Machine Learning In Finance
It focuses on developing data and making it useful for learning for themselves.
What is Machine Learning (in Finance)?
Machine learning uses algorithms to make decisions by generalization or assuming patterns from a given data set.
ML algorithms build a model from sample data, also known as training data, to make decisions or predictions without being programmed.
ML performs better than standard statistical approaches when dealing with many variables and when relationships are non-linear.
The term Machine Learning(ML) was first used in 1959 in a paper published in the IBM Journal of Research and Development authored by Arthur Samuel, mentioning the use of ML in the game of checkers back in 1959.
Since then, it has come a long way. Finally, almost six decades later, we embraced the concept and started using it daily.
One might ask why the machine needs to learn or why we can't give a set of instructions for it to perform.
There are many reasons for the Machine Learning concept because, in some tasks, we might be able to give inputs and outputs. Still, not a precise explanation of their relationship, so we need to find the pattern in which it performs in the desired manner.
It is also possible that among piles of data are hidden certain relationships and correlations that we need, so it is one of the ways to find that.
Machine Learning is a part of Artificial Intelligence and Computer Science that makes use of data available to imitate how humans think and perceive and reach gradual accuracy.
It focuses on developing data and making it helpful in learning for themselves.
The primary goal is to make machines learn by themselves without the help of human beings and adjust and make changes using the data provided.
It is used as the core of their business models for some companies, as Netflix uses it for suggestions, and Google uses it for its search engine.
More than 67% of businesses are using it in their daily activities, according to a recent survey.
Businesses are already using it for activities like recommendation algorithms, Image analysis, object detection, fraud detection, Automatic chatbots or chatbots, self-driving cars, and medical imaging and diagnostics.
How is machine learning used in finance?
In finance, it is used for making investment predictions, algorithmic trading, financial advisory, risk management, decision-making, and secure transactions.
Using ML in finance creates better security and compliance, better productivity, and enhanced learning, and provides less operational costs too.
Investment Firms are using technology at every stage of their operations- from improving their understanding of clients to uncovering new sources of alpha and executing trades more efficiently and effectively.
The growing volume of data and its variety has inspired the rapid growth of data science and its application in finance. These applications are at each asset and wealth management value chain step.
Chatbots answer basic retirement savings questions, or they can be used to generate alpha used in security selection.
These techniques provide better order of flow of management tools that, in return, reduce the costs of implementing portfolio decisions.
Even if technology has many challenges, many financial companies use it and take advantage of it. Some of the good reasons for using it are as follows:
- Reduced operational cost
- Increased revenues
- Better productivity
- Enhanced customer experience
- Better compliance and reinforced security
What is the future of machine learning in finance?
The global machine learning market is forecast to grow to $8.81 billion in 2022, producing a compound annual growth rate of 44%, according to a report by Markets and Markets Research.
The increased availability and declined cost of complex models has encouraged the financial industry to embrace Machine Learning to have a competitive business edge. Some of the applications will be:
1. Portfolio Management
So the portfolios are now designed using various parameters, while risk and return are the important ones. An initial portfolio allocation is done, and the algorithm is trained to make necessary changes to remain at its optimum level even if the market environment changes.
These AI-enabled Robo advisors are used because of their superior adaptability and optimization skills compared to their human counterparts.
2. Algorithmic Trading
Algorithmic trading autonomously uses the algorithm with less human intervention.
ML and Artificial Intelligence(AI) have made Algorithmic Trading capable of creating advanced trading strategies and gaining deep insights into stock prices and market movements.
Hedge funds and other financial organizations use these to make real-time high-frequency and high-volume trading decisions for critical applications.
3. Detection and Analysis of Fraud
Machine Learning Smart algorithms play a vital role in detecting and analyzing fraud.
One of the ways it is done is by comparing every transaction with the account history, and the spectral activities like out-of-the-state purchases or large sum withdrawals raise a flag, and the client is notified of the same.
There is an increased level of fraud and leakage of sensitive information because of the increased amount of data being stored and communicated online.
4. Financial Chatbots
AI chatbots can provide many effective ways to interact with clients and automate many financial institutions' activities.
In many cases, these chatbots are recommended for providing business decisions that are efficient and optimum.
And these chatbots allow the financial institution to provide superior customer satisfaction.
5. Risk Management
Risk management includes deciding how much to lend to the customer, how to improve customer compliance, and how the risk associated with a model can be minimized.
While traditional software measures the creditworthiness of an individual by analyzing the static information from the loan application or other financial reports, ML can go a step further by analyzing the applicant's financial status as current market trends may modify it.
6. Underwriting and Credit Scores
Underwriting in banks and other financial institutions. Data scientists train models on numerous profiles with hundreds of data entries for each customer. so that models can perform underwriting and credit scoring techniques in real life.
| AI | ML |
|---|---|
| It is human intelligence demonstrated by machines to perform simple to complex tasks. | It allows machines to understand and learn without being programmed to do so. |
| AI is programmed to make machines do things more humanely or smartly. | While ML is to make machines think and act as humans do. |
| It is based on human intelligence. | It is mostly based on probability. |
| Its objective is to maximize success. | Its objective is to maximize accuracy. |
| The two main subsets of it are machine learning and deep learning. | Deep learning is the most important subset of it. |
| The scope is wide | Its scope is limited |
| It is developing an intelligent system capable of performing various complex tasks. | It is working on creating machines that can only perform the tasks for which they have been trained. |
| This system is concerned with increasing its chances of success. | It is primarily concerned with accuracy and pattern recognition. |
| The main applications include Siri, customer service via chatbots, online gaming, intelligent humanoid robots, etc. | Some applications include online recommender systems, Google search algorithms, Facebook auto friend tagging suggestions, etc. |
Types of Machine Learning
Amid the extreme utilization of artificial brainpower and AI, separating the kinds of AI becomes essential.
As everybody sees everything unexpectedly, for computer users, this can be about the presentation of these various sorts of ML in a few applications.
For software engineers making such applications, it is fundamental to have some familiarity with the various kinds of ML to establish an appropriate learning climate and figure out the reason for making such applications.
The three significant perceived classes of AI are managed learning, unaided learning, and semi-directed learning.
1. Supervised Learning
Supervised learning uses labeled datasets for training the model, which can be used for purposes such as classification and regression.
Supervised learning allows you to collect or produce data from previous experiences, which helps you optimize performance criteria using these experiences.
2. Unsupervised Learning
Unsupervised learning uses datasets that are not labeled or classified in the dataset. Unsupervised learning is mainly used for finding patterns in the data. It finds all kinds of data in strange ways.
Also helps you to find patterns for categorization. And it is easier to get unlabeled from a computer than labeled data.
3. Semi-supervised learning
Semi-supervised learning provides a comfortable middle ground between supervised and unsupervised learning. It uses a more minor labeled data set to guide categorization and feature extraction from the more extensive, unlabeled data set during training.
This learning solves the problem of not having enough labeled data to train a supervised learning algorithm (or not being capable of labeling enough data).
Supervised Vs. Unsupervised Learning
Some of the differences are:
| Supervised Learning | Unsupervised Learning |
|---|---|
| Supervised learning uses labeled input and output data. | Unsupervised learning does not use labeled input and output data. |
| The algorithm learns from the training datasets, makes predictions on the data, and adjusts for the right answer. | These algorithms work independently to discover the inherent structure of unlabeled data. |
| The goal is to predict the outcomes of new data. | The goal is to get insight from a large amount of data. |
| It is ideal for spam detection, sentiment analysis, weather forecasting, and price detection. | It is ideal for anomaly detection, recommendation engines, customer personas, and medical imaging. |
| It is a simple method of ML, typically done using programs like R and Python. | It needs powerful tools for working with a large amount of unlabeled data. |
| One of the drawbacks, it is time-consuming and requires expertise to label inputs and outputs. | It has a drawback as it produces wildly inaccurate results unless it has human interactions to validate them. |
What is Overfitting and Underfitting
Overfitting and underfitting are two of the most critical topics in ML because it gives a fair idea about what ML is capable of.
Overfitting is when we feed more data than required in a statistical model.
So when we add more data than required, it catches noisy data or inaccurate values in the data.
Overfitting is a problem that occurs when the evaluation and assessment of machine learning algorithms on training examples differs from that of unseen data.
The causes of overfitting are:
- Low bias and high variance
- Overly complicated models
- The amount of training data
- Techniques for reducing overfitting include boosting training data:
- Model complexity should reduce,
- Early termination during the training phase,
- Regularization of the Ridge and Lasso, and
- To combat overfitting in neural networks, use dropout.
Underfitting is when we try to avoid overfitting of data and try to feed less data underfitting is defined as a model that neither performs well on training data nor generalizes to new data.
Reasons for underfitting include:
- Low variance and high bias,
- The size of the dataset used is insufficient,
- The model is overly simplistic,
- Training data has not been cleaned and contains noise.
- Techniques for reducing underfitting include Increasing the model's complexity and Expanding the number of features by performing feature engineering.
- Taking out the noise from the information improving results, and boosting the number of periods or the duration of training (however, it might lead to difficulty in capturing dominant trends).
When defined mathematically, overfitting means training data is more accurate than the testing data, and underfitting means both training and testing data have poor performance.
Neural Networks
An artificial neural network learning algorithm or neural network is a computational learning system that uses a network of functions to understand and translate data of one form into the desired output, usually in another format.
The concept of a neural network is taken from human biology and how the brain's neuron works.
Neural networks are being used in many ways now. Some of them are speech and image recognition, email spam filtering, finance, and medical diagnosis.
A neural network with more than three layers with inputs and outputs can be termed a deep learning algorithm. On the other hand, a neural network with two or three layers is called a basic neural network.
In the finance world, neural networks are used to develop processes such as time-series forecasting, algorithmic trading, derivative pricing, securities classification, and credit risk modeling.
Most studies have shown that the more training datasets are available, the better the prediction quality improves.
The similarities between the biological neural system and the artificial neural system are that both the neural networks acquire knowledge by learning and that the neural networks' ability is stored within the inter-neuron connection strengths known as synaptic weights.
Additional neural network uses are as follows:
- Currency exchange prediction
- Futures prediction
- Bonds Rating
- Debt risk assessment
- Credit approval
- Bank theft
- Bank failure prediction
Deep Learning
Deep Learning is a machine learning technique that teaches computers to do what comes naturally to human beings, like learning by example.
In deep learning, a computer learns to perform classification tasks directly from images, voices, or signs. As a result, it can achieve an extremely high level of accuracy, even better than human performance.
The neural networks stacked or have many layers are called "Deep Learning.
The layers are termed nodes, and it is loosely based on human brain neurons that are these nodes that light up when sufficient stimuli are encountered.
Unlike most traditional machine learning algorithms, deep learning performs automatic feature extraction without human intervention.
The Deep Learning Network trained on labeled data can be applied to unlabeled data, giving it access to many more inputs than machine learning nets.
Even though Deep Learning can be used as a function approximator, it can be coupled with more AI methods that can perform more complex tasks.
Reinforcement Learning
Reinforcement learning is training machine learning models to make a sequence of decisions and learn to achieve a goal in an uncertain and potentially complex environment.
Reinforcement Learning is all about making decisions sequentially. In simple words, we could use the current output to be based on the previous input and the next input to be based on the output of the last input.
This learning is independent, so we give labels to sequences of dependent decisions.
These learning algorithms aim to find the middle ground between the exploration of data, like unsupervised learning, and the usage of that knowledge, such as supervised learning. It is mainly used in game theory and robotics.
This learning enables an agent to learn in an interactive environment by trial and error methods using feedback from its own experience and actions.
Reinforcement Learning uses rewards and punishments as signals for positive and negative behavior.
Q-Learning and SARSA( State Action Reward State Action) are two of the most commonly used model-free RL Algorithms. However, they differ in exploration strategies while their exploitation strategy is similar.
While the Q-Learning method is an off-policy method in which the agent learns value derived from another policy, SARSA is an on-policy method where it learns value derived from its policy.
Advantages of Machine Learning
Many of today's most innovative technologies are powered by machine learning, from the predictive analytics engines that generate Amazon shopping recommendations to the artificial intelligence technology used during countless security and antivirus applications worldwide.
Every coin has two sides, each with its properties and features. It's time to reveal ML's many faces, a very powerful tool with the potential to change the way things work.
1. Easy identification of patterns and trends
The fact that ML has various ways to identify patterns and trends is quite familiar to us. It is used to identify trends and patterns that are not apparent to humans.
For instance, various online shopping sites use these data to analyze them, find the most searched and purchased items, and make advertisements relevant.
Facebook uses this by learning the patterns users surf and studying it and then uses those patterns to make the data available according to their interest to hook them up for hours.
2. Continuous improvement
As data grows, accuracy and efficiency also grow with subsequent training if you need to make a weather forecast model. The amount of data fed in improves the accuracy and predictions faster.
3. Automation
These algorithms make decision-making tasks to be automated, which in return helps developers spend more time using them. As it gets the ability to learn more, it improves, lets them make decisions, and gets the ability to evolve along the way.
One of the examples is google using voice and finding the results users want and antivirus software which detects viruses. For instance, if you are employing ML technology to develop an algorithm, you must develop an interface that suggests improvements and changes that human beings must implement.
4. A wide range of applications
Machine Learning is used in almost every field of life, from defense to education and healthcare. Applications like GPS Tracking for traffic, Email spam filtering, text prediction, spell check and correction, etc., are a few used these days widely.
It is considered best for education because education is dynamic, and smart classes, distance learning, and e-learning for students have increased significantly in recent years.
It will act as a teacher, keeping students up to date on world events. The same thing occurs in shopping or e-business; people need to stay updated so they are shown current world trends.
Disadvantages of Machine Learning
Nothing is absolutely flawless in this world, even technology. Similarly, while advanced technology has a wide range of applications, it also has some flaws and disadvantages.
Some are serious flaws that are worse than human flaws. Some of them are
1. Impact on jobs
After all, as technology advances, the work done by human hands will be shifted to machines. Similarly, Machine Learning may cause human jobs to be fully automated but not short.
Artificial Intelligence has been used in all sorts of businesses, not just in Tech.
Reducing human interference has caused the loss of many job opportunities; a widely used example is the use of chatbots; this innovation has caused the loss of jobs for people in this industry.
2. Privacy
Sometimes privacy in this AI-dominated world could be a thing of the past because it needs a huge amount of data to analyze our behavior.
Because AI/ML can predict an infinite number of actions ahead of you predicated on your predetermined actions, it has no privacy because it mimics your future behavior along each step's path.
3. Bias and discrimination
Bias means a deviation from the standard, sometimes necessary to identify the existence of some statistical patterns in the data or language used. Discrimination is treating an individual separately in a group.
Bias and discrimination aren't limited to the human resources function; they can be found in several applications, from facial recognition software to social media algorithms.
4. Accountability
Accountability is someone being held responsible for AI-made decisions.
Accountability in AI requires both the function of guiding action formed by beliefs and guiding actions and the function of explanation, placing context in a broader sense and classifying them with moral values.
As Artificial Intelligence becomes a part of every walk of life, to ensure it upholds ethical principles, we need accountability, responsibility, and transparency.
Conclusion
Machine Learning mimics the ability of human beings to think and learn and improve their performance with experience.
By performance, we refer to various cognitive abilities that humans perceive. A brief note is presented above.
It has become quite certain that this concept will have great consequences and uses in all aspects of living and hence worth developing.
The increasing volume and diversity of data, as well as the perceived increasing economic value of insights derived from these data, have fueled rapid growth in data science.
Mathematics, computer science, and business analytics are all part of this advanced field. It has theoretical as well as practical implications for asset management.
It also strikes a new direction that relies on learning- from basic learning functions that map relationships between variables to advanced neural networks that mimic physical processes that absorb, order, and adapt to information.
Its goal is to extract knowledge from massive quantities of data by using known examples to ascertain a fundamental structure in the data.
The focus is on generating structures or forecasts without the use of humans. A simple way to think about ML algorithms is to "discover the pattern, apply the pattern."
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