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

AI modeling techniques

In the following sections, we will introduce the Autoregressive Integrated Moving Average (ARIMA), the most traditional type of forecasting model. We will also introduce a neural network model. ARIMA is a class of statistical models that is used to forecast a time series using past values. ARIMA is an acronym for the following:

  • AR (autoregression): Autoregression is a process that takes previous data values as inputs, applies this to the regression equation, and generates resultant prediction-based data values.
  • I (integrated): ARIMA uses an integrated approach by using differences in observations to make the time series equally spaced. This is done by subtracting the observation from an observation on a previous step or time value.
  • MA (moving average): A model that uses the observation and the residual error applied to past observations.

Introducing the time series model – ARIMA ...