What Is Machine Learning?
Machine learning is a subset of artificial intelligence that trains computer systems to accept and process information automatically. It is based on the premise that systems can learn from data, recognize patterns and trends, and make decisions with little human involvement.
Why Is Machine Learning Important?
Machine learning (ML) technology focuses on developing computer programs that create self-learning algorithms to analyze and utilize data to make sound or repeatable decisions with little or no human intervention. These programs can simulate human learning to grow and adapt independently to new data. With self-learning algorithms, systems can process large amounts of data faster, make accurate predictions, and automate tasks.
Varieties and amounts of data are growing exponentially by the second and processing it all with speed, accuracy and efficiency is becoming impossible without automation. Machine learning processes data and facilitates the tracking of patterns and pinpointing of potential problems to help managers determine the most efficient steps to take. It reduces manual data entry workloads and improves customer experiences by making knowledge-based suggestions based on previous data entered.
Types of Machine Learning
Machine learning is divided into four categories:
In supervised learning, algorithms are trained with labeled data, where the input and output are understood. This technique is further divided into KNN Classification and Linear Regression, Support Vector Machine, Decision Trees, and Naive Bayes' theorem. Weather forecasting and process optimization systems use supervised learning.
With unsupervised learning, algorithms are trained with data that has no historical markers, so the output is unknown. The algorithm makes sense of the data and creates data clusters with new labels. The different kinds of unsupervised learning include Clustering and Dimensionality Reduction. This technology works well with identifying segments of customers for marketing efforts.
Reinforcement learning algorithms do not receive any raw data; they discover the best actions based on trial and error. Some of the main types include Q-Learning, State Action Reward State Action (SARSA), Markov Decision Processes, Deep Q-Network, and Deep Deterministic Policy Gradient (DDPG). These types of algorithms are often used in navigation, robotics, and video game design.
Semi-Supervised learning is frequently applicable for the same purposes as supervised learning, but it utilizes labeled and unlabeled data for training a relatively small amount of labeled data with a much larger amount of unlabeled data. This technology helps avoid human biases through excessive labeling and saves time and resources that would be otherwise taken in creating the labels.
Why Is Machine Learning So Popular?
Machine learning is booming thanks to affordable cloud platforms and innovative technologies that masterfully handle data aggregation, integration, and analysis. Most data-prolific sectors have taken advantage of machine learning to work more efficiently and gain a competitive edge. This technology is becoming more indispensable as businesses strive to harness data and automate processes.
Forbes reports that the worldwide machine learning market could grow from $1.4 billion in 2017 to $8.8 billion by 2022. A 2018 survey of data professionals found that about 60% of companies report that they actively use machine learning and Artificial Intelligence. These firms indicate that these technologies will be their most pressing data initiatives in the near future. Many organizations that have adopted machine learning technology say that ML has significantly enhanced their data analysis and R&D capabilities.
Applications in Finance
The finance industry is a prime beneficiary of machine learning. The high and growing volume of data, need for historical records, and demand for user-friendly interfacing are perpetuating an explosive trend toward utilization of this technology in the financial world. Companies employ machine learning to assign credit scores, assess risks, approve loans, and manage assets. Algorithms run chat bots and search engines nonstop.
Machine learning is transforming these areas:
- Portfolio Management: Robo-advisors are algorithms, not robots, that calibrate and manage portfolios to users' goals and risk tolerances. People who are comfortable investing without human advisors are gravitating toward these tools.
- Fraud Detection: As an industry fueled by sensitive data, the financial markets are extremely vulnerable to security breaches. Machine learning systems can pick up on aberrations and alert security teams. ML for modern fraud detection involves more than checking currently known risk factors — it actively learns and adjusts to new potential threats.
- Algorithmic Trading: Since the 1970s, algorithmic systems have been making millions of trades. Most financial institutions do not openly discuss their dependence on machine learning, but this technology is increasingly impacting real-time trading decisions.
- Underwriting: There is growing concern that machine learning will displace many underwriting professionals. Large organizations can train ML algorithms on consumer data to detect individual and regional trends. Thus, the bulk of the underwriting process can be delegated to systems instead of people.
- Finance Processes: Machine learning can streamline budgeting, invoice handling, and tax operations. It can also reduce operating expenses and guide users to accurate, actionable information quickly.