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

Building a bankruptcy risk prediction model

The bank, as the lender, needs to dictate the interest rates that will cover the cost of lending. The bank provides the interest rate by considering its cost of borrowing from others, plus the risk that the company might file for bankruptcy after taking the loan from the bank.

In this example, we shall assume the role of a banker to assess the probability of the borrowers becoming bankrupt. The data for this has been obtained from (, which provides us with the data for the bankruptcy predictions for different companies. The data available at this link was collected from the Emerging Markets Information Services (EMIS). The EMIS database has information about the emerging markets in the world.

EMIS analyzed bankrupt companies for the period 2000-2012 and operating companies for the period 2007-2013. After the data was collected, five classifications were made based on the forecasting period...