Big data in banking has only gotten bigger. Through digital channels, third-party applications and enhancements to core systems, institutions have access to more banking data than ever before. And bankers understand the value of this data availability. According to CSI’s 2024 Banking Priorities Survey, 34% of bankers reported investing in data analytics as a top technology.

What are Data Analytics in Banking?

Data analytics in banking refers to using data to understand customer behavior and drive business decisions. With this information, banks can better recognize trends and customers’ needs to provide a more targeted, personalized experience.

Leveraging data analytics allows banks to have insight into their customers and identify ways to better serve them. It also reduces the risk of attrition for institutions of all sizes, since customers are served with relevant products that they might have gone elsewhere for.

Why Should Community Banks Leverage Data Analytics?

In today’s world, people experience similar life events, such as graduating college, buying a home, creating a retirement account or opening a credit card. Each of these events involves financial activity, and breadcrumbs exist in a consumer’s financial data that indicate which life event could come next. Using this information, banks can target customers by life events, activities, interests or products used to create targeted, revenue-driving marketing campaigns.

However, anticipating life events can be difficult given the nature of transactional data. Categorizing and cleansing this data to form a clear picture can make all the difference. Institutions can leverage data to understand patterns to predict upcoming life events and detect behavioral changes to determine if someone is at risk of leaving their institution.

For example, if someone has made similar transactions at the beginning of each month but abruptly stops for several months, that could indicate their intention to switch to another institution. This gives institutions an opportunity to provide a targeted communication or product to that customer.

Gaining Insight into Banking Customers

Many institutions are setting their sights on attracting and retaining Gen X, millennials and Gen Z. While these generations have commonalities, banks cannot treat all customers or prospects the same.

Many within these generations are technology dependent. This holds especially true for millennials and Gen Z, who grew up with technology in their formative years and now expect brands to provide a timely, personal and digital-first experience. Millennials and Gen Z don’t necessarily compare their experience at Bank A vs. Bank B. Instead, they compare banking experiences against those they receive at other businesses or retailers like Amazon.

Further, millennials are set to receive the largest transfer of wealth over the next two decades, with some estimating the transfer will involve up to $90 trillion in assets. As such, they need trusted financial partners to engage and nurture them. It can be challenging for banks to target customers within these generations with the most relevant solutions and services—that’s where data analytics can prove most helpful.

Step 1: Find Your Bank’s Data Analytics Expert

Many financial institutions today view data in a siloed way. Data is segmented by department within the institution, with different leaders analyzing that data in isolation. Loan data is reported by a loan data expert, deposit data is reported by a head of deposit operations, etc. But data from these silos only tell part of the customer story. The value of your institution’s data is immediately maximized when you can see the whole picture. The natural question is, how do you connect disparate banking data, and who owns that initiative?

Enter your institution’s data guru. This expert should know and understand your institution’s complete data set and encourage usage of the analytics tools at your disposal to compile enterprise data into a 360-degree view of your customers.

So, what does this “data guru” look like? Should they be a marketer? Certainly, marketing leaders and department heads within your institution will rely on this professional’s guidance, but ultimately your bank’s data extends beyond any one entity. A well-rounded data strategy should touch nearly every department within an enterprise and provide invaluable feedback that informs strategic vision within the enterprise. That is why your institution should invest in a position that exclusively deals with data from across the enterprise.

Step 2: Take Stock of Your Bank’s Data Segmentation

There are two main categories of banking data that your institution should focus on during this step:

Transactional/Operational Bank Data

This is by far the largest data set at your bank’s disposal. This includes “traditional” core data like loan balances, CIF, account balances, transactional data and other data that resides around banking operations. This type of data is captured via digital channels like a mobile banking app and digital loan applications, or through teller staff, ATM and in-person interactions.

Behavioral Bank Data

This type of data is non-transactional by nature. Behavioral bank data includes insight on your customers’ spending habits, wants and needs. This data also includes anecdotal experiences and touchpoints with your institution. This type of information is captured through your bank’s CRM or third-party data integrations.

While both data sets are important to your bank’s analytics strategy, outlining them isn’t enough. You need to harness the right tools to capture them before true big data mastery can take place.

Step 3: Understand How Your Bank Captures Data

Maximizing big data in banking is impossible without the right tools. Here are the main channels your bank can use to capture customer data:

Core System Data

It’s no secret that most of your bank’s operational/transactional data is captured and recorded within your core banking system. From account balances to transaction history, your core system houses a plethora of banking data.

Digital Banking Data

Does your institution capture your customer’s digital banking interactions? If not, you should. According to a recent report, 84% of individuals use mobile banking channels at least once per week. If your institution doesn’t analyze digital interactions, both from a qualitative and quantitative perspective, a large chunk of your customer touchpoints remains a mystery. Further, your bank’s digital banking data should sync and integrate with core data.

Customer Relationship Management (CRM) Data

CRM data is unique because it is non-transactional data. CRM interactions provide a more creative view of the customer. This type of behavioral data is used directly to enhance the customer experience. CRM data is also vital to campaign management and other marketing efforts to increase cross selling. An integrated CRM specific to banks should allow you to view all CRM data within your core system, in real time.

Non-Core Data

Your relationship with your customers extends beyond your institution. That’s why it’s important to capture and showcase non-core data like investments, wealth management and insurance to gain a complete picture of your customers entire banking relationship.

Banking Data Analytics Dashboards

While the previous tools outline the various ways to capture customer data, banking analytics dashboards allow you to consume and analyze the data in a productive way. From integrated dashboards for your frontline banking staff to detailed executive reports, ensure your institution has access to customizable banking analytics dashboards to consolidate enterprise-wide data into a single view.

Now that you understand the channels and types of banking data at your disposal, it’s time to discuss how to enhance your bottom line by maximizing that data.

Step 4. Maximize Your Banking Data

Your bank’s data capture, integration and usage matters. When all three are prioritized, your institution can successfully leverage big data to maximize customer interactions and your bottom line.

Enhance Customer Interactions

Despite the move to digital channels, many customers still value interactions with your banking staff, especially when they have complicated questions around their needs and your product set. Viewing behavioral and operational data in real time provides an intimate understanding of the customer’s journey and needs, allowing your staff to personalize recommendations to them that are helpful and profitable.

Use Case: A customer has visited a branch to deposit a large sum of birthday cash. When making the deposit, your bank teller receives a “teller pop” notification telling them that the customer has started a loan application online but did not complete it. Your teller also sees a note that the customer had called to ask about your bank’s rates for a car loan. Your staff then asks the customer about what type of vehicle they were looking at, and if they’d like to continue where they left off on the application.

Cross Selling and Automated Marketing Campaigns

Understanding your banking customers’ wants and needs is half the battle. Once you capture that data, it’s time to act. With a consolidated view of your customer data, you can identify your most profitable customers and prioritize cross-selling opportunities that customers will find most helpful. You can take this a step further by creating detailed customer journey mapping for maximum profitability.

Use Case: One of your bank’s astute marketing professionals notices an opportunity to cross sell deposit accounts to existing bank customers who only have a loan. They run a report showing all the bank’s current customers who have loans and do not currently have a deposit account at the institution. They then load those leads into a marketing campaign with a targeted message and call to action.

Step 5: Keep Your Bank and Customer Data Safe

Consumers know and understand the risks associated with data privacy. And as big data in banking gets bigger, so does that risk. It is important that your institution is a good steward of customer data, both from a business and an ethical standpoint, with specific focus on:

• Efficient disaster recovery capabilities
• Current cyber threats and how to defend against them
• Your institution’s compliance obligation to regulations like GDPR, CCPA or other data privacy laws
• Vendor due diligence to ensure that all your third-party vendors—including your core partner—understand data privacy and have plans in place to protect it

Get More Info on Data Analytics in Banking

Want to know how bankers plan to use data analytics in banking? Read our CSI’s 2024 Banking Priorities Executive Report to find out.

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