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

Understanding sentiment analysis

Sentiment analysis is a technique in which text mining is done for contextual information. The contextual information is identified and extracted from the source material. It helps businesses understand the sentiment for their products, securities, or assets. It can be very effective to use the advanced techniques of artificial intelligence for in-depth research in the area of text analysis. It is important to classify the transactions around the following concepts:

  • The aspect of security the buyers and sellers care about
  • Customers' intentions and reactions concerning the securities

Sentiment analysis is known to be the most common text analysis and classification tool. It receives an incoming message or transaction and classifies it depending on whether the sentiment associated with the transaction is positive, negative, or neutral. By using the sentiment analysis technique, it is possible to...