Big Data and Analytics in Banking and Finance Industry

QSS
QSS
Feb 8 · 8 min read
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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.”

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:

  • 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.

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.

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

  • 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
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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.

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  • 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.

Leverage Big Data Analytics

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

  1. 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.
  2. 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.
  3. 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.
  4. Streamlined Customer Feedback: Stay up to speed on customer questions, comments, and concerns using big data to sort through feedback and respond on time.
  5. 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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