TRANSACTION DATA ENRICHMENT AS THE FIRST STEP ON THE BIG DATA JOURNEY

Transcription

1 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 1

2 90 percent of respondents agreed that skillful use of big data will define future winners in financial services 2. 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 services 2 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, Accessed February 3, Microsoft Enterprise Team. How Big Is Big Data? Big Data Usage and Attitudes among North American Financial Services Firms. December 12, Accessed February 3, Groenfeldt, Tom. Banks Betting Big on Big Data and Real-Time Customer Insight. September 1, Accessed February 3, downloadasset sep 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, Accessed February 3,

3 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. 3

4 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-ofsale 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. 4

5 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 5