▶What You Will Learn
- Automate commercial bank pricing with reinforcement learning
- Perform technical analysis using convolutional layers in Keras
- Use natural language processing (NLP) for predicting market responses and visualizing them using graph databases
- Deploy a robot advisor to manage your personal finances via Open Bank API
- Sense market needs using sentiment analysis for algorithmic marketing
- Explore AI adoption in banking using practical examples
- Understand how to obtain financial data from commercial, open, and internal sources
▶Key Features
- Understand how to obtain financial data via Quandl or internal systems
- Automate commercial banking using artificial intelligence and Python programs
- Implement various artificial intelligence models to make personal banking easy
▶Who This Book Is For
This is one of the most useful artificial intelligence books for machine learning engineers, data engineers, and data scientists working in the finance industry who are looking to implement AI in their business applications. The book will also help entrepreneurs, venture capitalists, investment bankers, and wealth managers who want to understand the importance of AI in finance and banking and how it can help them solve different problems related to these domains. Prior experience in the financial markets or banking domain, and working knowledge of the Python programming language are a must.
▶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.
