Big Data in Banking

Step-by-step Big Data strategy with practical hands-on tools

Big Data in Banking demonstrates how to build and execute an effective Big Data strategy in the context of finance data analytics. Clear and concise, this accessible guide consolidates a wealth of information into a non-technical overview of the issues and opportunities Big Data brings to the banking industry. Step-by-step guidance walks you through the possibilities and potential pitfalls, giving you an in-depth understanding of the economics and technology behind Big Data applications in finance, investments, wealth and asset management, and more.

You'll examine the technology behind high frequency trading, trading strategy development, data mining, and risk management, and learn how robo-advisors and emerging investment processes have shifted client behavior and acquisition. The discussion of intellectual property and transfer pricing provides material background for your own data strategy, and coverage of ethics, privacy, transparency, and trust serves to inform execution processes. Data sourcing, structuring, and analytics all fall under the Big Data umbrella, but certain aspects have greater relevancy within the world of finance.

This helpful guide sorts through the surplus of information to give you a direct overview of the technology, processes, and issues becoming increasingly critical in banking. * Learn how Big Data is used throughout a company * Understand the technology behind emerging banking opportunities * Integrate Big Data into client relationships and management * Utilize step-by-step digital training tools for R programming Big Data and its ancillary technologies bring both opportunity and risk to the banking industry. As banks rush to leverage the power of Big Data and advanced analytics, there are still major issues that require attention.

Big Data in Banking helps you develop a robust strategy for realizing the potential while avoiding the problems.