Book Image

Hands-On Artificial Intelligence for Banking

By : Jeffrey Ng, Subhash Shah
Book Image

Hands-On Artificial Intelligence for Banking

By: Jeffrey Ng, Subhash Shah

Overview of this book

Remodeling your outlook on banking begins with keeping up to date with the latest and most effective approaches, such as artificial intelligence (AI). Hands-On Artificial Intelligence for Banking is a practical guide that will help you advance in your career in the banking domain. The book will demonstrate AI implementation to make your banking services smoother, more cost-efficient, and accessible to clients, focusing on both the client- and server-side uses of AI. You’ll begin by understanding the importance of artificial intelligence, while also gaining insights into the recent AI revolution in the banking industry. Next, you’ll get hands-on machine learning experience, exploring how to use time series analysis and reinforcement learning to automate client procurements and banking and finance decisions. After this, you’ll progress to learning about mechanizing capital market decisions, using automated portfolio management systems and predicting the future of investment banking. In addition to this, you’ll explore concepts such as building personal wealth advisors and mass customization of client lifetime wealth. Finally, you’ll get to grips with some real-world AI considerations in the field of banking. By the end of this book, you’ll be equipped with the skills you need to navigate the finance domain by leveraging the power of AI.
Table of Contents (14 chapters)
Section 1: Quick Review of AI in the Finance Industry
Section 2: Machine Learning Algorithms and Hands-on Examples

Where to look for more use cases

AI applications listed in this book largely focus on front-office banking services; the back-office processing jobs are not covered in any great detail. Stepping back, where should you look out for opportunities in case you wish to start your own project?

  • Long hours; boring job: Boring means repetitive, and that's where machines thrive and data is rich.
  • Large labor force: When it comes to business cases, it is easy to look for jobs that have high levels of employment. This means a huge business potential and easy-to-justify implementation. This constitutes a huge challenge for HR professionals.
  • High pay: If we were to make finance accessible, can we make these highly paid jobs even more productive? In the case of investment bankers, security structurers, and hedge fund traders, how can their non-productive time be reduced?
  • Unique dataset: If the dataset is not accessible to outsiders, the chance...