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

Predict customer responses

So far, we have not talked about the day-to-day marketing activity of the bank. Now, we have finally come to look at how marketing prospects are determined. Even though each customer is unique, they are still handled by algorithms in the same way.

In this example, you will assume the role of a data scientist tasked with the marketing of a term deposit product. We are going to train the model to predict the marketing campaign for the term deposit. Data pertaining to the bank's internal data regarding customers and their previous responses to the campaign is obtained from the Center for Machine Learning and Intelligent Systems (https://archive.ics.uci.edu/ml/datasets/bank+marketing), the Bren School of Information and Computer Science, and the University of California, Irvine. Survey information about personal wealth is obtained from the US Census Bureau (https://www.census.gov/data/tables/time-series/demo/income-poverty/historical-income...