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

Funding a loan using reinforcement learning

Assuming that our role is the head of the bank, it becomes important to figure out the cost of funding the loan. The problem we are solving is comprised of three parties (or as we call them, agents)—the bank, depositors, and borrowers. To begin with, we assume that there is only one bank but many depositors and borrowers. The depositors and borrowers will be created through randomized generated data.

When it comes to simulating different behaviors for these parties in machine learning, each of these is called an agent or an instance of an object. We need to create thousands of agents, with some being depositors, some being borrowers, one being a bank, and one being a market. These represent the collective behavior of competing banks. Next, we will describe the behavior of each type of agent.

Let's say we assume the role of treasurer of the bank or head of the treasury. The job of the head of the treasury is...