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

The Open Bank Project

The world's most advanced policy that allows consumers to consolidate their own data is called the Open Banking Project. It started in the UK in 2016, following the European's Directive PSD2 – the revised Payment Services Directive ( This changed the competitive landscape of banks by lowering the entry barrier in terms of making use of banks' information for financial advisory reasons. This makes robo-advisors a feasible business as the financial data that banks contain is no longer segregated.

The challenge with this project is that the existing incumbent dominant banks have little incentive to open up their data. On the consumer side, the slowness in data consolidation impacts the economic values of this inter-connected network of financial data on banking services. This obeys Metcalfe's Law, which states that the value...