Hands-On Artificial Intelligence for Banking: A practical guide to building intelligent financial applications using machine learning techniques

Description

Delve into the world of real-world financial applications using deep learning, artificial intelligence, and production-grade data feeds and technology with Python Key FeaturesUnderstand how to obtain financial data via Quandl or internal systemsAutomate commercial banking using artificial intelligence and Python programsImplement various artificial intelligence models to make personal banking easyBook Description 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. What you will learnAutomate commercial bank pricing with reinforcement learningPerform technical analysis using convolutional layers on KerasUse natural language processing (NLP) for predicting market responses and visualizing them using graph databasesDeploy a robot advisor to manage your personal finances via OpenbankSense market needs using sentiment analysis for algorithmic marketingExplore AI adoption in banking using practical examplesUnderstand how to obtain financial data from commercial, open, and internal sourcesWho This Book Is For This is one of the most useful artificial intelligence books for machine learning engineers, data engineers, and data scientists working in the finance industry who are looking to implement AI in their business applications. The book will also help entrepreneurs, venture capitalists, investment bankers, and wealth managers who want to understand the importance of AI in finance and banking and how it can help them solve different problems related to these domains. Prior experience in the financial markets or banking domain, and working knowledge of the Python programming language are a must. Cover Title Page Copyright and Credits Contributors Table of Contents Preface Section 1: Quick Review of AI in the Finance Industry Chapter 01: The Importance of AI in Banking What is AI? How does a machine learn? Software requirements for the implementation of AI Neural networks and deep learning Hardware requirements for the implementation of AI Graphics processing units Solid-state drives Modeling approach—CRISP-DM Understanding the banking sector The size of banking relative to the world's economies Customers in banking Importance of accessible banking Open source software and data Why do we need AI if a good banker can do the job? Applications of AI in banking Impact of AI on a bank's profitability Summary Section 2: Machine Learning Algorithms and Hands-on Examples Chapter 02: Time Series Analysis Understanding time series analysis M2M communication The role of M2M communication in commercial banking The basic concepts of financial banking The functions of financial markets – spot and future pricing Choosing between a physical delivery and cash settlement Options to hedge price risk AI modeling techniques Introducing the time series model – ARIMA Introducing neural networks – the secret sauce for accurately predicting demand Backpropagation Neural network architecture Using epochs for neural network training Scaling Sampling Demand forecasting using time series analysis Downloading the data Preprocessing the data Model fitting the data Procuring commodities using neural networks on Keras Data flow Preprocessing the data (in the SQL database) Importing libraries and defining variables Reading in data Preprocessing the data (in Python) Training and validating the model Testing the model Visualizing the test result Generating the model for production Summary Chapter 03: Using Features and Reinforcement Learning to Automate Bank Financing Breaking down the functions of a bank Major risk types Asset liability management Interest rate calculation Credit rating AI modeling techniques Monte Carlo simulation The logistic regression model Decision trees Neural networks Reinforcement learning Deep learning Metrics of model performance Metric 1 – ROC curve Metric 2 – confusion matrix Metric 3 – classification report Building a bankruptcy risk prediction model Obtaining the data Building the model Funding a loan using reinforcement learning Understanding the stakeholders Arriving at the solution Summary Chapter 04: Mechanizing Capital Market Decisions Understanding the vision of investment banking Performance of investment banking-based businesses Basic concepts of the finance domain Financial statements Real-time financial reporting Theories for optimizing the best structure of the firm What decisions need to be made? Financial theories on capital structure Total factor productivity to measure project values The cash flow pattern of a project Forecasting financial statement items AI modeling techniques Linear optimization The linear regression model Finding the optimal capital structure Implementation steps Downloading the data and loading it into the model Preparing the parameters and models Projections Calculating the weighted average cost of capital Constraints used in optimization Providing a financial performance forecast using macroeconomic scenarios Implementation steps Summary Chapter 05: Predicting the Future of Investment Bankers Basics of investment banking The job of investment bankers in IPOs Stock classification – style Investor classification Mergers and acquisitions Application of AI in M&A Filing obligations of listing companies Understanding data technologies Clustering models Auto syndication for new issues Solving the problem Building similarity models Building the investor clustering model Building the stock-clustering model Identifying acquirers and targets Summary Chapter 06: Automated Portfolio Management Using Treynor-Black Model and ResNet Financial concepts Alpha and beta returns in the capital asset pricing model Realized and unrealized investment returns Investment policy statements Asset class Players in the investment industry Benchmark – the baseline of comparison Investors are return-seeking Trend following fund Using technical analysis as a means to generate alpha Trading decisions – strategy Understanding the Markowitz mean-variance model Exploring the Treynor-Black model Introducing ResNet – the convolutional neural network for pattern recognition Pooling layer ReLU activation layer Softmax Portfolio construction using the Treynor-Black model Solution Downloading price data on an asset in scope Calculating the risk-free rate and defining the market Calculating the alpha, beta, and variance of error of each asset type Calculating the optimal portfolio allocation Predicting the trend of a security Solution Loading, converting, and storing data Setting up the neural network Loading the data to the neural network for training Saving and fine-tuning the neural network Loading the runtime data and running through the neural network Generating a trading strategy from the result and performing performance analysis Summary Chapter 07: Sensing Market Sentiment for Algorithmic Marketing at Sell Side Understanding sentiment analysis Sensing market requirements using sentiment analysis Solution and steps Downloading the data from Twitter Converting the downloaded tweets into records Performing sentiment analysis Comparing the daily sentiment against the daily price Network building and analysis using Neo4j Solution Using PDFMiner to extract text from a PDF Entity extractions Using NetworkX to store the network structure Using Neo4j for graph visualization and querying Summary Chapter 08: Building Personal Wealth Advisers with Bank APIs Managing customer's digital data The Open Bank Project Smart devices – using APIs with Flask and MongoDB as storage Understanding IPS Behavioral Analyzer – expenditure analyzers Exposing AI services as APIs Performing document layout analysis Steps for document layout analysis Using Gensim for topic modeling Vector dimensions of Word2vec Cash flow projection using the Open Bank API Steps involved Registering to use Open Bank API Creating and downloading demo data Creating a NoSQL database to store the data locally Setting up the API for forecasting Using invoice entity recognition to track daily expenses Steps involved Summary Chapter 09: Mass Customization of Client Lifetime Wealth Financial concepts of wealth instruments Sources of wealth: asset, income, and gifted Customer life cycle Ensemble learning Knowledge retrieval via graph databases Predict customer responses Solution Building a chatbot to service customers 24/7 Knowledge management using NLP and graphs Practical implementation Cross-checking and further requesting missing information Extracting the answer Sample script of interactions Summary Chapter 10: Real-World Considerations Summary of techniques covered AI modeling techniques Impact on banking professionals, regulators, and government Implications for banking professionals Implications for regulators Implications for 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? 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