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
Mass Customization of Client Lifetime Wealth

In the previous chapter, we learned how to manage the digital data of customers. We also covered the Open Bank Project and the Open Bank API. In addition, we learned about document layout analysis and looked at an example of projecting the cash flow for a typical household. Then, we looked at another example of how to track daily expenses using invoice entity recognition.

In this chapter, we will learn how to combine data from a survey for personal data analysis. We will learn techniques such as Neo4j, which is a graph database. We will build a chatbot to serve customers 24/7. We will also learn how to predict customer responses using NLP and Neo4j with the help of an example. After this, we will learn how to use cypher languages to manipulate data from the Neo4j database.

The following topics will be covered in this chapter:

  • Financial concepts of...