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

Clustering models

Before we start looking at the programming content, let's take a look at clustering models, since we will be using one in our first example.

Clustering seeks to group similar data points together. As a simple example, when there are three data points, each with one column, [1],[2],[6], respectively, we pick one point as the centroid that represents the nearby points; for example, with two centroids, [1.5] and [5], each represents a cluster: one with [1],[2] and another cluster with [6], respectively. These sample clusters can be seen in the following diagram:

When there are two columns for each data point, the distance between the actual data point and the centroid needs to consider the two columns as one data point. We adopt a measurement called Euclidean distance for this.

One of the key challenges of adopting clustering in banking is that it leads to clusters that are too large, which reduces the true...