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

Identifying acquirers and targets

There has been a long history of corporate finance research in the field of acquirers and targets, and our challenge is to apply this rich body of research to the real world. Hedge funds have been applying these research findings as merger arbitrage, and M&A bankers have always had their eyes on scoring and assessing the market on a regular basis (for example, reading the morning news).

In this chapter, we will assume that you are an M&A banker looking for organization opportunities. To optimize our time allocation, we can allocate our time better by focusing on clients that can close the deal. Therefore, we will use a model to predict the probability of us being the acquirers or targets in M&A.

The current new generation of investment bankers should use automated financial modeling tools. Over time, data can be captured, and then prediction capability can be added to assist bankers in financial modeling. The current...