Banks face a future where traditional revenue sources —deposits, transaction processing fees, interest on loans, etc.— could disappear as customers seek low-cost, frictionless offerings from fintechs and other traditional and non-traditional competitors
To compete and thrive, retail banks need to explore new revenue sources and this is where the monetization of customer data can play an important role. While monetizing customer data may seem like a challenging proposition in the era of new regulations and privacy concerns, it is critical to sustainable profitability.
Findings from a report published by the World Economic Forum support this premise, stating “data will become a growing point of differentiation for banks, which, for that reason, will have to use a combination of data strategies to collect the depth and breadth of data needed to follow the lead of tech firms in data monetization.”
There are, however, a number of barriers banks will need to overcome to successfully monetize data, including: breaking down data silos, addressing the shortages of talent with data science skills and changing cultural thinking built around traditional service transaction revenue rather than creating new, innovative sources of revenue that leverage customer insights.
Banks, like other consumer-facing businesses, are hiring scores of data scientists to capture value out of their data and to help design great customer experiences. However, with the supply of data scientists greatly outstripping the demand, many companies are turning to DIY solutions to turn data into sources of revenue.
A data lake is a solid foundation for success because it provides a global storage repository for all types of data from multiple sources that business users can draw on to create new insights and drive better business decisions. It lets organizations cost-effectively store, manage, classify, and analyze their data. A data lake can help retail banks:
Break down data silos: Storing data in a centrally managed Apache Hadoop–based data lake infrastructure helps cut down the number of information silos in an organization making data accessible to users across the enterprise.
Address the shortages of talent with data science skills: Selecting the right data lake—a cloud based business data lake for line of business use cases as opposed to an enterprise data lake for IT operations- can help banks address the data science talent shortage. A data lake that offers easy deployment, a simplified user interface and minimal coding requirements reduces the dependency on data scientists and IT support.
Change cultural thinking by empowering business users: By putting data insight creation in the hands of business users with a business-user-friendly data lake, teams can draw new insights and accelerate the development of use cases built around monetizing customer data.
How a bank chooses to monetize their data depends on many factors including customer needs, strategic priorities, regulatory constraints and more. Here are just a few examples of how teams across the organization can leverage data lakes to deliver better customer experiences and improve operational efficiency.
- Help customers make better financial decisions: By incorporating structured and unstructured customer information into their risk modeling, banks can help customers make better credit risk decisions and monitor their portfolios for early identification of potential problems. The bank will also be able to use this information to detect financial crime and predict operational losses.
- Automate compliance and regulatory reporting: Banks can reduce highly manual tasks by streamlining data extraction from source systems and standardizing data aggregation and reporting to drive increased efficiency and productivity.
- Improve loan book profitability: A data lake provides access to data and helps prepare it for analysis of loan books and examination of credit profile spread against revenue and margins to reduce risk of delinquency and identify most profitable customers.
- Improve collection efficiency: Banks can improve debt collection effectiveness by analyzing collection performance and debt recovery along with stratification of customers to identify and prioritize what customers to and not to contact.
These are just a few of the many possible ways banks can monetize customer data. Partnering with third parties to offer new services and selling data insights to other organizations presents a number of additional monetization opportunities. With the growing number of privacy protection regulations, banks need to be thoughtful about how customer data is shared, but that’s a subject for another blog post.
The bottom line is that the ability to drive value from data will be a key differentiation for retail banks and data lakes can play a crucial role in executing a successful data monetization strategy. The banks that reap the value from all their data and make it accessible to teams across their organizations will be in a far better position to deliver exceptional customer experiences, improve operational efficiency and stave off the competition.
Learn More: Find out how you can empower your lines of business with easy access to data and analytics on a self-serve, end-to-end data lake platform. Check out the Connected Intelligence Data Lake for Business™ in AWS Marketplace. Get started today with a 30-day FREE trial.