A Complete Guide to Big Data in Banking, Plus 6 Examples

From personal information to transactional trends, the banking industry collects a tremendous amount of data. And while many businesses have fully embraced digital transformation to offer customers online and mobile banking options, many others have yet to implement big data strategies to use this data to their advantage.

Read on to get the scoop on big data in banking, including practical examples and how to get started.

What is Big Data?

Big data refers to a massive amount of complex data sets. To give you an idea of how much information this is, we generate 2.5 quintillion bytes of data every day!

But where is this data coming from? The technology behind smartphones, tablets, and the internet of things (IoT) has made it easier than ever for consumers to use online resources to communicate with companies, research products, purchase items, and even perform banking tasks. These activities are then used to develop customer profiles that can track trends, predict behaviors, and help businesses better understand their  customers.

Types of Big Data

With 2.5 quintillion bytes of data created every day, not all of it can fit into one category. There are three ways to classify big data:

  • Structured: This type of data is highly organized and in a fixed format, such as a CSV file.
  • Unstructured: This data has no clear format. An example could be emails, since they are difficult to process.
  • Semi-structured: Data that is semi-structured may initially look unstructured but can contain keywords that can be used in processing.

The incredible volume of data available at our fingertips requires advanced processing techniques in order to be translated into valuable, actionable information. Using the proper business tools is the most efficient way to filter through all types of big data.

Big Data in the Banking Industry

The banking industry is a prime example of how technology has revolutionized the customer experience. Gone are the days when customers had to stand in line on a Saturday morning just to deposit their paycheck. Customers can now use their mobile phone to check their account balances, deposit checks, pay bills, and transfer money — there’s no need for them to even leave the house.

These self-service features are fantastic for customers, but they are one of the main reasons why traditional banks are struggling to compete with similar businesses and online-only financial institutions. Since customer activity now occurs mostly online, certain in-person services that brick-and-mortar banks have been known to provide are no longer relevant to customer needs.

This is where adopting big data strategies and tools becomes so important to the banking industry. Using both personal and transactional information, banks can establish a 360-degree view of their customers in order to:

  • Track customer spending patterns
  • Segment customers based on their profiles
  • Implement risk management processes
  • Personalize product offerings
  • Incorporate retention strategies
  • Collect, analyze, and respond to customer feedback

6 Examples of Big Data in Banking

With big data, banks can use historical trends, present actions, and future predictions to gain the insight necessary for making crucial business decisions. Here are a few of the many ways that banks are using big data to think big and deliver small.

Fraud Detection

Everyone has unique spending patterns. Some people may stick to a strict budget while others may have more fluidity with their finances. With your data, you can develop customer profiles to keep track of transactional behaviors on an individualized level. For example, if a usually cautious banker withdraws all of the money from their checking account, your data will tell you if this goes against their typical behavior. You can then alert the customer of the activity and verify if their debit card was stolen, often before they even realize their account has been drained.

Risk Assessment

Using big data along with the right business intelligence tools can enhance risk assessment procedures. Automated analysis features and predictive capabilities are able to trigger red flags on customer profiles who may be higher risk than others. Since big data can also track customer behaviors, it’s possible for banks to use information to determine the likelihood that a certain customer may experience financial difficulties that could affect their ability to pay back a loan or meet their monthly minimum credit payments.

Customer Retention

The more you know about a person, the better connection you can make. This is a crucial aspect of client services in any industry. Adding a human element to the customer experience goes a long way, especially at a time when we can get overwhelmed by technology.

Using big data, banks can nurture client relationships as a retention strategy. Since everything you need to know in order to develop a rapport with a customer is available on their profile, every interaction can be personal. If a bank teller notices that a customer makes frequent travel-related purchases, they can offer a credit card that includes travel rewards. Not only can this sign the customer on for a new product, it shows them that the bank teller and company is looking out for them in helpful ways. paying attention to their habits.

Product Personalization

It’s no surprise that people are unlikely to engage with a business that offers products unrelated to their needs. For example, a longtime homeowner who uses a certain bank for their mortgage loan probably won’t be interested in receiving offers for first-time homebuyer programs.

A one-size-fits-all banking experience won’t differentiate your bank from another down the road, so providing a level of personalization is necessary to stand out from the competition. Your data can tell you the type of products your customers already have, and analytics can show what they may also be interested in. From there, you can design personalized products and services that show your commitment to understanding each customer’s needs. This is also a strategic way to encourage customer retention.

Customer Feedback

From call centers to social media channels, there are many different ways banks can receive customer feedback. Keeping up to speed with questions, comments, and concerns and responding in real time is key to customer satisfaction.

Big data tools can be used to sort through customer feedback so that banks can respond in a timely manner. Quick responses — especially to negative feedback — show your customers that you’re committed to putting their needs and concerns first.

Workplace Improvements

Don’t forget about your employees! Big data can help you create an environment where employees look forward to working.

Instead of relying on memory when it comes time for annual reviews, your data can keep track of performance metrics for more data-driven evaluations. Data can also be used to assess employee feedback and company culture, which can help you gauge overall employee happiness.

How to Get Started with Big Data

A challenge facing the industry is that many legacy banking systems aren’t equipped to handle the big data revolution. And although the concept of big data has been around for several years now, many banks have yet to plan for an infrastructure capable of handling the high volume of information that comes with it.

Through the power of the Microsoft platform, Hitachi Solutions delivers comprehensive solutions that help solve business problems, including data management and data architecture strategies, as well as the business intelligence (BI) tools that can assist bankers in gaining actionable insights into their business performance. Our financial services team offers products developed specifically for the banking industry and then customized to meet your business’ unique needs.

Ready to upgrade your data infrastructure? Reach out to the experts at Hitachi Solutions — we’re happy to help