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

What this book covers

Chapter 1, The Importance of AI in Banking, explains what AI is and discusses its applications in banking. This chapter also provides a detailed introduction to banking as a sector, the complexity of banking processes, and diversification in banking functions.

Chapter 2, Time Series Analysis, covers time series analysis. This chapter explains time series analysis in detail with examples and explains how the Machine-to-Machine (M2M) concept can be helpful in the implementation of time series analysis.

Chapter 3, Using Features and Reinforcement Learning to Automate Bank Financing, covers reinforcement learning. It also covers different AI modeling techniques using examples, as well as the business functions of the bank in the context of examples.

Chapter 4, Mechanizing Capital Market Decisions, discusses the basic financial and capital market concepts. We will look at how AI can help us optimize the best capital structure by running risk models and generating sales forecasts using macro-economic data. The chapter also covers important machine learning modeling techniques such as learning optimization and linear regression.

Chapter 5, Predicting the Future of Investment Bankers, introduces AI techniques followed by auto-syndication for new issues. We will see how capital can be obtained from interested investors. In the latter section of the chapter, we will cover the case of identifying acquirers and targets—a process that requires science to pick the ones that need banking services.

Chapter 6, Automated Portfolio Management Using Treynor-Black Model and ResNet, focuses on the dynamics of investors. The chapter discusses portfolio management techniques and explains how to combine them with AI to automate decision-making when buying assets.

Chapter 7, Sensing Market Sentiment for Algorithmic Marketing at Sell Side, focuses on the sell side of the financial market. The chapter provides details about securities firms and investment banks. This chapter also discusses sentiment analysis and covers an example of building a network using Neo4j.

Chapter 8, Building Personal Wealth Advisers with Bank APIs, focuses on consumer banking. The chapter explains the requirements of managing the digital data of customers. The chapter also explains how to access open bank APIs and explains document layout analysis.

Chapter 9,Mass Customization of Client Lifetime Wealth, explains how to combine data from the survey for personal data analysis. The chapter also discusses Neo4j, which is a graph database. In this chapter, we will build a chatbot to serve customers 24/7. We will also look at an example entailing the prediction of customer responses using natural language processing, Neo4j, and cipher languages to manipulate data from the Neo4j database.

Chapter 10, Real World Considerations, serves as a summary of the AI modeling techniques covered in the previous chapters. The chapter also shows where to look for further knowledge of the domain.