Big Data and Analytics in Banking and Finance Industry

QSS
QSS
Feb 8 · 8 min read

Big Data and Analytics in Banking and Finance Industry

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 defines “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 understand better their customer base, product performance, and market trends.

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 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 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 exact 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 used for processing.

The incredible volume of data available at our fingertips requires advanced processing techniques to translate 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 to deposit their paycheck. Customers can now use their mobile phones to check their account balances, deposit checks, pay bills, and transfer money — there’s no need for them to leave the house.

These self-service features are fantastic for customers, but they are one of the main reasons why traditional banks struggle 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 are known to provide no longer relevant to customer needs. Using both personal and transactional information, banks can establish a 360-degree view of their customers 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 can unlock the potential of big data. Businesses that can 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.

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. Still, basic segmentation lacks the granularity these institutions require to understand their customers’ wants and needs truly. 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 with other customers
  • Attitude toward their bank and the financial services industry as a whole
  • Behavioral patterns
  • Service preferences
  • And so on
  1. 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 highly personalized service level. For those banks and credit unions that hope not just to survive but thrive, a banking analytics-oriented shift in perspective and tailor-made customer experience are absolute necessities.

  1. Understand How Your Customers Buy

All the 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 into their customers’ spending habits and, over time, larger behavioral patterns.

  1. Identify Opportunities for Upselling and Cross-selling

Businesses are 60%–70% more likely to sell to existing customers than 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.

  1. 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 how banks can leverage big data to prevent fraud and make customers feel secure.

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 — inaccurate, inconsistent, incomplete, duplicate, or outdated — can skew results. Before 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 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 their sensitive data security. 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. 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 the 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. 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-driven technological innovation. 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, loan portfolios can apply machine learning and AI 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. The bank can then 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 critical influencers behind a customer’s decisions and to identify top performers within their teams.

Leverage 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 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 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 using big data to sort through feedback and respond on time.
  6. Workplace Improvements: Create an environment that your employees look forward to working in using big data to monitor performance metrics, assess employee feedback and company culture, and gauge overall employee satisfaction.

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