,Table of Contents
1. Preface
1. Who this book is for
2. What this book covers
3. To get the most out of this book
4. Download the example code files
5. Download the color images
6. Conventions used
7. Get in touch
8. Reviews
1. Section 1: Quick Review of AI in the Finance Industry
1. The Importance of AI in Banking
1. What is AI?
1. How does a machine learn?
2. Software requirements for the implementation of AI
1. Neural networks and deep learning
3. Hardware requirements for the implementation of AI
1. Graphics processing units
2. Solid-state drives
4. Modeling approach—CRISP-DM
2. Understanding the banking sector
1. The size of banking relative to the world's economies
2. Customers in banking
3. Importance of accessible banking
1. Open source software and data
2. Why do we need AI if a good banker can do the job?
4. Applications of AI in banking
1. Impact of AI on a bank's profitability
5. Summary
2. Section 2: Machine Learning Algorithms and Hands-on Examples
2. Time Series Analysis
1. Understanding time series analysis
2. M2M communication
1. The role of M2M communication in commercial banking
3. The basic concepts of financial banking
1. The functions of financial markets – spot and future pricing
1. Choosing between a physical delivery and cash settlement
2. Options to hedge price risk
4. AI modeling techniques
1. Introducing the time series model – ARIMA
, 2. Introducing neural networks – the secret sauce for accurately predicting
demand
1. Backpropagation
2. Neural network architecture
3. Using epochs for neural network training
4. Scaling
5. Sampling
5. Demand forecasting using time series analysis
1. Downloading the data
2. Preprocessing the data
3. Model fitting the data
6. Procuring commodities using neural networks on Keras
1. Data flow
1. Preprocessing the data (in the SQL database)
2. Importing libraries and defining variables
3. Reading in data
4. Preprocessing the data (in Python)
5. Training and validating the model
6. Testing the model
7. Visualizing the test result
8. Generating the model for production
7. Summary
3. Using Features and Reinforcement Learning to Automate Bank Financing
1. Breaking down the functions of a bank
1. Major risk types
2. Asset liability management
3. Interest rate calculation
4. Credit rating
2. AI modeling techniques
1. Monte Carlo simulation
2. The logistic regression model
3. Decision trees
4. Neural networks
5. Reinforcement learning
6. Deep learning
3. Metrics of model performance
1. Metric 1 – ROC curve
2. Metric 2 – confusion matrix
3. Metric 3 – classification report
4. Building a bankruptcy risk prediction model
1. Obtaining the data
2. Building the model
5. Funding a loan using reinforcement learning
1. Understanding the stakeholders
2. Arriving at the solution
, 6. Summary
4. Mechanizing Capital Market Decisions
1. Understanding the vision of investment banking
1. Performance of investment banking-based businesses
2. Basic concepts of the finance domain
1. Financial statements
1. Real-time financial reporting
2. Theories for optimizing the best structure of the firm
1. What decisions need to be made?
2. Financial theories on capital structure
3. Total factor productivity to measure project values
4. The cash flow pattern of a project
5. Forecasting financial statement items
3. AI modeling techniques
1. Linear optimization
2. The linear regression model
4. Finding the optimal capital structure
1. Implementation steps
1. Downloading the data and loading it into the model
2. Preparing the parameters and models
3. Projections
4. Calculating the weighted average cost of capital
5. Constraints used in optimization
5. Providing a financial performance forecast using macroeconomic scenarios
1. Implementation steps
6. Summary
5. Predicting the Future of Investment Bankers
1. Basics of investment banking
1. The job of investment bankers in IPOs
2. Stock classification – style
3. Investor classification
4. Mergers and acquisitions
5. Application of AI in M&A
6. Filing obligations of listing companies
2. Understanding data technologies
3. Clustering models
4. Auto syndication for new issues
1. Solving the problem
1. Building similarity models
2. Building the investor clustering model
3. Building the stock-clustering model
5. Identifying acquirers and targets
6. Summary
1. Preface
1. Who this book is for
2. What this book covers
3. To get the most out of this book
4. Download the example code files
5. Download the color images
6. Conventions used
7. Get in touch
8. Reviews
1. Section 1: Quick Review of AI in the Finance Industry
1. The Importance of AI in Banking
1. What is AI?
1. How does a machine learn?
2. Software requirements for the implementation of AI
1. Neural networks and deep learning
3. Hardware requirements for the implementation of AI
1. Graphics processing units
2. Solid-state drives
4. Modeling approach—CRISP-DM
2. Understanding the banking sector
1. The size of banking relative to the world's economies
2. Customers in banking
3. Importance of accessible banking
1. Open source software and data
2. Why do we need AI if a good banker can do the job?
4. Applications of AI in banking
1. Impact of AI on a bank's profitability
5. Summary
2. Section 2: Machine Learning Algorithms and Hands-on Examples
2. Time Series Analysis
1. Understanding time series analysis
2. M2M communication
1. The role of M2M communication in commercial banking
3. The basic concepts of financial banking
1. The functions of financial markets – spot and future pricing
1. Choosing between a physical delivery and cash settlement
2. Options to hedge price risk
4. AI modeling techniques
1. Introducing the time series model – ARIMA
, 2. Introducing neural networks – the secret sauce for accurately predicting
demand
1. Backpropagation
2. Neural network architecture
3. Using epochs for neural network training
4. Scaling
5. Sampling
5. Demand forecasting using time series analysis
1. Downloading the data
2. Preprocessing the data
3. Model fitting the data
6. Procuring commodities using neural networks on Keras
1. Data flow
1. Preprocessing the data (in the SQL database)
2. Importing libraries and defining variables
3. Reading in data
4. Preprocessing the data (in Python)
5. Training and validating the model
6. Testing the model
7. Visualizing the test result
8. Generating the model for production
7. Summary
3. Using Features and Reinforcement Learning to Automate Bank Financing
1. Breaking down the functions of a bank
1. Major risk types
2. Asset liability management
3. Interest rate calculation
4. Credit rating
2. AI modeling techniques
1. Monte Carlo simulation
2. The logistic regression model
3. Decision trees
4. Neural networks
5. Reinforcement learning
6. Deep learning
3. Metrics of model performance
1. Metric 1 – ROC curve
2. Metric 2 – confusion matrix
3. Metric 3 – classification report
4. Building a bankruptcy risk prediction model
1. Obtaining the data
2. Building the model
5. Funding a loan using reinforcement learning
1. Understanding the stakeholders
2. Arriving at the solution
, 6. Summary
4. Mechanizing Capital Market Decisions
1. Understanding the vision of investment banking
1. Performance of investment banking-based businesses
2. Basic concepts of the finance domain
1. Financial statements
1. Real-time financial reporting
2. Theories for optimizing the best structure of the firm
1. What decisions need to be made?
2. Financial theories on capital structure
3. Total factor productivity to measure project values
4. The cash flow pattern of a project
5. Forecasting financial statement items
3. AI modeling techniques
1. Linear optimization
2. The linear regression model
4. Finding the optimal capital structure
1. Implementation steps
1. Downloading the data and loading it into the model
2. Preparing the parameters and models
3. Projections
4. Calculating the weighted average cost of capital
5. Constraints used in optimization
5. Providing a financial performance forecast using macroeconomic scenarios
1. Implementation steps
6. Summary
5. Predicting the Future of Investment Bankers
1. Basics of investment banking
1. The job of investment bankers in IPOs
2. Stock classification – style
3. Investor classification
4. Mergers and acquisitions
5. Application of AI in M&A
6. Filing obligations of listing companies
2. Understanding data technologies
3. Clustering models
4. Auto syndication for new issues
1. Solving the problem
1. Building similarity models
2. Building the investor clustering model
3. Building the stock-clustering model
5. Identifying acquirers and targets
6. Summary
