Book Image

Hands-On Artificial Intelligence for Banking

By : Jeffrey Ng, Subhash Shah
Book Image

Hands-On Artificial Intelligence for Banking

By: Jeffrey Ng, Subhash Shah

Overview of this book

Remodeling your outlook on banking begins with keeping up to date with the latest and most effective approaches, such as artificial intelligence (AI). Hands-On Artificial Intelligence for Banking is a practical guide that will help you advance in your career in the banking domain. The book will demonstrate AI implementation to make your banking services smoother, more cost-efficient, and accessible to clients, focusing on both the client- and server-side uses of AI. You’ll begin by understanding the importance of artificial intelligence, while also gaining insights into the recent AI revolution in the banking industry. Next, you’ll get hands-on machine learning experience, exploring how to use time series analysis and reinforcement learning to automate client procurements and banking and finance decisions. After this, you’ll progress to learning about mechanizing capital market decisions, using automated portfolio management systems and predicting the future of investment banking. In addition to this, you’ll explore concepts such as building personal wealth advisors and mass customization of client lifetime wealth. Finally, you’ll get to grips with some real-world AI considerations in the field of banking. By the end of this book, you’ll be equipped with the skills you need to navigate the finance domain by leveraging the power of AI.

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Table of Contents (14 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Reviews
1
Section 1: Quick Review of AI in the Finance Industry
Section 1: Quick Review of AI in the Finance Industry
Free Chapter
2
The Importance of AI in Banking
The Importance of AI in Banking
What is AI?
Understanding the banking sector
Importance of accessible banking
Applications of AI in banking
Summary
3
Section 2: Machine Learning Algorithms and Hands-on Examples
Section 2: Machine Learning Algorithms and Hands-on Examples
4
Time Series Analysis
Time Series Analysis
Understanding time series analysis
M2M communication
The basic concepts of financial banking
AI modeling techniques
Demand forecasting using time series analysis
Procuring commodities using neural networks on Keras
Summary
5
Using Features and Reinforcement Learning to Automate Bank Financing
Using Features and Reinforcement Learning to Automate Bank Financing
Breaking down the functions of a bank
AI modeling techniques
Metrics of model performance
Building a bankruptcy risk prediction model
Funding a loan using reinforcement learning
Summary
6
Mechanizing Capital Market Decisions
Mechanizing Capital Market Decisions
Understanding the vision of investment banking
Basic concepts of the finance domain
AI modeling techniques
Finding the optimal capital structure
Providing a financial performance forecast using macroeconomic scenarios
Summary
7
Predicting the Future of Investment Bankers
Predicting the Future of Investment Bankers
Basics of investment banking
Understanding data technologies
Clustering models
Auto syndication for new issues
Identifying acquirers and targets
Summary
8
Automated Portfolio Management Using Treynor-Black Model and ResNet
Automated Portfolio Management Using Treynor-Black Model and ResNet
Financial concepts
Understanding the Markowitz mean-variance model
Exploring the Treynor-Black model
Portfolio construction using the Treynor-Black model
Predicting the trend of a security
Summary
9
Sensing Market Sentiment for Algorithmic Marketing at Sell Side
Sensing Market Sentiment for Algorithmic Marketing at Sell Side
Understanding sentiment analysis
Sensing market requirements using sentiment analysis
Network building and analysis using Neo4j
Summary
10
Building Personal Wealth Advisers with Bank APIs
Building Personal Wealth Advisers with Bank APIs
Managing customer's digital data
The Open Bank Project
Performing document layout analysis
Cash flow projection using the Open Bank API
Using invoice entity recognition to track daily expenses
Summary
11
Mass Customization of Client Lifetime Wealth
Mass Customization of Client Lifetime Wealth
Financial concepts of wealth instruments
Ensemble learning
Predict customer responses
Building a chatbot to service customers 24/7
Knowledge management using NLP and graphs
Summary
12
Real-World Considerations
Real-World Considerations
Summary of techniques covered
Impact on banking professionals, regulators, and government
How to come up with features and acquire the domain knowledge
IT production considerations in connection with AI deployment
Where to look for more use cases
Which areas require more practical research?
Summary
13
Other Books You May Enjoy
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Building a bankruptcy risk prediction model

The bank, as the lender, needs to dictate the interest rates that will cover the cost of lending. The bank provides the interest rate by considering its cost of borrowing from others, plus the risk that the company might file for bankruptcy after taking the loan from the bank.

In this example, we shall assume the role of a banker to assess the probability of the borrowers becoming bankrupt. The data for this has been obtained from data.world (https://data.world), which provides us with the data for the bankruptcy predictions for different companies. The data available at this link was collected from the Emerging Markets Information Services (EMIS). The EMIS database has information about the emerging markets in the world.

EMIS analyzed bankrupt companies for the period 2000-2012 and operating companies for the period 2007-2013. After the data was collected, five classifications were made based on the forecasting period...

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