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

IT production considerations in connection with AI deployment

AI is just a file if the algorithm is not run in the day-to-day decision making of banks. The trend, of course, is to provide AI as a service to the software developers who write the program. This aside, there are a list of items that require the following:

  • Encryption: Data is key and all the AI runs on sensitive data. Even though the data is anonymizedsomewhat with the scalers that change the data into the range of zero to one. Encryption remains important, however, in making sure that the encryption is in place when the data is in transit via the network and remains with an encrypted database.
  • Load balancing: Handling requests with the correct capacity to handle, as well as creating sufficient servers to run the algorithm, are required. With the trend of going serverless with a cloud provider, the issue appears to have abated somewhat. However, the issue still remains; it is just being...