Real-World Considerations

This chapter serves as the conclusion of the book. It wraps up the near-term banking world we will soon be living in. We will also add some useful tips on the considerations required to incorporate these AI engines in day-to-day production environments. This part corresponds to the business understanding step of the CRISP-DM, the approach for implementing any data mining project that we introduced in Chapter 1, The Importance of AI in Banking.

In this chapter, we will first summarize the techniques and knowledge that you learned throughout chapters 2 to 9, and then we will cover the forward-looking topics that will be an extension of our journey in banking. These are the topics and knowledge that will be covered:

• Summary of techniques covered
• Impact on banking professionals, regulators, and governments
• How to come up with features and acquire the domain knowledge...

Summary of techniques covered

Following along the business segments of banking, we have covered quite a lot of data and AI techniques. We have also gone through the models with minimal use of complex formula or jargons.

AI modeling techniques

We have covered statistical models, optimization, and machine learning models. Within machine learning models, we covered unsupervised, supervised, and reinforcement learning. In terms of the type of data the supervised learning models run on, we covered structured data, images, and languages (NLP). With regard to data processing, we have also covered a number of sampling and testing approaches that help us. We will now recap the AI modeling techniques covered in the book so far:

• Starting with supervised learning, this is a technique of labeling the input data prior to processing. The model is built to learn from the labels so that labeling will be done automatically with the next set of input data. Unsupervised...

Impact on banking professionals, regulators, and government

We have embarked on a long journey through commercial banking (Chapter 2, Time Series Analysis and Chapter 3, Using Features and Reinforcement Learning to Automate Bank Financing), investment banking (Chapter 4, Mechanizing Capital Market Decisions and Chapter 5, Predicting the Future of Investment Bankers), security sales and trading (Chapter 6, Automated Portfolio Management Using Treynor-Black Model and ResNet and Chapter 7, Sensing Market Sentiment for Algorithmic Marketing at Sell Side), and consumer banking (Chapter 8, Building Personal Wealth Advisers with Bank APIs and Chapter 9, Mass Customization of Client Lifetime Wealth) within the banking industry. This section accompanies a sample corporate client—Duke Energy—on its journey from commercial banking through to investment banking. In investment banking, we begin by introducing the investment communities who are on the buying side of the securities...

How to come up with features and acquire the domain knowledge

In all the chapters so far, we have not explained where we get this domain knowledge from. A typical AI project requires us to slip into the shoes of finance professionals. Where to begin? The following is a list that will help you:

• Textbook and training courses: The easiest path to follow is to follow how these professionals are trained. These courses contain the jargon, methodologies, and processes designed for the respective work type.
• Research papers in banking and finance: When it comes to finding the right data, research in finance and banking can prove to be a very valuable resource. It will not only show where to get the data; it will also showcase those features with strong powers of prediction. However, I normally do not get lost in the inconsistency of features across authors and markets. I simply include them all as far as possible—with the support of theory by researchers...

IT production considerations in connection with AI deployment

AI is just a file if the algorithm is not run in the day-to-day decision making of banks. The trend, of course, is to provide AI as a service to the software developers who write the program. This aside, there are a list of items that require the following:

• Encryption: Data is key and all the AI runs on sensitive data. Even though the data is anonymizedsomewhat with the scalers that change the data into the range of zero to one. Encryption remains important, however, in making sure that the encryption is in place when the data is in transit via the network and remains with an encrypted database.
• Load balancing: Handling requests with the correct capacity to handle, as well as creating sufficient servers to run the algorithm, are required. With the trend of going serverless with a cloud provider, the issue appears to have abated somewhat. However, the issue still remains; it is just being...

Where to look for more use cases

AI applications listed in this book largely focus on front-office banking services; the back-office processing jobs are not covered in any great detail. Stepping back, where should you look out for opportunities in case you wish to start your own project?

• Long hours; boring job: Boring means repetitive, and that's where machines thrive and data is rich.
• Large labor force: When it comes to business cases, it is easy to look for jobs that have high levels of employment. This means a huge business potential and easy-to-justify implementation. This constitutes a huge challenge for HR professionals.
• High pay: If we were to make finance accessible, can we make these highly paid jobs even more productive? In the case of investment bankers, security structurers, and hedge fund traders, how can their non-productive time be reduced?
• Unique dataset: If the dataset is not accessible to outsiders, the chance...

Which areas require more practical research?

In certain areas, this book has hit the ceiling of research, and these are the research areas that could help move AI applications in banking:

• Autonomous learning: AI will be replacing the works of AI engineers—given that the machine will be able to learn. Given the wealth of data nowadays, the machine will adopt its network structure itself.
• Transparent AI: As the machine starts to make decisions, humans will demand transparency as regards the decision-making process.
• Body of knowledge: In the case of expert knowledge, further research will look at how organizations can use AI to generate the body of knowledge. Practically, the Wikipedia form stored in BERT or any language model is not intended for human consumption or knowledge cultivation. And how do we squeeze the knowledge map to form a neural network, and vice versa?

• Data masking: To allow data to travel...

Summary

This book is designed to illustrate the current possibilities in terms of technology using public domain information. I hope that it helps to create a supply of talent and researchers to aid the industry. It also creates a base level of performance that any potential start-up needs to beat in order to be qualified. With all code in books now being open source, I am happy to be at the bottom of performance with regard to technological solutions!

There are too many books that are purely visionary, and there are also books that talk about the technical considerations without getting to the heart of the problem. There are books full of mathematics and formulas that deal with encrypted knowledge. This book is here to bridge the gap between the vision and the technical considerations. I believe that future generations deserve to study in an AI utopia before they come to change our world of work. For professionals in the industry and those wishing to upgrade their skills...