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

Which areas require more practical research?

In certain areas, this book has hit the ceiling of research, and these are the research areas that could help move AI applications in banking:

  • Autonomous learning: AI will be replacing the works of AI engineers—given that the machine will be able to learn. Given the wealth of data nowadays, the machine will adopt its network structure itself.
  • Transparent AI: As the machine starts to make decisions, humans will demand transparency as regards the decision-making process.
  • Body of knowledge: In the case of expert knowledge, further research will look at how organizations can use AI to generate the body of knowledge. Practically, the Wikipedia form stored in BERT or any language model is not intended for human consumption or knowledge cultivation. And how do we squeeze the knowledge map to form a neural network, and vice versa?
  • Data masking: To allow data to travel...