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Hands-On Artificial Intelligence for Banking

You're reading from  Hands-On Artificial Intelligence for Banking

Product type Book
Published in Jul 2020
Publisher Packt
ISBN-13 9781788830782
Pages 240 pages
Edition 1st Edition
Languages
Authors (2):
Jeffrey Ng Jeffrey Ng
Profile icon Jeffrey Ng
Subhash Shah Subhash Shah
Profile icon Subhash Shah
View More author details

Table of Contents (14) Chapters

Preface 1. Section 1: Quick Review of AI in the Finance Industry
2. The Importance of AI in Banking 3. Section 2: Machine Learning Algorithms and Hands-on Examples
4. Time Series Analysis 5. Using Features and Reinforcement Learning to Automate Bank Financing 6. Mechanizing Capital Market Decisions 7. Predicting the Future of Investment Bankers 8. Automated Portfolio Management Using Treynor-Black Model and ResNet 9. Sensing Market Sentiment for Algorithmic Marketing at Sell Side 10. Building Personal Wealth Advisers with Bank APIs 11. Mass Customization of Client Lifetime Wealth 12. Real-World Considerations 13. Other Books You May Enjoy

How to come up with features and acquire the domain knowledge

In all the chapters so far, we have not explained where we get this domain knowledge from. A typical AI project requires us to slip into the shoes of finance professionals. Where to begin? The following is a list that will help you:

  • Textbook and training courses: The easiest path to follow is to follow how these professionals are trained. These courses contain the jargon, methodologies, and processes designed for the respective work type.
  • Research papers in banking and finance: When it comes to finding the right data, research in finance and banking can prove to be a very valuable resource. It will not only show where to get the data; it will also showcase those features with strong powers of prediction. However, I normally do not get lost in the inconsistency of features across authors and markets. I simply include them all as far as possible—with the support of theory by researchers...
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