Machine Learning for Finance

Beginner's guide to explore machine learning in banking and finance (English Edition)

Saurav Singla

• Publisher: BPB Publications
• Year: 2020
• eBook ISBN: 9789389328622

Understand the essentials of Machine Learning and its impact in financial sector
Key Features

• Explore the spectrum of machine learning and its usage.
• Understand the NLP and Computer Vision and their use cases.
• Understand the Neural Network, CNN, RNN and their applications.
• Understand the Reinforcement Learning and their applications.
• Learn the rising application of Machine Learning in the Finance sector.
• Exposure to data mining, data visualization and data analytics. Description
  The fields of machining adapting, profound learning, and computerized reasoning are quickly extending and are probably going to keep on doing as such for a long time to come. There are many main impetuses for this, as quickly caught in this review. Now and again, the advancement has been emotional, opening new ways to deal with long-standing innovation challenges, for example, progresses in PC vision and picture investigation. The book demonstrates how to solve some of the most common issues in the financial industry. The book addresses real-life problems faced by practitioners on a daily basis. The book explains how machine learning works on structured data, text, and images. You will cover the exploration of Naïve Bayes, Normal Distribution, Clustering with Gaussian process, advanced neural network, sequence modeling, and reinforcement learning. Later chapters will discuss machine learning use cases in the finance sector and the implications of deep learning. The book ends with traditional machine learning algorithms.Machine Learning has become very important in the finance industry, which is mostly used for better risk management and risk analysis. Better analysis leads to better decisions which lead to an increase in profit for financial institutions. Machine Learning to empower fintech to make massive profits by optimizing processes, maximizing efficiency, and increasing profitability. What will you learn
  
• You will grasp the most relevant techniques of Machine Learning for everyday use.
• You will be confident in building and implementing ML algorithms.
• Familiarize the adoption of Machine Learning for your business need.
• Discover more advanced concepts applied in banking and other sectors today.
• Build mastery skillset in designing smart AI applications including NLP, Computer Vision and Deep Learning. Who this book is for
  Data Scientist, Machine Learning Engineers and Individuals who want to adopt machine learning in the financial domain. Practitioners are working in banks, asset management, hedge funds or working the first time in the finance domain. Individuals who want to learn about applications of machine learning in finance or individuals entering the fintech domain. Table of Contents
  1.Introduction
  2.Naive Bayes, Normal Distribution and Automatic Clustering Processes
  3.Machine Learning for Data Structuring
  4.Parsing Data Using NLP
  5.Computer Vision
  6.Neural Network, GBM and Gradient Descent
  7.Sequence Modeling
  8.Reinforcement Learning For Financial Markets
  9.Finance Use Cases
  10.Impact of Machine Learning on Fintech
  11.Machine Learning in Finance
  12.eKYC and Anti-Fraud Policy
  13.Uses of Data Mining and Data Visualization
  14.Advantages and Disadvantages of Machine Learning
  15.Applications of Machine Learning in Other Industries
  16.Ethical considerations in Artificial Intelligence
  17.Artificial Intelligence in Banking
  18.Common Machine Learning Algorithms
  19.Frequently Asked Questions About the Author
  SAURAV SINGLA —Saurav is a high performing Senior Data Scientist with 15 years of deep expertise in the application of analytics, business intelligence, machine learning, and statistics in multiple industries and 3 years of consulting experience and 5 years of managing a team in the data science field. He is a creative problem solver with a unique mix of technical, business, and research proficiency that lends itself to developing key strategies and solutions with a significant impact on revenue and ROI. He has working experience in machine learning, statistics, natural language processing, and deep learning with extensive use of Python, R, SQL & Tableau.
  LinkedIn Profile: https://www.linkedin.com/in/saurav-singla-5b412320/
  

CHAPTER 1

Introduction

Introduction

Machine learning is a branch of artificial intelligence that allows machines to learn and act the same way humans do. This allows them to come up with different kinds of output on their own.

Normally, machines are programmed to act a specific way depending on the actions that the user performs. This means that the user can also dictate what the outputs should be. Basically, humans still guide computers throughout the process.

In the case of machine learning, however, there is no need for the machine to be programmed in a specific way. Humans do not have to direct a specific path for the machine to take.

All it takes is the right data set. The machine will study the patterns in the data set. This will allow the machine to make its own decisions based on those patterns, and most (if not all) of it will be done autonomously or without human intervention.

Machine language (ML) advances from the investigation of free data available and advanced calculations on information and to give us meaningful insight. It is so inescapable today that a significant number of us likely use it a few times each day without even knowing it.

In prior phases of advancement in machine learning, the organizations that most profited from the new field were data firms and online organizations that saw and took advantage of the huge amount of information available. The capacity to give genuinely necessary information and data spoke to an unmistakable first mover’s bit of leeway for these organizations. While the first movers for quite a while were the huge victors, their favorable position won’t last any longer as efficiency levels out. The development of Analytics 3.0 is a distinct advantage, because of the scope of business issues that savvy mechanization—a blend of artificial intelligence (AI) and machine learning—can unravel is expanding each day. At this stage, each firm in any industry can benefit from clever computerization. Organizations that invest promptly in AI can increase their long-haul profits. To press home these advantages, organizations must reevaluate how they can benefit from the information with regards to Analytics.

Enormous changes are hatching in the showcasing scene, and these movements are, to a great extent, down to make ML powerful. Such is its effect that 97% of pioneers accept the eventual fate of promoting will comprise of keen advertisers working in a joint effort with AI-based mechanization elements.

Machine learning methods are utilized to tackle a large group of different issues, and organizations continue to profit a lot as we veer towards a universe of hyper-joined information, channels, substance, and setting. For the advanced advertising group, machine learning is tied in with discovering bits of prescient information in the influxes of organized and unstructured data and utilizing them to further their potential benefit.

The ability to react rapidly and precisely to changes in client conduct is basic in this day and age, and hence the need for AI. In this chapter, we investigate the advancements in machine learning that are being utilized successfully, and its potential uses in different organizations. AI is known as man-made brainpower. It very well may be viewed as part of ML. The historical backdrop of AI can enable us to comprehend it better, so let us do a quick review.

Structure

In this chapter, we will cover the following topics:

• How machines are taught.
• Factors contributing to the success of machine learning.
• Machine learning and artificial intelligence.
• Machine learning and deep learning.
• Machine learning and statistics.
• Machine learning and data mining.
• Machine learning in finance.
• Importance of machine learning in finance.
• Robo-warning.
• How to utilize machine learning in finance.
• Utilize outsider machine learning arrangements.
• Development and combination.
• How is machine learning used today.

Objective

After studying this chapter, you should be able to do the following:

• Understand the process of how machines are taught, and the relation between machine learning, AI, deep learning, data mining, and statistics.
• Understand the application of machine learning in the finance domain.

How machines are taught

The entire process can be considered complex. There are also different approaches applied. However, for the sake of translating the basics and to give an overview of what happens during the process, here are the three basic parts of machine learning:

1. Data input: The information or data sets to be used are fed to the machine. These data can come in the form of SQL databases, text files, spreadsheets, or anything similar.
2. Data abstraction: At this stage, the data is labeled and represented as required. It is then analyzed using the algorithm chosen for the process. This is where the basic learning process happens.
3. Generalization: Once the learning is completed, the machine starts to develop its own insights. From these insights, it comes up with an output. Not all machine learning processes require an output, though. In some cases, the goal is only to cluster the data together.

Note that, in this process, the goal is always to create a better version of the machine. After the process ends, it is expected that the machine becomes smarter regardless of whether there is an output or not.

Factors contributing to the success of machine learning

Although the computers used in the process are definitely more advanced than the regular ones, of course, there is still a margin of error to be considered. Because of this, there is a need to zero in on what could increase the chances of success.

These factors come to mind when it comes to ensuring success in machine learning:

• How well the generalization goes
• How well the machine can apply what it learned to practical use

When these two areas are done well, expect that the results will show a success. These are also the key elements in ensuring that a future course of action can be predicted and planned for.

Machine learning and artificial intelligence

Machine learning and AI and are closely related, but it is highly inappropriate to interchange the two terms. These are different concepts.

Artificial intelligence is a more general term that covers a number of applications. It involves the ability of machines to mimic the behavior of humans. This also includes the ability of machines to make intelligent decisions on their own.

Machine learning falls under artificial intelligence as it also gives the machine the ability to think. But where artificial intelligence covers all concepts involving machines acting and thinking the same way as humans do, machine learning focuses on a machine’s ability to learn on its own. As mentioned in the earlier definition of the term, this ability comes without the need for specific programming.

Machine learning and deep learning

Just like artificial intelligence, deep learning is yet another concept that is closely related to machine learning but is still essentially a different application altogether.

Deep learning, in essence, involves the creation of artificial neural networks. These networks use algorithms to learn and make decisions on their own.

Machine learning and statistics

Statistics, as you probably know, deal with data coming from either an entire population or from samples drawn from that population. From there, you can carry out analyses and draw inferences.

Statistical techniques are used in a number of applications like conditional probability, regression, standard deviation, variance, and a lot more.

So how do statistics fit into machine learning?

Although machine learning is part of computer science and statistics is part of mathematics, they work hand in hand in delivering results for artificial intelligence.

One example is the way your emails are segregated in your inbox. Let’s say you want to determine which emails are important and which ones should be recognized as spam. In this case, a machine learning algorithm called Naive Bayes will observe past spam emails to come up with a way to identify new emails coming in as spam.

Naive Bayes uses a form of statistical technique that is the basis for conditional probability. This technique will be discussed in a later chapter.

Machine learning and data mining

Again, it’s the use of data in both machine learning and data mining that makes people think that these two concepts are the same or are closely related.

Basically, data mining is a term that describes the process of searching through data for specific information. Machine learning, on the other hand, is only concerned with one thing – completing the task it was asked to do using the algorithms applied.

What’s the difference?

If someone is teaching you how to play the guitar, that’s a process that describes machine learning. If someone asks you to look for the best guitar performances ever, then that’s data mining.

Machine learning in finance

In finance, machine learning can do something amazing, even though there is no enchantment behind it (well, perhaps only a tad). In any case, the accomplishment of a machine learning undertaking depends on the structure of the foundation, gathering appropriate data sets, and applying the correct calculations.

Machine learning is making noteworthy advances in the finance-related administration industry. We should perceive any reason why budgetary organizations should keep in mind the advantage of machine learning, what arrangements they can actualize with machine learning and artificial intelligence, and how precisely they can apply this innovation. Most finance-related administration organizations are as yet not prepared to identify the genuine incentive of this innovation for the following reasons:

• Businesses have unrealistic desires and expectations towards machine learning solutions and their incentive for their associations.
• R&D in machine learning is expensive.
• The lack of data science/machine learning specialists is another significant concern.
• Financial savvy people are not deft enough with regards to refreshing the information.

Importance of machine learning in finance

Despite the difficulties, numerous budgetary organizations, as of now, utilize this innovation. They do it for a lot of valid justifications:

• Reduced operational costs because of procedure computerization.
• Increased incomes because of better profitability and upgraded client encounters.
• Better consistency and fortified security.

There is a wide scope of open-source machine learning algorithms and applications/ tools that fit extraordinarily with budgetary information. Also, established budgetary administration organizations have significant subsidies that they can stand to spend on cutting-edge registering machinery. Because of the enormous volumes of transactional information, machine learning can improve numerous parts of the budgetary environment. This is the reason such a significant number of financial organizations are investing vigorously in R&D on artificial intelligence. In the case of slowpokes, it can be expensive to disregard artificial intelligence and machine learning.

Robo-warning

Robo-advisors are currently in demand in the finance sector. As of now, there are two noteworthy uses of machine learning in the warning area.

Portfolio board is an online executives’ administration tool that uses calculations and insights to assign, oversee, and streamline customers’ advantages. Clients enter their present monetary resources and objectives, state, sparing a million dollars by the age of fifty. A robo-advisor at that point assigns the present resources crosswise over venture openings depending on hazard inclinations and ideal objectives.

Several online protection administrators use robo-advisors to prescribe customized protection plans to a specific client. Clients select robo-advisors over close-to-home financial advisors because of lower charges, just as customized and aligned proposals.

How to utilize machine learning in finance

Despite the considerable number of points of interest in machine learning and artificial intelligence, even organizations with deep pockets frequently experience serious difficulties extricating the genuine incentive from this innovation. Financial administrations need to use machine learning sensibly as they do not have ...

Table of contents

• Cover Page
• Title Page
• Copyright Page
• Dedication Page
• About the Author
• About the Reviewer
• Acknowledgement
• Preface
• Errata
• Table of Contents
• 1. Introduction
• 2. Naive Bayes, Normal Distribution, and Automatic Clustering
• 3. Machine Learning for Data Structuring
• 4. Parsing Data Using NLP
• 5. Computer Vision
• 6. Neural Network, GBM, and Gradient Descent
• 7. Sequence Modeling
• 8. Reinforcement Learning for Financial Markets
• 9. Finance Use Cases
• 10. Impact of Machine Learning on FinTech
• 11. Machine Learning in Finance
• 12. eKYC and Anti-Fraud Policy
• 13. Uses of Data Mining and Data Visualization
• 14. Advantages and Disadvantages of Machine Learning
• 15. Applications of Machine Learning in Other Industries
• 16. Ethical Considerations in Artificial Intelligence
• 17. Artificial Intelligence in Banking
• 18. Common Machine Learning Algorithms
• 19. Frequently Asked Questions
• Index