Hands-On Artificial Intelligence for Banking: A practical guide to building intelligent financial applications using machine learning techniques (Paperback or Softback)

Delve into the world of real-world financial applications using deep learning, artificial intelligence, and production-grade data feeds and technology with Python

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

Book Description

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.

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

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.

Table of Contents

1. The Importance of Artificial Intelligence in Finance Industry
2. Using Time Series Analysis to Automate Client Procurement
3. Using Features and Reinforcement Learning to Automate Bank Financing
4. Mechanizing Capital Market Decisions
5. Predicting the Future of Investment Bankers
6. Automated Portfolio Management Using Treynor Black Model and RestNet
7. Sensing Market Sentiment for Algorithmic Marketing at Sell-Side
8. Building Personal Wealth Advisers with Bank API
9. Mass Customization of Client Lifetime Wealth
10. Real World Considerations

Jeffrey Ng, CFA, works at Ping An OneConnect Bank (Hong Kong) Limited as Head of FinTech Solutions. His mandate is to advance the use of AI in banking and financial ecosystems. Prior to this, he headed up the data lab of BNP Paribas Asia Pacific, which constructed an AI and data analytics solution for business, and was the vice-chair of the French Chamber of Commerce's FinTech Committee in Hong Kong. In 2010, as one of the pioneers in applying client analytics to investment banking, he built the analytics team for the bank. He has undertaken AI projects in retail and commercial banks with PwC Consulting and GE Money. He graduated from Hong Kong Polytechnic University in computing and management and holds an MBA in finance from the Chinese University of Hong Kong.

Subhash Shah is an experienced solution architect. With 14 years of experience in software development, he works as an independent technical consultant now. He is an advocate of open source development and its utilization in solving critical business problems. His interests include Microservices architecture, Enterprise solutions, Machine Learning, Integrations and Databases. He is an admirer of quality code and test-driven development (TDD). His technical skills include translating business requirements into scalable architecture and designing sustainable solutions. He is a co-author of Hands-On High Performance with Spring 5, Hands-On AI for Banking and MySQL 8 Administrator's Guide. He has also been a technical reviewer for other books.