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

Predicting the trend of a security

In the preceding example, we played the role of a trader who followed the portfolio allocation set by the treasurer. Assuming that our job is to follow the securities required by the treasurer, the profit and loss of the trader hinges on how can we profit from buying low and selling high. We took the daily pricing history of securities as the data to build our model. In the following section, we will demonstrate how to predict the trend before making buy decisions for assets.


There are two major processes—one on model development and another on model backtesting. Both processes include a total of eight steps for real-time deployment, which we will not include here. However, it is very similar to model backtesting. The following diagram illustrates the flow of the process:

Loading, converting, and storing data

In this step, we will load the data, convert...