Delve into the world of real-world financial applications using deep learning, artificial intelligence, and production-grade data feeds and technology with Python
Key FeaturesRemodeling 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 learnThis 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.
Show moreDelve into the world of real-world financial applications using deep learning, artificial intelligence, and production-grade data feeds and technology with Python
Key FeaturesRemodeling 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 learnThis 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.
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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 works as head of technology at AIMDek Technologies Pvt. Ltd. He is an experienced solutions architect with over 12 years of experience. He holds a degree in information technology. He is an advocate of open source development and its utilization in solving critical business problems at a reduced cost. His interests include microservices, data analysis, machine learning, AI, and databases. He is an admirer of quality code and test-driven development (TDD). His technical skills include, but are by no means limited to, translating business requirements into scalable architecture, designing sustainable solutions, and project delivery. He is a coauthor of MySQL 8 Administrator's Guide and Hands-On High Performance with Spring 5.
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