Big Data Use Cases in Financial Services

£1,500.00

The who, what, where, when and why of Big Data

This report covers nine key use cases for Big Data technology in the financial sector. The use cases are demonstrated by descriptions of Big Data implementations across a range of financial institutions.

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In 2016, data-rich financial institutions are challenged by the need to effectively manage and monetise information that is often held in silos across the organisation in a number of different formats and schemas. For these companies, competitive advantages can be gained through bespoke analysis of this data in an effort to uncover hitherto unexplored correlations. Big Data technology is an enabler and, as this trend in the treatment of data gathers pace, financial companies are beginning to exploit its capabilities to achieve more profitable approaches to data management.

However, the benefits of Big Data implementations can only be fully achieved with a change of culture across the organisation. Successful Big Data project implementations consider how the technology can be used across the whole of an organisation, facilitating Big Data usage in various environments and for a number of different purposes. These joined-up thinking approaches to Big Data technology implementation can assist companies in the realisation of a data management approach that will future-proof their operations for years to come.

This report reviews the extent to which Big Data technology is already being implemented, and it examines nine business use cases specific to financial organisations. To support each use case, the report provides examples of implementations across the financial markets. This report is the second of two GreySpark reports exploring Big Data technology in the financial services industry; the first report, titled Big Data Technology in Investment Banking, described the key concepts of Big Data technology for non-technical business managers.

Published on: 15 Apr, 2016

Big Data Use Cases in Financial Services – Table of Contents

  • 1.0 An Introduction to Big Data
    • 1.1 Defining Big Data
    • 1.2 Motivations for Big Data Use in the Financial Services
    • 1.3 System Integration
    • 1.4 Speed
    • 1.5 Semantic Querying
    • 1.6 Dashboards
    • 1.7 Security Concerns and Data Protection
  • 2.0 Big Data Use Cases in Financial Services
    • 2.1 Supporting Strategic-level Competitive Insights
    • 2.2 Enhancing Product Offerings
    • 2.3 Augmenting Client Relationship Management
    • 2.4 Focusing Marketing Strategies
    • 2.5 Improving Sales Performance
    • 2.6 Energising Analytics for Decision Support
    • 2.7 Bolstering Risk Management
    • 2.8 Honing Fraud Detection
    • 2.9 Avoiding Reputational Damage
  • 3.0 The Future: Twice as Fast, but for a Price
  • 4.0 Appendices
    • 4.1 Glossary of Terms
    • 4.2 Table of Figures