Machine Learning for Finance

By Saurav Singla

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.

• Language: English
• Publisher: BPB Online LLP
• Release date: Nov 26, 2020
• ISBN: 9789389328639

Book preview

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:

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.

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.

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 a clear idea of how information science functions and how to utilize it.

Consistently, they experience difficulties such as the absence of business KPIs. This brings about ridiculous estimates and depletes spending plans. It isn’t sufficient to have a reasonable programming foundation set up (although that would be a decent start). It takes an unmistakable vision, strong and specialized ability, and assurance to convey an important machine learning advancement venture.

When you have a decent comprehension of how this innovation will accomplish your business targets, continue with plan approval. This is an undertaking for information researchers. They research the plan and help you formulate reasonable KPIs and make sensible appraisals.

Note that you need all of the information gathered by now. Else, you would require an information specialist to gather and tidy up this information.

Contingent upon a specific use case and business conditions, financial organizations can pursue various ways to implement machine learning. How about we look at them?

Artificial intelligence came into spotlight on getting meaningful insight from raw data using natural language processing.

Frequently, financial organizations start their artificial intelligence ventures to acknowledge they need legitimate information building. Max Nechepurenko, a senior information researcher at N-iX, said the following:

When building up a [data science] arrangement, I’d prompt utilizing the Occam’s razor rule, which means not overcomplicating. Most organizations that go for artificial intelligence, in truth, need to concentrate on strong information designing, applying measurements to the collected information, and representation of that information.

Simply applying factual models to handle well-organized data would be sufficient for a bank to resolve different bottlenecks and wasteful aspects in its activities.

What are the instances of such bottlenecks? They could be long queues at a particular branch, dull undertakings that can be dispensed with, wasteful HR exercises, blemishes in the banking application, etc.

In addition, the greatest challenge of any data science venture is building an organized biological system of stages that gather siloed data from several sources like CRMs, websites, social platform, spreadsheets, and that’s just the beginning.

Before applying any calculations, the data needs to be properly organized and tidied up. Then, that data can be transformed into experiences. ETL (extricating, changing, and stacking) and further cleaning of the data represents around 80% of the time for artificial intelligence undertaking.

Utilize outsider machine learning arrangements

Regardless of whether or not your organization chooses to use machine learning in its up and coming task, you don’t need to develop new calculations and models. Most machine learning undertakings manage issues that have just been tended to. Tech goliaths like Google, Microsoft, Amazon, and IBM sell machine learning programming as an administration tool.

These out-of-the-crate arrangements are, as of now, prepared to explain different business errands. On the off chance that your task covers similar use cases, do you accept that your group can outflank calculations from these tech titans with goliath R&D focuses?

One genuine model is Google’s Recommendation AI ecommerce tool. That product applies to different spaces, and it is recommended to check on whether it fits your business case.

A machine learning architect can execute the framework by focusing on your particular data and business space. The expert needs to extract the data from various sources, transform it for the specific framework, get the outcomes, and visualize the discoveries.

The trade-offs are the absence of authority over the external framework and constrained arrangement adaptability. Plus, AI calculations don’t fit into each utilization case. Ihar Rubanau, a senior data researcher at N-iX, said the following:

An all-inclusive machine learning algorithm doesn’t exist, yet. Data researchers need to alter and tweak calculations before applying them to various business cases crosswise over various areas.

So, if a current arrangement from Google comprehends a particular assignment in your specific area, you ought to likely utilize it. If not, go for custom improvement and mix.

Development and combination

Building up a machine learning arrangement without any preparation is one of the most dangerous, costliest, and tedious alternatives. In any case, this might be the best way to apply machine learning innovation to some business cases.

Machine learning tasks focus on a special need in a specific specialty, and they require a top-to-bottom examination. In the event that there are no arrangements available to take care of those particular issues, external AI programming is probably going to deliver incorrect outcomes. You will most likely need to depend extensively on the open-source AI libraries from Google and the preferences. Current machine learning tasks are generally about applying the existing best in class libraries to a specific space and use case.

How is machine learning used today

Financial services

Experian is one of the, if not the, biggest credit reference agency in the world. They store an amazing amount of data about every individual in their records. They can convey any financial institution about your purchases, court cases, and other relevant information that could help banks figure out how your finances stand.

Knowing how much data their system goes through for every loan and mortgage application being processed, it isn’t surprising to learn that they have machine learning helping them out. Basically, machine learning sorts through all that data and tells them whether a certain individual is a risky bet, or whether a loan application is worth approving.

American Express is another financial giant that takes advantage of machine learning. With over 110 million active AmEx cards, how do they manage to keep track of fraudulent activity?

You guessed it — through machine learning. By using the right data sets and algorithms, AmEx has the ability to notice discrepancies in spending habits among card users. This allows them to detect potential fraud in real-time.

Conclusion

In this chapter, we discussed the process of how machines are taught. We also discussed the relation between machine learning, artificial intelligence, statistics, deep learning, and data mining. We also discussed the application of machine learning in the finance domain.

In the next chapter, we will learn about normal distribution and automatic clustering.

CHAPTER 2

Naive Bayes, Normal Distribution, and Automatic Clustering

Introduction

The seemingly unstoppable interest in machine learning stems from the same variables that data mining and Bayesian analysis are applied more. The underlying factors contributing to this popularity are increasing amounts and varieties of data, cheaper and more effective computational processing, and cheap data storage. To get an idea of how important machine learning is in our daily lives, it’s easier to pinpoint which part of our advanced way of life hasn’t been affected by it. Every aspect of human life is influenced by smart machines that are designed to expand human capabilities and improve efficiency. Artificial intelligence and machine learning are