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

Building a chatbot to service customers 24/7

When we interact with a robot, we expect it to understand and speak to us. The beauty of having a robot work for us is that it could serve us 24 hours a day throughout the week. Realistically, chatbots nowadays interact poorly with customers, and so we should try to break down the components of these chatbots to raise the bar to a higher standard. For an application-type development, you could use Google Assistant, Amazon's Alexa, or IBM Watson. But for learning purposes, let's break down the components and focus on the key challenges:

The chatbot performs two operations at a high level. One is to convert an input from voice to text, and another one is to translate an output from text to voice. Both of these operations involve extracting the entity and understanding the intent. In this example, the resulting text is an entity, whereas the meaning of the text is an intent. It represents...