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

Financial concepts of wealth instruments

In this section, we will be answering a few questions asked by a consumer bank's marketers. Then, we will look at another important model development technique—ensemble learning—which will be useful in combining predictions from different models.

Sources of wealth: asset, income, and gifted

One of the most common tasks in retail banking customer analytics is to retrieve additional data that helps us to explain the customers' investment behavior and patterns. No doubt we will know the response of the customers, but the work of a model is to find out why they respond as they do. Surprisingly, there is a lot of aggregated information concerning the behaviors of individuals, such as census data. We can also find data from social media, where users use social media for authentication. The relevant social media information can then be chained together with individual-level transactional data that we observed...