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5 Big Data Use Cases for Banking and Financial Services – Part 2

In our first blog we covered two of the five Big Data use cases. We will cover now three additional ones that can help Financial Organizations better innovate.

Use case #3: Customer segmentation

In the banking and financial services industry, customer segmentation is a key tool for sales, promotion, and marketing campaigns. Companies collect all kinds of data – from day-to-day customer transactions to home values, travel records, and online buying habits. And they’re turning to Big Data to fill in the gaps by providing the processing power needed to mine for intelligence from underlying data.

By collecting and analyzing all of the data that banks have available about their customers, they can then group customers into different segments based on their expectations and banking needs. With accurate and up-to-date customer segmentation, banks and financial services companies can grow their customer base and increase business by:

  • Improving relationships with profitable customers
  • Increasing new product development and bundling
  • Offering bundled and a la carte pricing options
  •           Cutting costs by understanding channel usage

Use case #4: Risk management

New legal requirements and increasing demand for better internal management support make finance and risk management a main focus area for banks. The solution? A central, integrated data platform that can quickly and flexibly address new requirements.

A finance and risk data management platform can help banks prepare for today’s challenges by:

  • Enabling cross-functional data management processes
  • Supporting different data sourcing strategies, such as periodic and real-time data feeds
  • Consolidating and storing data from diverse source systems in a central platform

To be effective, the data model should be based on industry best practices and be extensible for addressing customer requirements. And the data platform should be open for all kinds of analytical solutions and calculation engines, be supplemented with prepackaged business solutions, and be ready for batch and real-time processing. For companies with an enterprise data warehousing strategy, finance and risk must smoothly integrate with the data warehousing infrastructure.

Use case #5: Personalized product offerings

Retail banks have vast amounts of customer data – from Internet data to customer transaction data to social media data. The challenge is to figure out how to use this data for dynamic customer segmentation that can support marketing campaigns and personalized product and service offerings.

To target new product and service offerings to the right customers, retail banks need more flexible and integrated processes. They need to understand customer buying habits, what channels the customer listens to, and who the key influencers are. Understanding the customer’s influence quotient also helps in acquiring new leads through referrals and determining incentive schemes to be given to the customer for referrals.

With software that supports flexible and integrated processes, retail banks can build customer loyalty and increase profits by:

  • Predicting what new products customers will purchase from the bank, and when
  • Customizing offers to segmented customers
  • Creating targeted marketing campaigns to segmented customers

Defining the right use cases for your organization and building the right Big Data infrastructure can boost your innovation and tremendously improve your business processes. But remember: data will always be as good as the insights you are able to extract out of it.

Check out the 5 Big Data Use Cases for Banking and Financial Services.

Learn how SAP Services can help you define the right strategy to build a Big Data infrastructure to support your business goals:

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