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By Ohad Shalev
Fraud is a long-standing challenge, as is the effort to combat it. What changes over time are the tools and techniques used by both sides in this ongoing battle. In the past decade, artificial intelligence (AI) and machine learning (ML) have transformed how industries detect and prevent fraud, providing more adaptable and precise solutions than ever before.
In this article, we will explore how ML developers can utilize these technologies to detect fraud, examine popular algorithms, and share best practices for maximizing effectiveness. While our primary focus will be on examples of financial fraud, the principles discussed here are applicable across various industries.
Before we dive into the specifics of applying machine learning to fraud detection, let’s define what machine learning is. According to expert.ai, machine learning is an application of AI that allows systems to learn and improve from experience without being explicitly programmed.
Traditionally, fraud detection has relied on rules-based systems that use predefined criteria—such as blocking transactions from specific high-risk locations—to flag potentially fraudulent activity. While effective in certain cases, this approach often casts too wide a net, leading to false positives and overlooking more sophisticated fraud patterns.
Machine learning enhances these methods by analyzing historical data to identify patterns and adapt to evolving threats, making ML models ideal for tackling today’s dynamic fraud tactics. A hybrid approach—combining rules-based systems with machine learning—often yields the best results, effectively balancing precision and adaptability.
When it comes to using machine learning algorithms for fraud detection, there are generally two ways to go about it. The first is anomaly detection, which approaches the problem from an unsupervised learning perspective. The other is classification, which is a supervised learning approach.
In a general sense, anomaly detection, which is also called clustering, is a machine learning technique used to identify unusual behavior. Far-out data points that indicate unusual behavior are referred to as point anomalies. When it comes to detecting financial fraud, it’s important to understand that most financial transactions (more than 99 percent) are not fraudulent. Hence, the small percentage of transactions that fraudsters actually do perpetuate are point anomalies. These are the transactions your system needs to flag.
Using classification in machine learning to detect fraud approaches the problem from a different angle. Here, you train a model to learn the characteristics of good and bad transactions in order to classify new transactions coming in. It’s important to note that this means you’ll need to have enough data about good and bad transactions in the past that are labeled as such so that the system knows whether a transaction was fraudulent or not.
You can use many machine learning algorithms for fraud detection. However, there is no best fraud detection machine learning algorithm because which one to use depends on the data you have in hand. Below are some of the more popular machine learning algorithms, but this is by no means an exhaustive list.
Logistic regression is the most basic yet powerful machine learning algorithm you can use to predict true or false (binary) values. It estimates discrete values (usually binary values like fraud/no fraud) from a set of independent variables by fitting the data to a logistic function.
Decision trees are another popular algorithm that learns rules to split or classify data. What makes a decision tree particularly interesting is that the model is a set of rules that’s easy to explain. To make things even better, you can take these rules and create a rules-based system. However, the machine learning model is in no way a rules-based system as slight changes in the underlying data could result in a completely different set of rules.
A random forest is a machine learning algorithm that builds on multiple decision trees to provide classifications that are more accurate. It does this by averaging the results of individual decision trees, hence its predictive power is superior. Random forests work well with very large training datasets that have a large number of input variables.
On the other hand, random forests are less explainable than decision trees. Instead of a single set of rules, you end up with many of them. This could pose a problem, especially when an explanation is necessary for system compliance or other regulatory requirements.
This is a simple algorithm that stores all available cases and classifies any new cases by taking a majority vote of its k best neighbors. To do this, it makes use of a distance function like the Euclidean distance. The training process does not exactly produce a model. Rather, “training” and “classification” happen on the fly.
This makes the KNN algorithm a little more compute-intensive for fraud detection than other machine learning algorithms.
This is an unsupervised machine learning algorithm (different from KNN) that solves clustering problems. The algorithm works by grouping a given dataset into a number of clusters such that data points in a cluster are as similar as possible. Similar to KNN, it makes use of a distance function.
In real-world fraud detection, it’s almost guaranteed that you’re going to have to deal with an unbalanced dataset. This is for the very simple reason that fraud entries are a small minority. This is a problem if you’re applying supervised machine learning because the algorithms work best with balanced data. A common solution is to use fraud detection techniques like up sampling to increase the minority fraud samples or down sampling to reduce the majority of legitimate samples.
It’s really a cat-and-mouse game when dealing with fraudsters. Their behavior quickly changes, which leads to changes in the data as well. This means that it’s important to constantly train new fraud detection models. One efficient way to do this is to set up a model retraining process to adapt faster and to catch fraudulent behavior much better.
We showed you the basics of using machine learning algorithms for fraud detection. We began by framing fraud detection as a machine learning problem, looked at some popular algorithms, and finally discussed key challenges to consider.
Technical innovations in the field of machine learning continue to arise every day. So, if using machine learning to detect fraud is a journey you want to embark on, you can consider what we’ve covered in this article as a starting point rather than a destination.
That said, take up whatever tool or programming language you’re familiar with and start kicking the fraudsters from your system today.
This post was written by Boris Bambo. Boris is a data & machine learning engineer fascinated by technology, education, and business. Feel free to connect with him on LinkedIn.
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