Your Go-to Guide to Big Data Analytics in Banking

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Banking customers generate an astronomical amount of data every day through hundreds of thousands — if not millions — of individual transactions. This data falls under the umbrella of big data, which is defined as “large, diverse sets of information that grow at ever-increasing rates.” To give you an idea of how much information this is, we generate 2.5 quintillion bytes of data every day! This data holds untapped potential for banks and other financial institutions that want to better understand their customer base, product performance, and market trends.

But where, exactly, does all this data come 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 banks better understand their customers.

Types of Big Data

With 2.5 quintillion bytes of data generated every day, not all of it can fit within a single category. There are three ways to classify big data:

  • Structured: This type of data is highly organized and exists 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 might initially appear unstructured but contains keywords that can be used for 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 Banking

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

Using analytics-driven strategies and tools, banks are able to unlock the potential of big data, and to great effect: Businesses that are able to quantify their gains from analyzing big data reported an average 8% increase in revenue and a 10% reduction in overall costs, according to a 2015 survey from BARC. To better illustrate just how financial institutions can take advantage of big data and big data analytics in banking, we’ll follow the journey of a fictional customer, Dana, who recently opened a primary checking account with America One, a fictional bank.

The Top 5 Benefits of Big Data in Banking

After years of dissatisfaction with her previous bank, Dana recently made the switch to America One at the recommendation of a few of her friends. Dana’s excited to be with America One because she’s heard great things about its personalized customer service, and America One is excited to have her, too. Now that she’s officially a customer, America One’s team is ready to use big data and banking analytics to ensure that Dana has the best experience possible.

1. Gain a Complete View of Customers With Profiling

Customer segmentation has become commonplace in the financial service industry because it enables banks and credit unions to separate their customers into neat categories by demographic, but basic segmentation lacks the granularity these institutions require to truly understand their customers’ wants and needs. Instead, these institutions need to use big data in banking to take segmentation to the next level by building detailed customer profiles. These profiles should account for a variety of factors, including:

  • The customer’s demographic
  • How many accounts they have
  • Which products they currently have
  • Which offers they’ve declined in the past
  • Which products they’re likely to purchase in the future
  • Major life events
  • Their relationship to other customers
  • Attitude toward their bank and the financial services industry as a whole
  • Behavioral patterns
  • Service preferences
  • And so on

According to America One’s customer profile of Dana, she’s a woman in her late 30s, which means she’s a member of Generation X. Her attitude toward the financial services industry is more favorable than those of her Millennial counterparts and, so far, she’s very happy with America One’s service. Dana is college-educated, lives just outside a major metropolitan area, and has been married to her partner — who is also an America One customer — for the past four years. When Dana joined America One, she was earning a median salary, but a recent promotion has pushed her into a higher income bracket. At present, Dana has two accounts — a primary checking account and a high-interest savings account — and a credit card with America One; as a homeowner, Dana also has a home mortgage with a different bank. Dana’s a big fan of online banking; she checks her accounts at least once a day through America One’s mobile application and has only submitted two service requests to date, both of which were resolved within 24 hours.

2. Tailor the Customer Experience to Each Individual

Nearly one-third of customers expect the companies with which they do business to know personal information about them; in fact, 33% of customers who abandoned a business relationship last year did so because of a lack of personalization in the service they received. For all its talk of relationship banking, the financial services industry isn’t exactly known for its high level of personalized service. For those banks and credit unions that hope to not just survive, but thrive, a banking analytics-oriented shift in perspective and tailor-made customer experience are absolute necessities.

By looking at Dana’s customer profile and service history, an American One employee can see that she prefers to do most of her banking online using the bank’s mobile app. Based on this data, and data from other customers with similar preferences, America One’s executive leadership team decides to add an AI-enabled chatbot functionality to its apps so that customers can submit service requests and resolve issues entirely online. Since the chatbot uses AI technology to analyze Dana’s data and identify behavioral patterns (more on that in a minute), it is able to accommodate her preferences and provide personalized responses without ever sacrificing the quality of service. Should Dana’s request exceed the chatbot’s capabilities, or should she decide that she’d like to talk to a person, the bot will escalate her request to a live service representative.

3. Understand How Your Customers Buy

Almost all big data in banking is generated by customers, either through interactions with sales teams and service representatives or through transactions. Although both forms of customer data have immense value, data generated through transactions offer banks a clear view of their customers’ spending habits and, over time, larger behavioral patterns.

America One already knows what Dana’s monthly paycheck is, that she likes to pay her bills early and that she puts an average of $500 into a high-interest savings account per paycheck. This information provides a solid foundation for who Dana is as a person, such as that she’s a relatively high earner with disposable income, has a high credit score, is responsible about her monthly payments, and values saving money for the future. Seeing that her savings account currently holds a balance of over $10,000, America One offers her a high initial rate CD offer during her next login and suggests that she talk to an in-house financial advisor to learn how much she could earn in higher interest by putting that money to work.

4. Identify Opportunities for Upselling and Cross-selling

Businesses are 60%–70% more likely to sell to existing customers than they are to prospects, which means cross-selling and upselling present easy opportunities for banks to increase their profit share — opportunities made even easier by big data analytics in banking.

One day, while reviewing Dana’s transaction history, an America One employee notices that she recently purchased plane tickets for her and her partner to a few different cities across Europe and South America, as well as booked hotels for each location. Based on this information, the employee (as it just so happens, correctly) assumes that Dana is passionate about travel. ­The employee then pulls up Dana’s customer profile, which shows them that she already has one credit card with America One but that her credit utilization is slightly low. Seeing an upselling opportunity, the employee targets Dana with a marketing campaign for America One’s travel rewards card, which she can use to earn airline miles while increasing her credit utilization and improving her credit score in the process.

5. Reduce the Risk of Fraudulent Behavior

Identity fraud is one of the fastest-growing forms of fraud, with 16.7 million victims in 2017 alone — a record high that followed a previous record high in 2016. Monitoring customer spending patterns and identifying unusual behavior is one way in which banks can leverage big data to prevent fraud and make customers feel more secure.

Prior to embarking on a trip to Barcelona, Dana notifies her bank that she’ll be traveling out of the country so that it won’t put a freeze on her account while she’s abroad. However, while Dana is on his trip, an America One employee notices that someone attempts to withdraw money from her account from an ATM in Houston, TX — over 1,000 miles away from Dana’s hometown in Chicago, IL, and over 5,000 miles from her current location in Barcelona. Suspecting fraudulent activity, the employee pulls Dana’s phone number from her customer profile and contacts her directly to notify her. After confirming that it is, indeed, fraudulent activity, the employee denies the ATM request, thereby keeping Dana’s account safe.

What to Watch for When Implementing Banking Analytics

Implementing a big data banking analytics strategy is in the best interest of any financial institution, but it isn’t without its challenges. There are a few things banks and credit unions should be aware of before they proceed.

  • Legacy systems lack the infrastructure to accommodate big data analytics. The sheer volume of big data puts a considerable strain on legacy systems, and many legacy systems lack the advanced analytics in banking to make sense of it in the first place. Banks are therefore advised to upgrade their existing systems before implementing a big data strategy.
  • Data quality management needs to be a top priority. Even if a bank upgrades its system, dirty data — data that is inaccurate, inconsistent, incomplete, duplicate, or outdated — can skew results. Prior to the digital age, most data was entered manually, thereby introducing the risk of human error. Banks should carefully review and consolidate their existing data before they enter it into a new system in order to identify and eliminate instances of dirty data and, in the future, authenticate data input sources to reduce new instances of dirty data.
  • Customers are concerned about the state of data privacy. With multiple security breaches making the news — most recently, a hacker gained access to 100 million Capital One accounts — bank and credit union customers are on high alert over the security of their sensitive data. Banks that hope to capitalize on big data also need to implement robust security measures, such as two-factor customer authentication, data encryption, and real-time and permanent masking, to allay customers’ fears.
  • Consolidation is crucial after an acquisition. Any time an acquisition occurs, new databases are added to a bank’s data estate. In order to be analyzed and put to effective use, the data spread out across each of these disparate systems needs to be consolidated in a central repository. By consolidating data in the immediate aftermath of an acquisition, financial institutions can more easily identify and eliminate dirty data and prevent employees from having to comb through multiple systems to locate relevant customer and product data.
  • Financial institutions are subject to more rules and regulations than ever before. From FINRA to FinCEN to the much-talked-about GDPR, banks are under mounting pressure to remain compliant with an ever-growing list of data-related regulations and regulatory agencies. In order to ensure compliance, banks and credit unions need to go above and beyond when it comes to security and risk management.

The Future of Big Data in Banking

Financial institutions are finding new ways to harness the power of big data analytics in banking every day — a journey of discovery that’s being driven by technological innovation. Two such innovations, machine learning, and artificial intelligence (AI) models combine big data and automation to optimize data quality management and customer segmentation, reduce errors, and make it easier for banks to make groupings and review product data and customer preferences.

For example, machine learning and AI can be applied to loan portfolios to help banks target customers more effectively. These technologies can automatically review a bank’s customer database and highlight common data points, such as credit score, household income, and demographics, which the bank can then use to see which customers could be the right candidate for a particular loan or other product. Banks and credit unions can also use machine learning and AI to pinpoint key influencers behind a customer’s decisions and to identify top performers within their teams.

6 Examples of How Banks are Leveraging Big Data Analytics

So, to recap—the primary benefits of leveraging big data analytics in banking are:

  1. Enhanced Fraud Detection: With big data, you can develop customer profiles that enable you to keep track of transactional behaviors on an individualized level.
  2. Superior Risk Assessment: Big data, when plugged into business intelligence tools with automated analysis features and predictive capabilities, can trigger red flags on customer profiles that are at higher risk than others.
  3. Increased Customer Retention: With in-depth customer profiles at your fingertips, it’s easier to build stronger, longer-lasting customer relationships that drive customer retention.
  4. Product Personalization: Demonstrate your commitment to understanding each individual customer by developing products, services, and other offerings tailored to their specific needs based on their existing customer profiles.
  5. Streamlined Customer Feedback: Stay up to speed on customer questions, comments, and concerns by using big data to sort through feedback and respond in a timely manner.
  6. Workplace Improvements: Create an environment that your employees look forward to working in by using big data to monitor performance metrics, assess employee feedback and company culture, and gauge overall employee satisfaction.

Better Banking With Hitachi Solutions

One of the biggest challenges facing the modern banking industry is that many legacy systems aren’t equipped to handle the big data revolution. And although the concept of big data in banking has been around for several years now, many institutions have yet to build an infrastructure capable of handling the high volume of information that comes with it.

Are you ready to rethink your infrastructure and discover the true potential of big data in baking? Hitachi Solutions is the perfect partner to help you do it. From our Data Quality Health Check to Self-Service Reporting, we specialize in providing the systems, services, and support to help banks not only manage big data but modernize their entire data estate. Our data scientists can apply data models to your data to provide inside based on key metrics and suggest best practices, and our team of banking experts can help you integrate data points spread across disparate systems to create a truly modern data architecture.

Whatever your big data or banking analytics needs, we’re here to help. Contact us today to get started.