Value Creation through Big Data: Banking Perspective

Big data is a game changing opportunity for financial services companies. McKinsey Global Institute’s June 2011 report, Big data: The next frontier for innovation, competition, and productivity, estimated that US banks and capital markets firms together had more than an exabyte of stored data in 2009. That much data makes a unified data management system – one with high-performance analytics to grow revenue, reduce risks, prevent fraud and meet regulations – imperative.

While tumult in the financial markets shows no signs of slowing, as Europe’s debt crisis unfolds and the US recovery remains fragile. For retail banks, industry analysts estimate that the cost of recent regulations, combined with continued low interest rates, could reduce retail bank revenues by 30 to 50 percent, according to a December 2011 BAI Executive Report. Meanwhile, the technology needed to manage risk and regulation continues to chew up 15 percent of IT investments. Financial services companies will struggle in 2012 to find the optimal channel mix to deliver value to clients.

In the event of such a situation looming around financial institutions, can these huge volumes of unstructured data pouring in from regular sources as well from social networking channels like Facebook and Twitter serve a saviour’s task to unfold new vistas of ensuring profitability, productivity and better positioning for financial institutions.

Now the question remains to be asked. Will big data transform the future of financial marketing? Or will banks and credit unions get crushed by a crippling tidal wave of information?

Understanding Big Data

With all the devices available today to collect data such as RFID readers, microphones, cameras, sensors and so on, we are seeing an explosion in data available worldwide.

Big Data is term used to describe large collections of data, also called data sets that may be unstructured and grow so large and quickly that it is difficult to manage with regular databases or statistical tools.

While there are several definitions of big data, the most common reference focuses on data that reflects added Volume (terabytes, records, transactions), additional Variety(internal, external, behavioral, social), and increased Velocity (near- or real-time assimilation).

Understanding the “Three Vs of Big Data” is important, since understanding the value of data being created today allows a bank to understand their businesses, customers, channels and the marketplace dynamics, including new sales and service opportunities.

“Analytics, customer-centricity, and multichannel technologies are major trends across the market,” says Bart Narter, SVP of the analyst firm Celent, and co-author of a recent report, Core Banking Solutions for Midsize Banks: A Global Perspective.

“Banks increasingly want a core system that utilizes data analytics in order to provide a more complete view of the customer, which then allows for better customer-bank communication,” Narter writes.

In the financial services industry, while there is a great deal of discussion around big data, many banks are just beginning to consolidate and utilize many of the internal data elements at their disposal, such as debit and credit transactions, purchase histories, channel usage, communication preferences, loyalty behavior, etc.

In the context of big data, banks would expand their current structured insights to include the gathering and analysis of data from sources including web click streams, social interactions (Facebook and Twitter), geo-locational insight, and other similar new wells of information.

Let’s have a view of the depiction below published by IDC that shows the explosion in data and the available storage where the former is bypassing the storage capacity and raising new challenges in terms of storage for the huge quantities of data pumped in through various mediums.

Getting Big Data Right: Initiatives already begun....

The risk side of banking is all about controlling costs and risk – consider market, counter-party credit or liquidity risks; reputational harm; or government fines that affect financial health or solvency. Data integration and quality are paramount.

A risk analytics data model defines instruments, positions and counterparties along with market data, risk factors and models to compute risk exposures. It also supports stress testing and scenario analysis. With risk data housed in a unified repository, it is much easier to analyze market and credit risks, asset-liability management and liquidity risks. Aggregating risks across all portfolios provides a complete risk picture to the firm. This will help the executive committee and boards of directors understand total firm exposure and how that compares to the firm’s risk appetite.

It’s not just talks, but action has begun amongst banking community. As part of its five-year vision around data, ING Direct is planning to invest AU$1 million to execute its big-data strategy, with technology implementation set to be completed by late 2013. ING Direct's BI team is not contained within the IT department. In 2006, the data-warehouse team was taken out of the IT department, and merged with the analytics team to form the BI division. The latter has since been combined with marketing intelligence, and now sits in ING Direct's customer department.

The BI team has a stake in marketing, the brand, customer experience, and the banking products themselves. "Basically, we are responsible for data warehousing, for reporting and analytics, for market research, and targeted marketing,"  ING Direct head of business intelligence (BI) Greg Nichelsen said.

At ING Direct, data analytics is fed into everything. The bank is able to gleam customer segmentation, churn behaviours, and customer experience through data analytics.

Earlier this year, Bank of America launched a trial service that allows customers to enjoy savings based on their history of spending. With the advantage of online banking, customers can view deals that match with their shopping behavior.

Whenever a customer swipes a Bank of America credit card, the deals automatically apply. As a result, the number of credit card activations and usage rose exponentially. The campaign is a win-win for all parties. Customers save money from the deals, merchants can tailor the deals and recipients to influence consumer behavior, and the bank receives more transactions because people use their credit cards more frequently.

BNY Mellon is taking on a major IT project that's reimagining everything about how the $1.4 trillion-asset bank stores, crunches, uses and delivers data - perhaps the second most important four-letter word in banking after cash. The bank's IT executives have realized that strong data management and analytics are no longer a nice-to-have, but a must-have.

BNY Mellon is embarking on its project at a time when almost all banks are sorely behind when it comes to collecting and crunching customer and market information to be an informed partner with its customers and staff. It's a holy grail that still eludes almost all traditional financial institutions, and it's why you hear so much about PayPal, Square, Movenbank, Google and even Walmart doing the innovative work in user experience that banks should be doing.

The bank's goal is to make BNY Mellon's web analysis work "more like Google" in the sense that when people log into the bank's site from a PC or mobile device, or a more traditional channel, the bank is ready with a full picture that leverages all of this new data to anticipate a service query or a need. For example, Google's analytics program provides marketers with information on a consumer's web visits, the visits' geographic origin, time of day, amount of time spent on site and what parts of a site that consumer visited.

The bank's goal is to make BNY Mellon's web analysis work "more like Google" in the sense that when people log into the bank's site from a PC or mobile device, or a more traditional channel, the bank is ready with a full picture that leverages all of this new data to anticipate a service query or a need. For example, Google's analytics program provides marketers with information on a consumer's web visits, the visits' geographic origin, time of day, amount of time spent on site and what parts of a site that consumer visited.

As part of the new Big Data project, the bank, which employes 13,000 technologists globally, will seek to move beyond the siloed and department-centric manner in which data has been stored and analyzed. BNY Mellon also hopes to enable centralized access to its data regardless of which data center it chooses for storage. The bank has data centers around the world and its strategy will be independent of physical location.

BNY Mellon's database tech includes MongoDB, Cassandra and Lucene, which are designed to scale based on need and expansion. MongoDB is an open source NoSQL document oriented database which Kumar says can store time series effectively in addition to content management. Kumar says that in a large organization, there are lots of applications that create and store operational data. For any application that needs data across the enterprise, such as risk, the application typically receives that data from its source system and creates a data warehouse. Over a period of time, he says you end up with too many data warehouses with redundant data. The bank plans to use Cassandra to store the data once. "The quality of data is better because we have [centralized access to] data to keep track of instead of multiple [department data sources]," CIO Suresh Kumar says. He also says that by using open source technology, the bank is able to scale to store more data at less expense because it's managing less hardware. "Your cost of ownership goes down."

Bankinter, the tech-savvy small Spanish bank, last year started using a system to analyse complex loan portfolios on computers run by Amazon, an online retailer. Cloud computing enables it to hire massive number-crunching capacity whenever it needs it. These two factors are making it easier for smaller banks the world over to keep their credit-card businesses to themselves and lean against the powerful forces for more and more consolidation in banking.

Card networks such as MasterCard and Visa have begun mining their data to provide targeted advertisements. So have some banks. Citigroup, for instance, monitors the credit-card transactions of its customers in some Asian countries and uses the information to send text messages offering special deals. A customer buying clothes at around lunchtime, for example, might be offered a discounted meal at a nearby restaurant.

Asia is also seeing much activity in terms of Big Data. Panning to a scenario in Asia, Cosmos Bank is a dominant player in Taiwan’s cash-card market. The company uses analytics software to dive into its big data for up-to-date customer intelligence and risk management. Cosmos Bank is able to process and analyze each transaction throughout the month for a more accurate view of each customer’s behaviour, which helps them to extend the right offer at the right time.

Financial institutions have realized that they can’t be just mute spectators to the data that’s getting accumulated and available within their organizations and hence many have taken the initiative to start analyzing these data to provide valuable insights for various purposes. They cannot let themselves crushed by these huge tidal wave of information and hence have decided to utilize them.