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

Performing document layout analysis

In ML, there is a discipline called document layout analysis. It is indeed about studying how humans understand documents. It includes computer vision, natural language processing, and knowledge graphs. The end game is to deliver an ontology that can allow any document to be navigated, similar to how word processors can, but in an automated manner. In a word processor, we have to define certain words that are found in headers, as well as within different levels of the hierarchy – for example, heading level 1, heading level 2, body text, paragraph, and so on. What's not defined manually by humans is sentences, vocabulary, words, characters, pixels, and so on. However, when we handle the images taken by a camera or scanner, the lowest level of data is a pixel.

Steps for document layout analysis

In this section, we will learn how to perform document layout analysis. The steps are as follows:

  1. Forming...