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


In this chapter, we learned about NLP and graph databases and we learned about the financial concepts that are required to analyze customer data. We also learned about an artificial intelligence technique called ensemble learning. We looked at an example where we predicted customer responses using natural language processing. Lastly, we built a chatbot to serve requests from customers 24/7. These concepts are very powerful. NLP is capable of enabling programs to interpret languages that humans speak naturally. The graph database, on the other hand, is helpful in designing highly efficient algorithms.

In the next chapter, we will learn about practical considerations to bear in mind when you want to build a model to solve your day-to-day challenges. In addition, we also want to look at the practical IT considerations when equipping data scientists with languages to interact with IT developers who put the algorithm to use in real life.