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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

Building a chatbot to service customers 24/7

When we interact with a robot, we expect it to understand and speak to us. The beauty of having a robot work for us is that it could serve us 24 hours a day throughout the week. Realistically, chatbots nowadays interact poorly with customers, and so we should try to break down the components of these chatbots to raise the bar to a higher standard. For an application-type development, you could use Google Assistant, Amazon's Alexa, or IBM Watson. But for learning purposes, let's break down the components and focus on the key challenges:

The chatbot performs two operations at a high level. One is to convert an input from voice to text, and another one is to translate an output from text to voice. Both of these operations involve extracting the entity and understanding the intent. In this example, the resulting text is an entity, whereas the meaning of the text is an intent. It represents...

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