According to Microsoft, over 60 percent of banks in North America say that big data would offer them an advantage, but only 37 percent have experience with live big data technology. Most are still using experimentation and pilot programs to determine how to gather and use data.
This is one of the many reasons banks need to start paying better attention to big data. There are several obstacles that seem to be holding most banks back. These include:
- Data Silos: This means data is housed in different places like CRMs, portfolio management, and loan servicing — sources that do not “talk” to each other. This means analysts do not get a full picture of who the customer is.
- Lack of Analytics Talent: Hiring the right analysts and deploying the right resources is hard, and banks struggle to hire the best talent as they are often competing with much larger organizations.
- Lack of Strategic Focus: Without the right talent, it is difficult for banks to know what to focus on and what strategies to employ. This lack of strategy is often what makes it difficult to hire great talent.
- Privacy Concerns: Combining data creates privacy concerns, and the security of customer data, already a common concern and challenge, becomes even more important.
How do banks overcome these challenges, and how can they better use big data? Here are some practical applications.
Offering additional banking services
Most consumers are aware only on a surface level what their credit score actually means. However, banks have extensive data, information that can benefit both them and their customers. Banks can see if the customer’s score is on the rise or the decline and even see possible reasons for the fluctuations.
Medical bills, employment, increased housing costs, purchases, and paying off credit cards and loans are all things that have an immediate, measurable impact. These are important facts to a bank when marketing products to a customer.
A bank often offers products the consumer is not aware of, things they may even be getting elsewhere or could save money using. This is all a part of website personalization and meeting customers where they are.
The underlying message is the need for a good credit score when it comes to everything from home to auto loans. Using big data to help customers improve their credit score and become eligible for more banking products is in the best interest of both the customer and the bank. The offer of debt consolidation loans and even free financial advice is a great use of this data.
The application of this information comes as customer support, risk assessment, decision-making support and researching for new profit opportunities. Big data processing comes in three different forms; variety, velocity, and volume. Each of these different forms of data should lead to the most important v: value.
Sam Kumar, Global Head of Analytics at the British multinational banking and financial services company, Standard Chartered Bank, says technology and data are making it easier for banks to improve customer service. “What it allows us to do is build a very good picture of a client as an individual,” Kumar said, “their preferences and financial aspirations.”
Big data allows a client’s data to be analyzed in a matter of minutes to increase efficiency for the bank and client. Standard Chartered operates more than 1200 branches, which means it’s especially important for them to be able to analyze all of their data quickly. This bank has used big data to improve customer experience and to speed up the process of loan approval.
Wells Fargo Chief Financial Officer John Shrewsberry appeared at the Milken Institute’s annual conference, “Navigating a World in Transition,” in May 2018 as a panelist on the topic of “Big Data and Creating Customer Growth in the Age of A.I.”
“Today, seemingly unlimited amounts of data and ever-increasing computing power give us the capability to deliver contextual, hyper-personalized experiences to each and every customer, in the moment, wherever they are,” said Shrewsberry on his LinkedIn page.
Over the last few years, Wells Fargo has sought out more data analysis technology to facilitate their use of big data and their attempts at improving efficiency. With over 8700 branches, Wells Fargo’s data department must be able to collect, combine, clean, and categorize data from 70 million different customers. Without data analytics software, this can be an extremely difficult and complicated process due to the mass amounts of data.
According to Jim Smith, Executive VP and Head of the digital channels group at Wells Fargo, “The areas we think are really interesting in the next five years relate to mobile,” he stated. “So far, most companies have just taken capabilities they had in online banking and other channels and rebuilt them into mobile.” Because big data is helping to identify and prevent fraud, Smith expects that functions such as loan applications and opening new accounts could be done entirely over mobile in the coming years.
Bank of America
As the second largest bank in the U.S., Bank of America has been a frontrunner in the use of big data to improve their customer processes. The banking giant has openly discussed the benefits big data provides to the financial industry.
Spokespersons for the bank have stated the use of big data technology in processing and analyzing data from its full customer set. Because the various sales channels can communicate with each other, it helps decrease the number of customer experiences that fall through the cracks. According to a Bank of America spokesperson, “A customer who starts an application online but doesn’t complete it could get a follow-up offer in the mail, or an email to set up an appointment at a physical branch location.”
Other applications for big data have been discovered by Bank of America analysts as well, such as using data to expedite their financial forecasting. This allows them to develop forecasts that used to take months in a matter of days.
Leveraging buying and savings patterns
When does the customer make purchases? When do they save? Is there an observable pattern? What about with customers overall? When is a good time to market tax savings accounts to business customers? When do they open them, and when do they use the funds? What about vacation accounts, holiday savings accounts, and other types of accounts?
While it is difficult to predict the behaviors of one individual, as a group customers tend to follow a pattern that can be established by the use of big data and predictive analytics. This information can be used to structure marketing campaigns and even help banks with their own financial planning efforts.
Everything from special credit offers, sales on car and home loans, and even the timing of refinancing offers can be determined by using the big data the bank is already collecting.
Marketing is all about reaching the customer with the right offer at the right time, and big data like credit reports and other customer information makes that possible.
Anticipating customer needs
All of this data is about anticipating customer needs and even keeping them from financial trouble in the first place. For instance, using the data they have, a bank can see that direct deposits from an employer have stopped, the customer has set up no new direct deposits, and there are no new paper payroll deposits. The bank may also see direct deposits from unemployment insurance or workman’s compensation. This likely means the customer’s career situation is changing.
If that same customer has home and auto loans with the bank, an offer to refinance to lower payments or an offer of a lower interest rate may prevent default later on. Even an offering of a grace period might be in the best interest of the bank.
This is just one example of anticipating a customer’s needs based on current behavior, data that banks already possess and process. Beyond individual customers, community events and patterns, local economic trends and other data can also help banks serve their customers better.
This anticipation of customer needs is really what marketing is all about, and a good understanding of marketing trends and the digital marketplace, combined with the gathering and analysis of big data, will determine the banks that thrive in the future.
The big data of banking goes far beyond simple credit scores to deep, rich data. The challenge of hiring analysts and blending data must be met with a good data strategy, and only then can this increasingly valuable information be harnessed.