The banking and financial services (BFS) industry has been one of the biggest adopters of Big Data technologies. There are several reasons for banks and financial services organizations to gravitate towards these platforms.

  • Ability of Big Data platforms to process and analyze unstructured data efficiently unlike current platforms that are limited to handling structured data. This flexibility makes it possible to gain insights from variety of data.
  • Analytics of unstructured/ social/ mobility data makes it feasible to conduct sentiment analysis and study transaction patterns.
  • Need to move away from expensive Big Data platforms to ones that are more cost effective Patterns are emerging in the adoption of Big Data even as it is being adopted to fulfill various business objectives.

The diagram lists various use cases of big data analytics in financial industry.

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Drivers

Technology

Technological Ability to handle various Data characteristics

  • Improvement of business process efficiencies by centralizing the processing of data
  • Use of faster and more efficient architectures to process data
  • Availability of more data for analysis
  • Improvements in time-to-market capabilities
  • Ability to deal with semi-structured information like proprietary XML Blobs eliminating the need for data to be decomposed into 3rd normal form.
  • Aggregation and collation information from multiple lines of business (LOBs) and multiple formats (click stream, app logs, call center log, Enterprise data etc.)

Advanced scientific analysis of data

Advanced analysis of data would not have been possible with legacy platforms due to constraints to the amount of data available for analysis. The use of machine learning to gain insights including patterns and similarities in consumption styles, recommendations and up sell and cross sell capabilities. This has resulted in the following:

  • Adoption of advanced analytical applications for fraud detection and risk management
  • Trade clearing, settlement, reconciliation
  • Audit trails, regulatory compliance
  • Measuring success of card loyalty programs, Anti Money Laundering (AML) applications
  • Use of Monte Carlo simulations for operational risk modeling

Integration with Social Media

With the ubiquity of social media, enterprises are starting to avail of the unique features and opportunities that such sites provide for businesses.

  • Integration with social media sites to perform sentiment analysis and voice of customer applications
  • Harnessing social data for targeted campaigns for credit card, gift cards, and other products.
  • Social Media site which is primarily targeted at individual users provides these users with seamless access to other sites where they can post their comments and voice their opinions.

Compliance and Regulatory Reporting

Comply with a key provision of the Dodd Frank Act that requires big swap traders to document everything that goes into each swap trade by implementing a deal monitoring system based on a new generation of Big Data technology.

Customer Segmentation

Group customers into different segments to support sales, promotion, and marketing campaigns by collecting and analyzing all available data and using Big Data technology to mine for intelligence from underlying data.

Personalized Product Offering

Target new product and service offerings to the right customers by implementing software that supports flexible and integrated processes for understanding customer buying habits, what channels customers listen to, and who the key influencers are.