Fraudsters have been around forever, and so have the good people trying to stop them. The methods the two employ change over time, though. With artificial intelligence taking center stage over the past decade, using machine learning for fraud detection has become popular in many industries.

In this article, we’ll explore how to use machine learning for fraud detection, some of the most commonly used algorithms, and best practices so you can get the most out of this powerful technique. It’s important to note that although we mostly refer to examples of financial fraud in this post, the concepts also apply more broadly.

fraud detection machine learning

Benefits of Using Machine Learning Over Traditional Methods

 

fraud detection machine learning

 

First let’s briefly define what machine learning is before we dig into how to use it to detect fraud. One of the best definitions I’ve seen yet is from expert.ai.

Machine learning is an application of AI that enables systems to learn and improve from experience without being explicitly programmed.

There are two approaches. The most common is the rules-based approach, while the most effective is using machine learning. Rules-based detection has been around for a while and is still widely used, but it’s less reactive to the ever-changing fraud landscape. In addition, using rules means you have to cast a wide net, which usually leads to many honest transactions being flagged as fraudulent. For example, a risk analyst could create a rule based on location and block transactions originating from supposedly risky locations.

 

Machine learning improves on rules. As stated in its definition, with machine learning a system can learn from previous experiences (data), which is exactly what you need if you’re dealing with a fraudster. This in no way means that rules are not useful or that they’re obsolete. In fact, using a combination of both methods gives you the best shot when fighting the bad guys.

Using Machine Learning for Fraud Detection

fraud detection machine learning

When it comes to using machine learning to detect fraud, 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.

Anomaly Detection

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.

fraud detection machine learning

Classification

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.

Algorithms for Machine Learning Fraud Detection

fraud detection machine learning

You can use many 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 algorithms, but this is by no means an exhaustive list.

Logistic Regression

Logistic regression is the most basic yet powerful 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

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 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.

Random Forests

A random forest is an 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.

K-Nearest Neighbors (KNN)

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.

K-Means

This is an unsupervised 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.

Challenges using Machine Learning in Fraud Detection

fraud detection machine learning

Label Imbalance

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 techniques like up sampling to increase the minority fraud samples or down sampling to reduce the majority of legitimate samples.

Non-stationary Data

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 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.

Conclusion

We showed you the basics for using machine learning to detect fraud. 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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