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

Managing customer's digital data

In this era of digitization, there is no reason that money cannot be 100% transparent or that money transfers can't happen in real time, 24/7. Consumers have the right to their data as it represents their identity. Whether it is possible to or not, we should be consolidating our own data – realistically, this should be happening today and in the coming few years. It is best to consolidate our banking data in one place; for example, our frequent flyer mileage. The key point is that there shall be two tiers of data architecture – one for consolidation (including storage) and another for running the artificial intelligence services that will be used to analyze the data through the use of a smart device, also known as mobile applications. It can be painful to design an AI algorithm without understanding what is going on at the data consolidation layer.

Here, our data source could be identity data, bio/psychometric data...