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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

Cash flow projection using the Open Bank API

In the future, we will need robo-advisors to be able to understand our needs. The most basic step is to be able to pull our financial data from across banks. Here, we will assume that we are customers of consumer banking services from the US who are staying in the UK. We are looking for wealth planning for a family of four—a married couple and two kids. What we want is a robo-advisor to perform all our financial activities for us.

We will retrieve all the necessary transaction data from the Open Bank Project (OBP) API to forecast our expenditure forecasting via Open Bank API. The data that we will be using will be simulated data that follows the format specified in the OBP. We are not going to dive deep into any of the software technologies while focusing on building the wealth planning engine. The family description we'll be using has been obtained from the Federal Reserve (https://www.federalreserve...

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