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The Biggest Opportunities and Challenges of Data in Banking Today Blog Featured Image

The Biggest Opportunities and Challenges of Data in Banking Today

In the banking industry, data has emerged as a central pillar of modern operations. Data spans decision-making, enhancing customer experiences, and optimizing operational efficiencies. In this age of digital data-centricity, data in banking is abundant. But it is also a functional, critical aspect of all business operations — from customer engagement at the front to risk mitigation at the backend.

From customer transactions to market analysis, every facet of the industry relies on data-driven insights for informed decision-making. Customer preferences have shifted to a more digital-forward channel for interactions. As a result, the volume and variety of data generated has surged, creating both challenges and opportunities for banks.

Data in banking: The key to staying competitive

Banks need to maintain competitiveness against traditional peer banks as well as the emerging threat of neo-banks and fintech companies. That requires harnessing data assets not only to enhance their customer experience but also to streamline operational efficiency and proactively mitigate risks. Easier said than done though; despite the market-wide push to data-driven banking, many banks are stuck in a holding pattern due to technical debt, inconsistent processes, and red tape at the decision-making level.

Traditional banks run the risk of lagging on the switch to data-driven banking

This shift within the banking industry is pressuring traditional banks to evolve rapidly or face being outpaced. In this fast-changing landscape, where stringent regulatory demands persist, old methodologies are becoming obsolete. CRM systems, such as Salesforce, are foundational and indispensable tools for revolutionizing customer engagement, and their successful integration is key for banks undergoing digital transformation to maintain relevance and a competitive edge.

Today, financial institutions encounter hurdles spanning regulatory compliance, data privacy, data quality, integration, technological advancements, innovation, and meeting evolving customer expectations for personalization. However, advancement is both essential and attainable with the prioritization of data analytics and the adoption of tailored strategies for effective implementation and customization.

Case study: Revolutionizing customer engagement through data analytics in banking

A customer of Atrium, a large national bank, needed a strategy for reducing customer attrition and maximizing customer lifetime value with the use of predictive analytics. Here is a look at the challenges they faced, the solutions we implemented, and the resulting business growth and customer satisfaction improvements.

Initial challenges

The bank faced significant challenges in customer retention, struggling to reduce customer attrition and maximize customer lifetime value. A critical issue was the lack of a clear definition of customer churn and the absence of a systematic approach to identifying and addressing factors leading to customer disengagement.

Solution implementation

  1. Defining customer churn: The first step involved collaborating with Atrium to develop an accepted definition of customer churn, which was crucial for a targeted strategy.
  2. Comprehensive data analysis: Atrium’s data science team conducted an extensive data analysis to identify patterns and trends in customer behavior. This process also involved assessing data quality and consistency to ensure reliable analytics.
  3. Predictive analytics model: Leveraging the insights gained, Atrium built a predictive model to proactively identify clients at higher risk of churn. This model allowed the bank’s relationship managers to understand which accounts were at risk and where to focus their retention efforts.
  4. Improving retention strategies: The bank used the model’s findings to enhance its customer retention strategies, identifying not only the red flags indicating potential churn but also opportunities for proactive and personalized customer engagement.
  5. Incorporating third-party data: To expand business and reduce risks, the bank integrated valuable third-party data from Bloomberg/UCC into their analytics. This included diverse information like client revenue, geographic region, and maturity date.
  6. Dashboard for relationship managers: Atrium developed a user-friendly dashboard that combined Bloomberg/UCC and Salesforce data. This tool enabled relationship managers to quickly identify prospective clients and make informed decisions without manually analyzing extensive reports.


As a result of these initiatives, the institution witnessed a series of notable achievements: a significant reduction in customer churn, accelerated business growth, heightened customer satisfaction, and the attainment of a competitive edge.

  • Reduced customer churn: The predictive model and improved strategies led to a significant decrease in customer churn, enhancing customer loyalty and retention.
  • Enhanced business growth: The integration of third-party data and the development of an efficient dashboard facilitated quicker sales processes and more effective prospecting, contributing to business expansion and diversified revenue streams.
  • Increased customer satisfaction: With better insights into customer needs and behaviors, the bank was able to offer more personalized and proactive engagement, leading to higher customer satisfaction.
  • Competitive advantage: The comprehensive data analysis and the seamless merging of sales data provided relationship managers with the necessary tools to stay agile and competitive in their territory and industry.

The benefits of leading with a data-driven approach

By accurately identifying and addressing the factors leading to customer churn and strategically using data for business growth, the bank not only enhanced customer satisfaction but also achieved substantial growth in its business operations.

That’s one way in which we help banking institutions maximize the value of their data and bring predictability and growth opportunities to their business. Your bank’s data should be able to deliver personalized insights that drive growth, increase revenue, and boost customer satisfaction. Does it?

Download our whitepaper on the future of big data in banking for more on today’s challenges, opportunities, and evolving expectations.