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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

IT production considerations in connection with AI deployment

AI is just a file if the algorithm is not run in the day-to-day decision making of banks. The trend, of course, is to provide AI as a service to the software developers who write the program. This aside, there are a list of items that require the following:

  • Encryption: Data is key and all the AI runs on sensitive data. Even though the data is anonymizedsomewhat with the scalers that change the data into the range of zero to one. Encryption remains important, however, in making sure that the encryption is in place when the data is in transit via the network and remains with an encrypted database.
  • Load balancing: Handling requests with the correct capacity to handle, as well as creating sufficient servers to run the algorithm, are required. With the trend of going serverless with a cloud provider, the issue appears to have abated somewhat. However, the issue still remains; it is just being...
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