in
good standing with the bank. The bank had more than 40 dispersed applications
across its various business units and numerous others from where customer
information had to be reconciled before an application could be approved and the
client on-boarded. The bank decided that a MDM strategy was critical to improving
operational efficiency in this area by creating a single view of customer.
Some of the areas in which costs savings can be achieved through a Single View of
Customer are –
• Reduced Failures or Delays for Orders, Trades, Confirmations, Settlements
and Payments through elimination of incorrect account – client linkages
• Increased Straight Through Processing (STP) levels and Program (algorithmic)
Trading – this is achieved in combination with an instrument / security
(product) master, counterparty master
• Timely and Accurate Operations and Financial Reporting by reducing
reconciliation effort between financial ledgers and client master
• Elimination of incorrect or duplicate mailings
3. Risk Management and Regulatory Compliance
Credit Risk Management and Regulatory Compliance have been one of the earliest
drivers for achieving a Single View of Customer for banks.
A leading European Bank with global operations implemented MDM in record time to
build a central credit risk reference data repository from a set of diverse databases
and excel spreadsheets. The bank also used MDM to secure their Data Governance
processes across the organization, resulting in better compliance to both SOX and
BASEL II requirements. Compliance was achieved through MDM by its ability to
enforce rights and security rules, data governance rules, create secure audit trails
from multiple sources, and the ability to manage and control multiple versions of
How do Banks typically approach establishing a Single View of
• Ignore the Problem – Yes, surprisingly enough, due to the complexity and
large volume of data involved in client data remediation, some firms take the
approach of side-swiping the entire issue. The cost of not addressing this
problem is huge but often tactical band-aid solutions are used to provide
• Employ a Master Data Management (MDM) solution for customer data
integration – This method is typically the optimum solution and is described in
the following sections. Options include either a custom built solution or a
packaged MDM solution. Typical issues with a custom built application include
higher implementation and maintenance costs, hard to code complex
probabilistic and deterministic matching, building the relationships and
hierarchies, limitation in business rules implementation, and insufficient data
governance user interfaces and workflows. Currently, there are many robust
and cost effective MDM platforms on the market that can provide a better
alternative to a custom built solution. A custom built MDM solution should
only be pursued if the business requirements of the bank are validated to be
so unique that none of the MDM vendors support them.
What is a Master Data Management (MDM) Solution?
Master Data Management (MDM) spans all organizational business processes and
application systems, enabling the ability to create, store, maintain, exchange and
synchronize a consistent, accurate and timely “system of record” for core business
entities. When the business entity being managed is Customer, it is also referred to
as Customer Data Integration (CDI).
A MDM solution should have the following core capabilities:
• Identify a customer across multiple sources, by matching various identifying
attributes of a customer such as name, address, phone, email, SSN, etc.
• Probabilistic (fuzzy) matching – consider changes in attribute values due to
spelling errors (Main St. – Mai St.), phonetic differences (Tami - Tammy),
nicknames (Robert – Bob), etc.
• Deterministic matching – match based on pre-defined rules that are based on
data profiling and source system specific information.
• Assign suspect customer record matches to a data steward for manual review.
• Construct a golden view of the customer by assembling attributes from the
matched source records according to pre-defined rules such as trusted source,
most current attribute, etc. Publish golden view changes to consuming
• Identify, construct and manage customer hierarchies and relationships, with
the help of third party reference data.
MDM Solution Components for Banking
1. MDM or CDI Hub - Data standardization algorithms, Core matching
(probabilistic algorithms / deterministic rules), Golden copy rules assignment,
Hub data model and reporting structures.
2. Task management system for manually linking and unlinking customer
records from same/multiple systems.
3. Identity management User Interface (UI) for displaying golden view and
detailed views. Hierarchy & Relationship management UI for viewing,
comparing and remediating multiple hierarchies and relationships.
4. Batch and Real-Time Interfaces to receive source data and distribute
mastered data – ETL or EAI, Adaptors for various messaging standards, SOA
Compliancy, API’s, Web Services, etc.
5. Security and Visibility Components (the chosen MDM system must be able to
integrate well with the firm’s existing security models and policies)
A MDM solution may not include the following tools and capabilities which are
however key to a successful MDM implementation, and thus they may need to be
• Data Quality – Profiling, Cleansing and Transformation (ETL)
• Data Enrichment – e.g. Address Validation, Dun & Bradstreet, Bloomberg, etc
• Rules Engine
Many leading vendors of MDM solutions offer some of these as separate products or
provide adaptors to integrate with other vendors offering these capabilities. The
integration effort required for these tools to interact with the MDM solution needs to
be carefully evaluated before a sourcing decision is made.
Which MDM Architecture Style is most suited for a Bank?
There are different architectural patterns for implementing a MDM solution and the
appropriate style for a bank depends on its business objectives and its existing
technology maturity, which is required to support the solution. In most cases, the
Coexistence (Hybrid) style is appropriate for large banks that are embarking on the
MDM journey. As the bank gains more MDM maturity, it can transition to a
Centralized (Transaction) style MDM solution.
# MDM Architecture Style Adoption by Banks
1 Consolidation Style - This style extracts
a copy of the master data from the MDM
Hub, and stores it in a data warehouse for
reporting and analysis purposes. This style
does not improve the integrity of master
data in the operational systems.
Banks that adopt this style are
usually ones that want to understand
the benefits of MDM in an analytical
environment before they make the
investment to integrate it with their
2 Registry Style – This style maintains only
cross-referenced links to multiple data
sources by means of keys or pointers in
the hub. All other data attributes should be
retrieved from the relevant source systems
using these keys. It can be deployed fairly
quickly, but transitioning into more mature
styles would be difficult due to significant
Most banks see little value in
adopting this architecture style, but
sometimes adopt them for a quick
proof of concept.
3 Coexistence (Hybrid) Style – This style
stores a physical copy of the customer
master data in the MDM hub, and
synchronizes it with multiple operational
systems at varying latencies. It is easier to
transition from this style to the centralized
style, if required.
A large number of banks adopt this
architecture style since it
accommodates different operational
systems that have variances in their
ability to consume customer master
4 Centralized (Transaction) Style – This
style federates up-to-date customer
transaction data (Create, Read, Update,
and Delete activities) in real time from
multiple channels, and unifies it with the
most reliable master reference and
relationship data within the hub. This style
delivers the most timely, unified views of
the customer to supporting applications.
This is a very invasive approach
given that various operational
systems in a bank have to consume
customer master data directly from
the same hub. For that reason, it is
also hard to implement and is
attempted only by bank’s that have
reached a high maturity level in MDM
and data governance.
How can the key challenges in implementing MDM be
From a strategic perspective the following are some of the key challenges banks face
in implementing MDM, and recommended approaches to addressing those challenges:
Challenge Recommended Approach
Executive Ownership and Stakeholder Buy-in
Participating is not the same as buying in - Key
stakeholder groups need to be determined and senior
executive leadership need to be active, engaged
participants. Successful large scale programs have a
clear Executive Champion
• Key Steering and Working Committee
participants must set aside the appropriate
amount of time and be actively engaged in
• Development of a steering committee with
an identified chair that is cross functional
• Hold Steering committee responsible and
accountable for key decisions throughout
the project (Target future state, roadmap
initiatives, and business case estimates)
Fact-Based Business Case
All spreadsheets are not equal - Many business
cases are based on hypothetical costs and benefits.
Costs are typically estimated at a deeper level of detail
than benefits. Frequently, business cases are build on
fact based components validated with real world
• Leverage multiple sources for calculation of
benefits – such as the results achieved by
peer organizations, case studies and
external benchmarks from other Financial
• Validation and sign off from working teams
throughout the process
Practical Implementation Roadmap
Big bang does not typically deliver explosive
results - The complexity of large scale Customer
Information Implementations often results in multi-year
big bang implementations before business value is
delivered. These result in organizations losing
momentum and often funding for the initiatives
• Pragmatic and staged roadmap approach
that identifies quick wins an self funded
• Continued participation of the steering
committee through business case and
Technology by itself will not deliver the results -
Programs that focus exclusively on delivering
technology do not achieve sustainable business results.
Focusing on addressing the required organizational
elements (new roles, responsibilities, interaction
models) and business process changes that are required
help ensure that the technology will enable business
• Dedicated stream of work focused on
process will identify necessary changes that
have to align with the technology
• Cross functional working teams will ensure
that all relevant processes are addressed
Proactive Data Governance and Management
Plan and Pray is not an option - Trust but Verify -
Many programs make dramatically incorrect
assumptions around the current quality of existing
customer information. Planning for data profiling,
detailed data quality assessments in the early stages of
• Implementation plan should include initial
early data assessment of core data stores
• Extensive data profiling and data quality
• Proof of concept for data migration efforts
the implementation roadmap helps avoid significant
data conversion overruns. Many programs leave the
assessment of data quality and subsequent conversion
too late in the lifecycle
From a business case perspective, realistic benefit calculation is critical to its validity
and in achieving executive buy-in. The following are some sample benefits that have
been achieved by Banks in recent years and can be used in building the business
Client Solution Benefit Identified
Global Bank Risk-scoring improvements - These enable a
relationship approach to credit management,
thus enabling a customer centric view across
businesses and products (restrict good accounts
if customer is delinquent on another account or
other behavioral activities such as excessive cash
$120 million (EBIT) over 5
years in hard dollar benefits
identified to manage customer
Commercial Lending platforms
(reduced credit losses)
Global Bank Speed up cycle time - By capturing and scoring
behavior data nearer to the event, highly
targeted offers can be made – across any
channel – in a matter of days rather than months
$26 million (EBIT) / year
from incremental lift in overall
response rate and improved
overall retention rate
Customer centricity - Implemented integrated
enterprise-wide data model, information hub, BI
tools, contact and client management systems,
and messaging broker to enable enterprise-wide
Computing and operations
efficiencies funded 64% of
total incremental investments
with a 285% ROI
Global Bank Reduced IT costs - Consolidation of customer
information into a single repository meant that
certain O&T enhancements must only be made
into a single system.
This efficiency will enable O&T to deliver one
incremental project per release
$14 million (EBIT) over 3
years in reduced project costs
resulting from a central data
Global Bank Cross-sell and reduced operational costs -
Use customer-centric data to increase online
market share and move customers to a more
electronic environment (i.e. payments,
$38 million (EBIT) over 3
years in hard dollar benefits.
New business and lower
operational costs associated
with migrating customer to
When implementing a MDM solution, it is important to keep it first and foremost a
business focused project. This is because the majority of benefits will come from:
cross-sell / up-sell revenue lift, significantly improved risk management capability,
and cost savings through operational efficiencies.
Also, MDM program governance that extends beyond Data Governance is critical
because of the enterprise-wide implications and operational governance that will be
required to realize the full benefits of this initiative. Without it, the capabilities built
may not be converted to business results due to lack of process re-engineering and
integration into customer facing activities.
Now, from a tactical perspective, the following are some of the roots causes of data
quality problems with customer data, which can affect the success of an MDM
program, which can be addressed through application controls, improved customer
data governance processes, as well as staff training and education:
• Lack of integrated systems and the complexity required to on-board a client:
There is a lack of ability to be able to look up an existing client across
channels, and there is a tendency to enter dummy data when client vetting
and Know Your Customer (KYC) activities are too complex or take too long;
and too often the dummy data is not corrected, when the right data is
captured later. Incorrect data then propagates downstream to the transaction
processing environment. This negatively impacts control functions in the bank
such as credit, finance, compliance and tax. Application controls with
systematic edits and validation at the point of entry can help to address this.
• Indifference towards “getting data right the first time” in the front office, at
the time of data capture which stems from an apathetic attitude by the front
office that the middle / back office would capture the error and remediate it.
To address this issue, a global bank even took the approach of tying a part of
their front line staff bonuses to data quality performance.
• Lack of defined data ownership and stewardship – Many data attributes used
by more than one function suffer from a lack of ownership and stewardship.
There might be inherent variances in the way the data attribute and its usage
is defined. For e.g. the credit risk function might be using and changing
certain client attributes based on their definitions, without comprehending the
effects it might have on financial reporting. Improved data governance
processes help to address this.
• Unavailability of an external hierarchy which can be used as a remediation
reference – Client data quality gets eroded all the time with new data coming
in from multiple sources, which might have new or changed processes for
data capture. The typical way to address this is to compare the bank’s
internal customer data with external sources such as the Postal Service, Dun
& Bradstreet, Acxiom, Bloomberg, etc.
• Lack of data quality performance metrics – Without defining data quality
performance metrics; it is hard to see if data remediation efforts are paying
off. Key Performance Indicator (KPI) Dashboards with drill-down metrics are a
useful tool in monitoring and tracking of data quality trends and performance
What makes Banking MDM implementations uniquely
There are several unique challenges in implementing MDM at banks, and it would be
prudent for banks to recognize them before they embark on their MDM journey.
These challenges need to be considered not only when selecting the MDM solution,
but also in adopting the methodology used for the implementation.
• Dealing with Big Data - Most banks have a large customer base and
experience frequent changes to this data. These changes could be core
customer data (e.g. name, address, phone) or secondary customer data (e.g.
products owned, interactions). An increasing number of new channels for
banking and interaction management (e.g. mobile devices, social networking
sites) have added to this data explosion. MDM solutions have to scale
effectively to deal with these large volumes of data and real-time
• Rapid M&A Activity - Due to the highly dynamic nature of corporate actions
in this industry, MDM solutions need to be highly flexible to accommodate
various new sources and consumers of customer data, as well as the
associated challenges already mentioned.
• Complex Customer Relationships and Hierarchies - In banking, it is just
not enough to identify who your customers are and what products they own,
but also to understand the relationships they have with other customers from
the perspective of a household, guarantor, etc. When the bank’s customers
are institutions as opposed to individuals, it poses the challenge of
maintaining corporate hierarchies. This is because understanding the ultimate
parent entity and all its subsidiaries across the globe are critical to accurately
assessing risk as well estimating the value of a customer.
• Need for Customer-Product Linkage - In addition to core customer data,
most banks find it desirable to maintain high level product data linked to its
customer data within the MDM Hub, as opposed to creating that linkage
within downstream analytical data stores such as a data warehouse. This
linkage is usually required for accessing the 360 degree profile view of the
customer, including what products are owned and what the customer’s
contact preferences are. Maintaining it within the MDM Hub provides an
instant and one-stop access for applications interacting directly with the Hub
(e.g. contact center and marketing applications). To retrieve more product-
centric detail data, they can use smart navigation techniques to drill into the
• Need for High-Accuracy Solutions - When it comes to customer matching
for supporting a banking platform, the level of accuracy demanded from an
MDM solution’s core matching process should be higher than that of less
demanding applications such as for a marketing campaign.
Banks that have recognized the need to establish a Single View of the Customer to
drive revenue growth and manage risk are turning towards MDM as an enabling
technology. This is becoming more important with the increased competitive and
regulatory environment. For banks to succeed, they need to ensure that they have a
well defined MDM strategy and practical implementation plan which is supported by a
clear business case endorsed by key executive sponsors.