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

Exploring the Treynor-Black model

Due to the instability of the Markowitz mean-variance model in managing problems associated with multi-asset class portfolios, the Treynor-Black model was established. Treynor-Black's model fits the modern portfolio allocation approach where there are certain portfolios that are active and others that are passive. Here, passive refers to an investment that follows the market rate of return—not to beat the market average return but to closely follow the market return.

An active portfolio refers to the portfolio of investment in which we seek to deliver an above-market average return. The lower the market return with a market risk level, the higher the portfolio. Then, we allocate the total capital to an active portfolio. So, why take more risk if the market return is good enough? The Treynor-Black model seeks to allocate more weight to the asset that delivers a higher return/risk level out of the total risk/return level of the...