Preface – Hands-On Artificial Intelligence for Banking


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 implementations 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 learning about the importance of AI, 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 the 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.

Who this book is for

  • Students in banking or technologies are the target audience for the book as there is a vacuum in the publication space regarding how AI technologies are used in the banking and finance industry. This book aims to give a reasonably useful list of use cases commonly known in the public domain and provide real sample code that is easy to implement. With this book, I am trying to expound important use cases but not to give you a machine learning model that you can put in use the next day.
  • For bankers who are already in the field, I'm sure this book will help you build your services in the long run. It may encourage you to challenge anything that is obviously different from the way a start-up would function if you were ever to start one. Changes need to be made inside-out and outside-in. For IT managers within banks, this will give you a concrete code base on how the technologies can be applied and which open source libraries are available. Perhaps you are not convinced about developing everything in-house for production purposes. This book serves as a code base for any experiments you wish to launch.
  • For investors, aspiring start-up founders, or MBA students, this is the industry participant's effort to share our problems and challenges with you. Please make banking better by creating better products that fit our needs. I hope your investment journey is smooth.
  • ForFinTech start-upswho have started businesses in this field, this book provides you with the floor and encourages you to open source and collaborate on industry-wide challenges, rather than close sourcing your work.
  • Forregulators, this serves as a guide on what is happening in banking. Your job is instrumental in the adoption of AI in banking—while at the same time, you could challenge models and decisions and encourage research by opening up more data for analysis.
  • As a Chartered Finance Analyst (CFA), it is my duty to make investment in AI more effective and efficient. The best way to do that is to have hands-on knowledge about technology. If the company/investment project is just copying and pasting codealong witha fancy renowned school name, just ignore that and spend your energy somewhere better.
  • For research analysts and management consultants looking at the banking industry, this is a bottom-up approach to guide you through how exactly we can change banks to be able to run better for a higher return on equity.
  • Last but not least, AI hardware and software developers and researchers, this can perhaps help you look at common opportunities for your research topics in case you need ideas.

What this book covers

Chapter 1, The Importance of AI in Banking, explains what AI is and discusses its applications in banking. This chapter also provides a detailed introduction to banking as a sector, the complexity of banking processes, and diversification in banking functions.

Chapter 2, Time Series Analysis, covers time series analysis. This chapter explains time series analysis in detail with examples and explains how the Machine-to-Machine (M2M) concept can be helpful in the implementation of time series analysis.

Chapter 3, Using Features and Reinforcement Learning to Automate Bank Financing, covers reinforcement learning. It also covers different AI modeling techniques using examples, as well as the business functions of the bank in the context of examples.

Chapter 4, Mechanizing Capital Market Decisions, discusses the basic financial and capital market concepts. We will look at how AI can help us optimize the best capital structure by running risk models and generating sales forecasts using macro-economic data. The chapter also covers important machine learning modeling techniques such as learning optimization and linear regression.

Chapter 5, Predicting the Future of Investment Bankers, introduces AI techniques followed by auto-syndication for new issues. We will see how capital can be obtained from interested investors. In the latter section of the chapter, we will cover the case of identifying acquirers and targets—a process that requires science to pick the ones that need banking services.

Chapter 6, Automated Portfolio Management Using Treynor-Black Model and ResNet, focuses on the dynamics of investors. The chapter discusses portfolio management techniques and explains how to combine them with AI to automate decision-making when buying assets.

Chapter 7, Sensing Market Sentiment for Algorithmic Marketing at Sell Side, focuses on the sell side of the financial market. The chapter provides details about securities firms and investment banks. This chapter also discusses sentiment analysis and covers an example of building a network using Neo4j.

Chapter 8, Building Personal Wealth Advisers with Bank APIs, focuses on consumer banking. The chapter explains the requirements of managing the digital data of customers. The chapter also explains how to access open bank APIs and explains document layout analysis.

Chapter 9,Mass Customization of Client Lifetime Wealth, explains how to combine data from the survey for personal data analysis. The chapter also discusses Neo4j, which is a graph database. In this chapter, we will build a chatbot to serve customers 24/7. We will also look at an example entailing the prediction of customer responses using natural language processing, Neo4j, and cipher languages to manipulate data from the Neo4j database.

Chapter 10, Real World Considerations, serves as a summary of the AI modeling techniques covered in the previous chapters. The chapter also shows where to look for further knowledge of the domain.

To get the most out of this book

Before you get started, I assume that you are running Ubuntu 16.04LTS Desktop or above and have done your Python 101 course. Knowledge of how to install the relevant software packages is assumed and will not be covered in this book.

Three database engines (SQLite, MongoDB, and Neo4j) are used in this book. Please make sure that you have them installed.

Regarding data sources, we will get data from ( and a paid subscription to Quandl (Sharadar Core US Equities Bundle ( for chapters 4 and 5, and Sharadar Fund Prices ( for chapters 6 and 7), Twitter's Premium Search ( for chapter 7, and the Open Bank Project ( for chapter 8.

Download the example code files

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Download the color images

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here:

Conventions used

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "The function will download the price data of any given ticker in the SHARADAR database from Quandl."

A block of code is set as follows:

#list of key intent, product and attribute
product_list = ['deposit','loan']
attribute_list = ['pricing','balance']
intent_list = ['check']
print('loading nlp model')
nlp = spacy.load('en_core_web_md')

Any command-line input or output is written as follows:

          sudo cp dataset.csv /var/lib/Neo4j/import/edge.csv
sudo cp product.csv /var/lib/Neo4j/import/product.csv
sudo cp customer.csv /var/lib/Neo4j/import/customer.csv

Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "An asset class is defined as a group of assets that bear similar characteristics."

Warnings or important notes appear like this.
Tips and tricks appear like this.

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