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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
Toc

Table of Contents (14) Chapters close

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

Which areas require more practical research?

In certain areas, this book has hit the ceiling of research, and these are the research areas that could help move AI applications in banking:

  • Autonomous learning: AI will be replacing the works of AI engineers—given that the machine will be able to learn. Given the wealth of data nowadays, the machine will adopt its network structure itself.
  • Transparent AI: As the machine starts to make decisions, humans will demand transparency as regards the decision-making process.
  • Body of knowledge: In the case of expert knowledge, further research will look at how organizations can use AI to generate the body of knowledge. Practically, the Wikipedia form stored in BERT or any language model is not intended for human consumption or knowledge cultivation. And how do we squeeze the knowledge map to form a neural network, and vice versa?
  • Data masking: To allow data to travel...
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