Finance glossary

What is unsupervised machine learning?

Bristol James
5 Min

When a machine learning model is given a set of data to analyze, but not given any labels, context explanations, or rules on how to comb through the data, unsupervised machine learning is in action. Unsupervised learning is helpful when trying to recognize patterns or trends in large sets of data, and it can often highlight key insights that would otherwise go unnoticed.

Unlike other machine learning models, unsupervised machine learning algorithms aren’t reliant on human intervention; no one needs to provide guidance to the model or conduct formal model training exercises. Although unsupervised machine learning aids in the speed and depth of data analytics, this advanced technology comes with its own challenges. As we see more and more use cases pop up for these models, understanding how they work, how to best deploy them, and what to watch out for are crucial.

Supervised vs. Unsupervised Machine Learning

The key difference between unsupervised machine learning and supervised machine learning is that the latter requires the data it processes to be labeled before it runs through the model. Supervised machine learning models rely on humans to provide context for the data and train the model on how to process the data upfront. Because of the human involvement, supervised ML models usually provide more accurate results. Supervised machine learning can be split into two types: regression and classification models.

Types of Unsupervised Machine Learning

Unsupervised learning can be broken apart into three different methods: clustering, association rules, and dimensionality reduction. Let’s examine all three.


If data needs to be broken into different categories based on the characteristics of said data, clustering is at play. One of the most-used types of unsupervised machine learning, clustering is the process of separating data into “clusters” based on how similar or different data points are to one another. You may see clustering applied in spaces like fraud detection or customer segmentation.

Association Rules

Used to assess relational insights between data, association rules can identify correlations and data points that occur in similar situations. Think of this as an “if-then” rule in data – if X happens, Y happens, too. In business settings, association rules are used to identify customer purchasing patterns. For instance, this type of unsupervised machine learning might identify that 72% of customers who bought a specific table also bought the matching chair set.

Dimensionality Reduction

Because of the large data sets that most machine learning models are working with, dimensionality reduction helps simplify those sets by removing unnecessary dimensions of data. These models may notice that certain data is not relevant to any insights or outcomes, enabling the simplification of the model itself, and often boosting the efficacy of its own outcomes. Think of this as a “self-cleaning” function within the data itself.

Why Unsupervised Machine Learning is on the Rise

A 2021 survey conducted by McKinsey highlighted that 56% of businesses were using artificial intelligence in one or more functions, and that number has grown steadily since. As more organizations begin to deploy AI solutions in their business processes, understanding the benefits and drawbacks of each new tool is more important than ever. When it comes to unsupervised machine learning, the top benefits are:

  • Less upfront investment from humans is needed to train the model or label the data.
  • Unsupervised machine learning models can identify patterns or insights that were previously undiscovered.
  • The “self-cleaning” functionality of these models (dimensionality reduction) enhances data processing efficiency automatically.
  • Because of how these models bring new insights to light, businesses can use them to understand their customers, processes, and markets in new ways. This can result in better strategic prioritizations and growth outcomes.

Top Challenges with Unsupervised Machine Learning

Like anything that’s “unsupervised,” machine learning models that are working on their own aren’t always perfect, especially at first. While the benefits from unsupervised machine learning materialize, you may experience a few challenges, such as:

  • Unsupervised ML models can take a bit longer to get up and running. It can take some time for the model to sift through the data, learn its patterns, and draw meaningful insights.
  • Without human intervention, these models have higher rates of inaccurate results. Because of this, it’s important for experts to review the model’s outputs before sharing them more broadly.
  • Less is known about how the model conducted its analysis and identified correlations, patterns, or cluster types.

Unsupervised ML in Practice

Depending on the industry and business type in question, there are many ways that unsupervised machine learning can be deployed. Here are a few:

Customer Categorization

Perhaps the most-used application of unsupervised ML revolves around customer segmentation. Looking at customer data to understand purchasing behavior, campaign effectiveness, and customer characteristics, these models support monetary gain and organizational growth.

Fraud Detection

By identifying anomalies in data sets, unsupervised ML models can flag worrisome transactions or suspicious purchases, helping businesses mitigate payment fraud and theft. When paired with secure payment protection platforms like Eftsure that are specifically designed to prevent fraud, unsupervised machine learning helps businesses avoid cybersecurity breaches and financial losses.

Recommendation Engines

As a customer, when you purchase a product or service and get a message that suggests another product you might like, unsupervised machine learning is likely at work behind the scenes. The model can look at all the other customers who purchased the same initial product, identify which products they also purchased, and recommend them to you.


  • Unsupervised machine learning models work to assess unlabeled data, looking for patterns and relationships within the data that have not yet been uncovered. These models do not require human intervention for upfront training and data cleaning, but they may need intervention to ensure the outputs are accurate.
  • There are three types of unsupervised machine learning: clustering, association rules, and dimensionality reduction.
  • Despite the potential for innovative insights and less human intervention, unsupervised machine learning is prone to inaccuracies. Less transparency throughout the analytical process can make it hard to understand the models and work through these inaccuracies.




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