Big Data is revolutionizing Big Banking. Banking by its very nature is data driven - tracking deposits, withdrawals, loans, balances and interest has been a required mainstay since inception of the first banks where a pencil and a ledger book served as a business intelligence system. The amount of data has exploded as banking has grown into a wide array of new financial products, many of which are highly personalized, engaging directly with the consumer leveraging constantly evolving technology, service and communication methods. Traditional relational database management systems (RDBMS) have been a major player in managing transaction focused or structured data. Today, a transaction is one component of a much larger data picture that includes the who, how, when, where, using what, how long and many more attributes that are all potentially valuable information sources. Much of this non-transactional data is unstructured data. The expansion of unstructured data has accelerated the pace of data growth and new Big Data technologies have stepped up to help provide additional new tools to harness the power of that data.
Thinking beyond traditional balance sheet, transaction and account data, there are many new useful data points that can and are being used to offer new insights into areas including:
Security
Fraud
Economic Trends (Local through Global)
Key Productivity, Labor, Costs
Customer Acquisition
Customer Retention
Credit and Risk Analysis
Cash and Asset Management
A few examples of the application of Big Data to the banking industry include innovative projects highlighted by JP Morgan Chase & Co, The World Bank and The Center for Financial Services Innovation.
JP Morgan Chase & Co launched a new institute that uses Big Data to analyze hundreds of thousands of accounts to understand relationships between income and spending patterns (1).
“Researchers tracked the spending and income patterns of 100,000 randomly selected individuals from a sample of 2.5 million accounts at the bank over a 27-month period ended last December. Among its findings: While two in five individuals saw their income vary by at least 30% from one month to another, three out of five individuals saw their spending vary by at least 30%.
Other findings from the institute's first run through its data lab:
Monday is the top spending day of the week and Sunday is the lowest. Americans spend three times as much on a Monday as they do on Sunday.
Excluding Tax Day, the single biggest day for consumer spending last year fell on March 3, the Monday that followed the single highest earning day of the year, Feb. 28.
The top 12 days for spending at restaurants and bars fell between Feb. 14 and May 11”
The World Bank, while not a traditional bank, highlighted in a publication focused on “Big Data for Development”, several case studies including the work of Joshua Blumenstock and Dave Donaldson in analyzing Call Detail Records (CDRs) to track patterns of population movement to facilitate an understanding of the role of migrants in labor markets (2)(3). The following quote shines a light on why Big Data tools and techniques are important and more easily allow the analysis of non-traditional data sources.
“CDR data is a remarkably unwieldy and inconsistent dataset to work with, received in unstructured repositories of millions of individual files. The initial steps in the project from pre-processing the data to teasing out the relationships between the different datasets (e.g. wage and crop price data) are incredibly time consuming.”
The Center for Financial Services Innovation (CFSI), supported financially by Morgan Stanley and a grant from the Ford Foundation, published a comprehensive research paper focused on the impact of Big Data on financially underserved consumers, which highlighted four Big Data trends (4).
“Trend #1: Getting Granular Through Getting Huge…
The impersonal becomes personal when overlaying data from multiple sources yields precise, tailored consumer profiles. The data sets may be vast, but successful providers can use them to accurately identify individual needs, better know their customers, and help customers better know their own habits and financial patterns. Creating best-fit products that are responsive to the changing circumstances of each consumer increases efficiencies and cost savings for providers and increases access, value, and quality for consumers.
Trend #2: Connecting the Dots…
Financial providers can learn a lot from data generated through the daily activities of consumers, but often consumers are not aware they are generating data that impacts analytical models. Some innovative companies are going beyond legally required minimums of disclosure by transparently conveying the types of data sources they use or explaining to consumers how their behavior can drive profile improvements that lead to better rates and offers. Well-informed consumers who are empowered to report erroneous data or shift behaviors to improve their financial standing can enhance data quality and reduce risk for providers while securing better outcomes for themselves.
Trend #3: Opening the Books…
Integrating highly reliable personal data leads to lower risk for Big Data-driven providers, but capturing that data often means venturing into private territory. One effective strategy is to invite consumers to opt in and voluntarily share more personal information and financial data in exchange for more attractive offers and lower rates. Products that allow consumers to control the balance of trade-offs between greater privacy and greater value can allow both customers and providers to reap greater benefits.
Trend #4: Hitting the Target…
Big Data applications become smarter and more efficient as they secure industry partners and access a richer variety of data sets. But to truly enhance data accuracy and impact end consumers, Big Data companies must not only increase their network footprint, but also scale up their industry adoption rates. Successful companies are finding routes to wide market exposure, often by crafting compelling tools for financial services providers that have a broad market reach and high returns on investment. Others are accessing strategic customer targets through partnerships with existing brands, or through well-travelled rails in other verticals, such as the mobile phone industry.”
We are in the midst of significant change in how banks attract, interact, retain and serve their customers. Moving well beyond a traditional transactional data model to a new era driven by new technology and many new data sources. Business Intelligence strategies will continue to evolve often leveraging both their historic roots, but also new Big Data technologies providing new tools to harness the power of that data.
References and Citations:
JP Morgan Chase & Co Website, “How a Bank Will Use 'Big Data' To Study the U.S. Economy”, Nick Timiaraos, https://www.jpmorganchase.com/corporate/Corporate-Responsibility/st-banks-study-economy.htm, Accessed June 4, 2016
World Bank Website, “Big Data in Action”, World Bank Case Study, Andrea Coppola, Oscar Calvo-Gonzalez, Elizabeth Sabet, Natalia Arjomand, Ryan Siegel, http://live.worldbank.org/sites/default/files/Big Data for Development Report_final version.pdf, Accessed June 4, 2016
“How Do Labor Markets Equilibrate? Using Mobile Phone Records to Estimate the Effect of Local Labor Demand Shocks on Internal Migration and Local Wages”, Blumenstock, J. & Donaldson, D., (2013)
Morgan Stanley Website, “Big Data, Big Potential: Harnessing Data Technology for the Underserved Market”, Eva Wolkowitz and Sarah, http://www.morganstanley.com/sustainableinvesting/pdf/Big_Data_Big_Potential.pdf, Dated 2015
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