- 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.
Hands-On Artificial Intelligence for Banking
Hands-On Artificial Intelligence for Banking
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.
Table of Contents (14 chapters)
Section 1: Quick Review of AI in the Finance Industry
The Importance of AI in Banking
Section 2: Machine Learning Algorithms and Hands-on Examples
Time Series Analysis
Using Features and Reinforcement Learning to Automate Bank Financing
Mechanizing Capital Market Decisions
Predicting the Future of Investment Bankers
Automated Portfolio Management Using Treynor-Black Model and ResNet
Sensing Market Sentiment for Algorithmic Marketing at Sell Side
Building Personal Wealth Advisers with Bank APIs
Mass Customization of Client Lifetime Wealth
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