Machine Learning (ML) is a subset of technology within Artificial Intelligence (AI). Both ML and AI algorithms have a diverse range of uses across a variety of landscapes which is based on large quantities of data. The machine or algorithm is trained using patterns to predict new sets of data. ML is becoming more and more popular because there is much more data, computing power, and algorithms now than ever before.


Machine learning is known for having two main branches – supervised and unsupervised. 


Supervised machine learning is task-driven and requires a large amount of data, specifically input data and the desired output. The data you put into an algorithm (input data) determines the data the algorithm or machine produces (output data).

Have you ever been on your streaming platform browsing for a new T.V show or movie to watch and the platform offers you a recommendation? That is an example of “nearest neighbor” – or a form of supervised machine learning. The database analyzes other users’ behavior similar to yours to recommend what you like. The recommendation is based on what you search, purchase, like, and dislike. Read more about Netflix using ML here.

Nearest neighbor has also been seen a lot on social media platforms, where it tracks when you stopped to read, share, or like the content. Supervised machine learning requires minimal training and can classify data easily.

Netflix uses machine learning to produce personal recommendations
Netflix uses machine learning to produce personal recommendations

Unsupervised machine learning also requires a large amount of data. In this form of ML, the machine works independently to find patterns, groupings, and relevant data from the information it is given.

A simple example of this would be in the real estate industry. Information of a house, such as the number of bedrooms and square feet, can be put into a database. The database categorizes the houses based on their common characteristics. It can group houses with similar characteristics to make realtors and home buyers find their desired home much more efficiently. Check out more ways technology is changing the real estate industry here.

This branch of ML can be used when there is a large amount of data as it can be understood faster than a human can.

Machine learning in real estate
ML is commonly used in the real estate industry


In the context of financial technology (FinTech), there are three core application areas of machine learning. 

Cognitive Automation

Cognitive automation is the notion of automating processes and tasks through ML or AI. This can be seen in a variety of different forms including robo-advisors.

Some wealth management firms have implemented robo-advisors to replace financial advisors. It can reach more clients while keeping the same quality of work like a robo-advisor serving over 300 clients with investment and rebalancing strategies, while a financial advisor could only serve approximately 30. Other examples of cognitive automation can be seen in self-driving cars or digital assistants. 

Cognitive Engagement

Cognitive engagement is the notion of improving and enhancing unique customer experiences through conversational interfaces. Cognitive engagement is one of the more popular forms of machine learning within businesses as seen through chatbots and voice interfaces.

AI makes it easy to create a unique customer experience by quickly providing them with relevant data at a much quicker pace. Engaging ML can also make the customer experience more personalized by understanding what the customer does and does not like. 

Cognitive Insight

Cognitive insight can help predict and deflect fraud data using insights from large amounts of information. Insightful ML is extremely valuable as it can pull insightful data and unusual behavior from a large amount of information that humans would be unable to do or require a large number of resources. The algorithm knows what typical transactions look like, and flags ones that do not fit the behavior in real-time. Fraud detection and cybersecurity have become immensely better due to cognitive insight routed from AI and ML.


Machine learning is a very powerful subset of artificial intelligence that can be used in a vast amount of industries. Due to data, computing power, and ML algorithms growing – it is becoming more and more popular. Machine learning is an emerging technology in the upcoming years.

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