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)
1
Section 1: Quick Review of AI in the Finance Industry
3
Section 2: Machine Learning Algorithms and Hands-on Examples

Summary

We began this chapter by explaining what AI is all about. AI is the technology that makes machines perform tasks that humans can do, such as weather prediction, budget forecasting, and more. It enables machines to learn based on data. We looked at the various techniques of AI, such as machine learning and deep learning. Later, we looked at the complex processes of the banking domain. If we can automate them, we can reduce costs in the banking sector. We also learned about the importance of accessible banking. Later, we looked at the application of AI in the banking sector and its positive impact, with a few numbers to support it.

In the next chapter, we will continue our journey of AI in banking. As a next step, the chapter will focus on time series analysis and forecasting. It will use various Python libraries, such as scikit-learn, to perform time series analysis. The chapter will also explain how to measure the accuracy of machine learning-based forecasting...