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Hands-On Artificial Intelligence for Banking

By Jeffrey Ng , Subhash Shah
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  1. Free Chapter
    The Importance of AI in Banking
About 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.
Publication date:
July 2020
Publisher
Packt
Pages
240
ISBN
9781788830782

 
The Importance of AI in Banking

Artificial intelligence, commonly known as AI, is a very powerful technology. A thoughtful implementation of AI can do wonders in automating business functions. AI has the power to transform a wide variety of industries through its application. As computer systems have evolved over time, they have become very powerful. Consequently, machines have also become very powerful and can perform many complicated tasks with ease. For example, Optical Character Recognition (OCR) is a task that even personal computers can perform easily with the help of software. However, OCR requires intelligence to translate dots from an image into characters. So, in an ideal case, OCR will be considered an area of AI. However, because of the power of machines, we tend to not consider it as an application of AI.

In this chapter, our focus is to understand what AI is and its application in banking. Banking is an industry...

 

What is AI?

AI, also known as machine intelligence, is all about creating machines that demonstrate the intelligence that is usually displayed by humans in the form of natural intelligence. John McCarthy coined the termartificial intelligence in 1955.

AI has witnessed two winters so far: once in the 1970s with the reduction of funding by the Defense Advanced Research Projects Agency or DARPA (https://www.darpa.mil/), then known as ARPA, and another time with the abandonment of an expert system by major IT corporates such as Texas Instruments (http://www.ti.com/) and Xerox (https://www.xerox.com/).

In a way, AI aids in the process of transferring decision making from humans to machines, based on predefined rules. In the field of computer science, AI is also defined as the study of intelligent agents. An intelligent agent is any device that learns from the environment and makes decisions based on what it has learned to maximize the probability of achieving its predefined...

 

Understanding the banking sector

The banking sector is defined as a devoted economy for holding specific types of financial assets using methods that will make said assets grow financially over a period of time. Banking sectors are governed by rules imposed by governments or similar bodies.

Renowned author and financial consultantStephen Valdez described in his work, Introduction to Global Financial Markets (please visit https://www.macmillanihe.com/companion/Valdez-Introduction-To-Global-Financial-Markets-8th-Edition/about-this-book/), the different types of banking in the global financial markets. These are commercial banking, investment banking, securities firms, asset management, insurance, and shadow banking.

These types of banking are required to fulfill the needs of a wide variety of customers, ranging from large organizations to individual customers. The following is a description of these various types of banking based on the needs of customers:

...
 

Importance of accessible banking

Like electricity and water, banking should be made as widely and easily available as utilities. Only when we make banks efficient can we make them accessible and have them benefit the highest number of people possible. Banking is a service that is provided to make the best use of capital/money to generate returns for those whosaveand/orthose who need the capital to have a more productive life at an agreed risk and return.

What we want to do is to be consistent with Robert J. Shiller's sentiment in his book,Finance and the Good Society, where he indicates the necessity of information technology in finance to help achieve our goals. A step further would be to leverage open source methods and applications to solve the accessibility challenges of the banking industry. Open source software solutions tend to be cost-effective, robust, and secure.

To make banking accessible, one of the most important things to do is to have a lot...

 

Applications of AI in banking

According to theMcKinsey Global Institute (https://www.mckinsey.com/~/media/mckinsey/industries/advanced%20electronics/our%20insights/how%20artificial%20intelligence%20can%20deliver%20real%20value%20to%20companies/mgi-artificial-intelligence-discussion-paper.ashx), outof 13 industries, financial services ranked third in AI adoption, followed by the high-tech, telecommunications, and automotive and assembly industries.

As theMckinsey report does not mention the use case in banking, with a bit of research, perhaps we can take a look at the four ways in which AI creates values, as shown in the following list:

  • Project: Forecast and anticipate demand, improve sourcing, and reduce inventory (capital).
  • Produce: Provide services at a lower cost or higher quality.
  • Promote: Provide offers for the right price with the right message for the right customers at the right time.
  • Provide: Rich, personal, and...
 

Summary

We began this chapter by explaining what AI is all about. AI is the technology that makes machines perform tasks that humans can do, such as weather prediction, budget forecasting, and more. It enables machines to learn based on data. We looked at the various techniques of AI, such as machine learning and deep learning. Later, we looked at the complex processes of the banking domain. If we can automate them, we can reduce costs in the banking sector. We also learned about the importance of accessible banking. Later, we looked at the application of AI in the banking sector and its positive impact, with a few numbers to support it.

In the next chapter, we will continue our journey of AI in banking. As a next step, the chapter will focus on time series analysis and forecasting. It will use various Python libraries, such as scikit-learn, to perform time series analysis. The chapter will also explain how to measure the accuracy of machine learning-based forecasting...

About the Authors
  • Jeffrey Ng

    Jeffrey Ng, CFA, works at Ping An OneConnect Bank (Hong Kong) Limited as Head of FinTech Solutions. His mandate is to advance the use of AI in banking and financial ecosystems. Prior to this, he headed up the data lab of BNP Paribas Asia Pacific, which constructed an AI and data analytics solution for business, and was the vice-chair of the French Chamber of Commerce's FinTech Committee in Hong Kong. In 2010, as one of the pioneers in applying client analytics to investment banking, he built the analytics team for the bank. He has undertaken AI projects in retail and commercial banks with PwC Consulting and GE Money. He graduated from Hong Kong Polytechnic University in computing and management and holds an MBA in finance from the Chinese University of Hong Kong.

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  • Subhash Shah

    Subhash Shah is an experienced solution architect. With 14 years of experience in software development, he works as an independent technical consultant now. He is an advocate of open source development and its utilization in solving critical business problems. His interests include Microservices architecture, Enterprise solutions, Machine Learning, Integrations and Databases. He is an admirer of quality code and test-driven development (TDD). His technical skills include translating business requirements into scalable architecture and designing sustainable solutions. He is a co-author of Hands-On High Performance with Spring 5, Hands-On AI for Banking and MySQL 8 Administrator's Guide. He has also been a technical reviewer for other books.

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