TRANSACTION
DATA
ENRICHMENT
AS THE FIRST
STEP ON THE
BIG DATA
JOURNEY
A key part of its industry-leading platform for digital financial services,
the new Yodlee® TransactionDataEnrichment solution enables financial
institutions and non-bank digital service providers to capitalize on
big data. To crack the code on the marketing potential of mass
personalization, the ideal starting point is to enhance data contained
within a bank’s own core systems – transaction data from banking,
credit and debit card transactions. By making internal transaction
data ready for consumption by big data toolkits, banks can develop
powerful new automated services. In addition, transaction data
enrichment also improves statement readability, which benefits the
customer and the bank alike.
The personalization code
In online and mobile ecosystems, service providers from search
engines to social networks have successfully convinced their
customers to trade their personal information for personalized
services. The power of personalization has created immense
opportunities for companies offering secure, trusted access to
comprehensive services.
Unlike the major technology companies, most financial institutions
(FIs) haven’t yet cracked the personalization code – despite having
access to a valuable collection of transaction data from banking,
credit and debit card transactions.
produced by sourcemedia marketing solutions group • American Banker
| SPONSORED BY YODLEE
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90 percent of
respondents agreed that
skillful use of big data will
define future winners in
financial services2.
To date, a significant number of banks have begun to explore
and deploy projects using “big data” – a field of computing that
yields analytical insights using distributed computing and parallel
processing. A 2013 survey indicated that 38 percent of banks had
gone through with a live implementation of a big data project;
25 percent were experimenting; and 37 percent were still in the
exploratory stage.1 Furthermore, 90 percent of respondents agreed
that skillful use of big data will define future winners in financial
services2 – a strong vote of confidence for the potential of the
technology.
Yet as of two years ago, most bankers had little to no visibility of their
customers’ digital lives outside of their banking relationship. In a 2013
survey, only 29 percent of respondents indicated their institutions
have mature capabilities in monitoring wallet share; only 32 percent
said their banks monitor customers’ social media activity; and 46
percent said their banks monitor any external data about customers.3
It’s a curious paradox – almost everyone in banking agrees on the
importance of big data and many organizations have committed
significant IT budgets to the technology; yet, only a small percentage
of FIs have the ability to calculate “share of wallet” – which, by
measuring usage of an FI’s financial products as a percentage of a
customer’s total spending in a given category, is one of the most
important metrics in the banking business.
FIs’ apparent priorities seem counterintuitive, until one compares
the characteristics of internal and external sources of data. External
sources of data, such as online and social media activity, are readily
accessed using powerful Application Programming Interfaces (APIs)
through a rich ecosystem of third-party providers. By contrast, internal
sources of data are frequently locked away in proprietary formats
within organizational silos.
The tools of data science rely upon clean, standardized, and enriched
sources of data. It’s relatively simple to apply big data algorithms
on datasets that are prepped for analysis. But when the underlying
data hasn’t been appropriately enhanced in advance, the analytical
possibilities are hampered from the outset.
“Too many silos”
When asked about their organization’s biggest impediment to using
big data for effective decision-making, the top result – for 63 percent
of respondents – was: “Too many silos.”4 At financial institutions,
1. Microsoft Enterprise Team. “How Big Is Big Data? Big Data Usage and Attitudes among North American Financial Services Firms.” December 12, 2014. Accessed February 3, 2015. http://
www.microsoft.com/enterprise/industry/financial-services/banking-and-capital-markets/articles/how-big-is-big-data.aspx.
2. Microsoft Enterprise Team. “How Big Is Big Data? Big Data Usage and Attitudes among North American Financial Services Firms.” December 12, 2014. Accessed February 3, 2015. http://
www.microsoft.com/enterprise/industry/financial-services/banking-and-capital-markets/articles/how-big-is-big-data.aspx.
3. Groenfeldt, Tom. “Banks Betting Big on Big Data and Real-Time Customer Insight.” September 1, 2013. Accessed February 3, 2015. http://www.sap.com/bin/sapcom/hu_hu/
downloadasset.2013-09-sep-10-16.Banks Betting Big on Big Data and Real-Time Customer Insight (Bloomberg 2013)-pdf.html.
4. “The Deciding Factor: Big Data & Decision Making | Resource.” Capgemini Worldwide. June 4, 2012. Accessed February 3, 2015. http://www.capgemini.com/resources/thedeciding-factor-big-data-decision-making.
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Banks will only be
able to apply big
data analytics to
customer data after the
underlying transactions
been cleaned up,
pre-processed, and
prepared for real-time
analysis.
customer data typically resides in silos within each line of business, as
well as within horizontal systems built for a specific purpose such as
CRM, portfolio management, or loan servicing. Although these mature
legacy systems adequately support automated routing for day-to-day
operations, they were not built for the new possibilities enabled by big
data, such as statistical analysis or real-time, end-user interfaces.
The essential problem with silos is that they store data only for those
narrowly-defined purposes envisioned when the silo was built. When
a bank receives credit card data from merchant processors, the
data travels, without significant modification, directly into automated
systems that handle balances, billing, and statements. Over time,
banks have become very efficient at generating statements. As a
result, the credit card silos have been more or less left alone, as have
the silos for checking and savings accounts, payments, lending, and
other core systems.
This approach of enabling silos won’t work effectively for big data
analytics, which requires access to enriched transaction data. To
illustrate, search engines have the ability to respond to queries in a
fraction of a second only because they’ve searched the entire public
Internet in advance, transforming a world of non-standardized web
pages into a format easily searchable using parallel-processing
algorithms. Similarly, FIs will only be able to apply big data analytics
to customer data after the underlying transactions have
been cleaned, pre-processed, and prepared for real-time analysis.
Cleaning customer data makes transactions easy to understand for
both databases as well as customers. Indeed, there are significant
hidden costs to retaining obscure codes in transaction lines. Consider
what happens when a person doesn’t recognize a line item on a
statement. In one case, the customer may call the customer service
center to inquire about the transaction, driving up costs for the
financial institution. Or, the customer may skim over a cryptic charge
that turns out to be fraudulent and subject to chargebacks. In either
case, those cryptic charges lead to costly outcomes for the bank.
Unlocking the value of enhanced bank data
Clean data represents an essential first step toward creating new,
intelligent, and proactive solutions, built using big data analytics.
Merchant transactions are the main driver of customer information
for banks, which is why transaction data enhancement is central to
big data initiatives in the industry. Enhancements to transaction data
enrichment include: merchant identification with better descriptions,
categorization by merchant type, and accurate geolocation using
industry coding standards.
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Virtually every source
of customer data
at a bank has the
potential for information
enhancement.
By applying these enhancements to merchant transactions:
• Bank employees will have a better understanding of consumer purchase behavior, enabling them to provide targeted recommendations and advice based on trends and patterns identified within transaction data.
• Automated solutions will be able to suggest relevant products and services, and make targeted promotional offers at the point-of-
sale – whether offline, online, or triggered by geographic proximity to selected merchants through a mobile device.
• Risk management teams and consumers will be able to detect fraud faster, and more accurately, while achieving resolution rates that reduce cost and risk for the bank.
• Privacy policies will evolve in a positive manner based on an improved mutual understanding between the bank and its customers regarding the content of transaction data.
• Consumers will be able to understand their statements without having to decipher obscure codes, leading to lower service costs at the call center, higher rates of fraud detection, reduced customer chargebacks, and a more satisfying user experience.
• Call center agents will be able to focus on more meaningful interactions with customers.
The enhancement of merchant transactions represents a critical first
step in an FI’s big data journey. Virtually every source of customer
data at a bank has the potential for information enhancement. The
goal of the FIs should be to establish a data repository that contains
enhanced, readable data not just for transactions, but also for
customer interactions, intentions to purchase, and other valuable
insights that can be gleaned from external sources of data.
Such a repository should combine, via standardized APIs, both
internal bank data and external data from social media and online
partners. Through the growth of a repository of cleansed customer
data, FIs will find applications based on data science that generates
significant market value and competitive advantage.
It’s a long journey, which begins within FIs’ legacy transaction
databases.
Yodlee TransactionDataEnrichment
To support banks throughout the big data journey, Yodlee®, the
leading platform for digital financial services, has introduced
TransactionDataEnrichment, a new service that enhances banking,
credit card and debit card data with simple descriptions, merchant
identification, merchant categorization, and geolocation coding.
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In an industry first, Yodlee
TransactionDataEnrichment
automatically sorts
individual transactions into
clear, readable categories,
reducing the stress of
tracking spending for
consumers and financial
institutions alike.
In an industry first, Yodlee TransactionDataEnrichment automatically
sorts individual transactions into clear, readable categories, reducing
the stress of tracking spending for consumers and financial institutions.
Here’s how it works: The solution automatically scans raw, unsorted
credit card and transaction data – the strings of hard-to-read
abbreviations and numbers on a statement – and uses powerful
analytical algorithms to identify each transaction’s recipient, merchant
type, and geographical location. The platform then replaces the
confusing records with clean, sorted versions of the data, which can
be organized by category to allow consumers to determine how much
they are spending at different merchants, in different areas, or at
different types of stores. The breakthrough technology for merchant
identification performs categorization with a regional context, ensuring
the highest levels of suitability for the enriched data.
Moreover, with access to enriched data, Yodlee’s FI and non-bank
partners have the power to analyze consumer spending patterns and
trends using the latest big data toolkits. This opens the door to a
new world of real-time banking applications that cater to customers’
specific financial needs – whether it be alerts triggered by spending in
specific categories, contextual mobile offers based on geolocation, or
whatever else your bank’s product development team can imagine.
Yodlee TransactionDataEnrichment operates as an integrated
component of the Yodlee platform for digital financial services, which
also includes market-leading capabilities in aggregation and API
access to core systems within the financial institution and to external
data sources. The offering can be used in conjunction with existing
digital services, and it was designed for maximum ease of deployment.
By adding Yodlee TransactionDataEnrichment, financial institutions or
non-bank service providers will be able to provide more relevant and
meaningful interactions with their customers over time – delivering
improved engagement and satisfaction for customers and unrivaled
capabilities for Yodlee clients.
About Yodlee
Yodlee (NASDAQ: YDLE) is a leading technology and applications platform powering dynamic, cloud-based
innovation for digital financial services. More than 750 companies in 16 countries, including 9 of the 15
largest U.S. banks and hundreds of Internet services companies, subscribe to the Yodlee platform to power
personalized financial apps and services for millions of consumers. Yodlee solutions help transform the speed
and delivery of financial innovation, improve digital customer experiences, and deepen customer engagement.
Yodlee is headquartered in Redwood City, CA with global offices in London and Bangalore.
For more information, visit www.yodlee.com.
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