AI fraud detection: how Machine Learning prevents financial fraud
July 30, 2026 13 min read 235 views
Explore supervised and unsupervised Machine Learning approaches used to detect suspicious transactions, identify fraud patterns, and improve financial fraud prevention.
For decades, financial organizations relied on rule-based monitoring systems for fraud detection. These legacy solutions, deployed in SQL or C/C++, were engineered to translate the expertise of domain specialists into complex SQL queries. However, these systems often needed to be more cohesive and brittle, and any attempt to modify them, such as updating a threshold, could lead to the collapse of the entire codebase. This rigidity hindered banks’ ability to combat fraud effectively, as criminals continually devised new methods to evade the fraud detection system used by these rule-based platforms.
In response to these challenges, many financial firms have abandoned their legacy tools in favor of new-age Machine Learning (ML) solutions. Machine Learning algorithms can swiftly process millions of data objects and identify suspicious patterns by linking instances from seemingly unrelated datasets. These algorithms are among the few remaining tools capable of helping banks and healthtech organizations keep pace with increasingly sophisticated defrauding schemes. However, choosing the correct ML algorithm to identify illicit transactions can be unclear for those with a data science background.
This article describes a few popular choices to aid in this decision-making process.
Fraud detection using Machine Learning algorithms
Financial institutions use both supervised and unsupervised Machine Learning algorithms to detect fraudulent activity. Common supervised models include Random Forest, K-Nearest Neighbors (KNN), Logistic Regression, and Support Vector Machines (SVM), while unsupervised approaches such as K-means clustering and Self-Organizing Maps (SOM) help identify unusual patterns in transaction data without predefined fraud labels. The most effective approach depends on factors such as available data, fraud patterns, transaction volume, and the specific detection goals.
Random Forest
The method leverages a set of randomized decision trees and averages across their predictions to create outputs. It has multiple trees producing different values, and this prevents the algorithm from overfitting to training datasets (something standard decision tree algorithms tend to do) and makes it more robust to noise.
Numerous comparative studies have proven RF’s effectiveness in fraud detection relative to Machine Learning solutions and other models. The results show that an RF-based model outperforms a support vector, Machine Learning algorithms, and even a neural network in terms of AP, AUC, and PrecisonRank metrics (all of the models made predictions on an actual transaction data from a Belgian payment provider).
K-nearest Neighbors (KNN)
The algorithm predicts which class an unseen instance belongs to based on K (a predefined number) of most similar data objects. The similarity is typically defined by Euclidean distance, but for specific settings, Chebyshev and Hamming distance measures can be applied too, when that is more suitable.
So, after being given an unseen observation, KNN runs through the entire annotated dataset and computes the similarity between the new data object and all other data objects in it. When an instance is similar to objects in different categories, the algorithm picks the class with the most votes. If K=10, for example, and the object has 7 nearest neighbors in category a and 3 nearest neighbors in category b – it will be assigned to a.
Though quite noise-sensitive, KNN performs well on real financial transaction data. Over the years, studies have demonstrated that KNNs have a lower error rate than Decision Trees and Logistic Regression models and can beat Support Vector Machines regarding fraud detection rates (sensitivity) and Random Forests in balance classification rate.
Logistic Regression
An easily explainable model that enables us to predict the probability of a categorical response based on one or a few predictor variables. LR is quick to implement, which might make it seem like an attractive option. However, the empirical evidence shows it could perform better when dealing with non-linear data and that it tends to overfit training datasets.
Previous research has demonstrated that neural networks and Gradient Boosted Tree models can outperform Logistic Regression in credit card payment fraud detection problems. For example, a comparative study evaluating Logistic Regression and Gradient Boosting models found that while Logistic Regression achieved 95% accuracy, its recall and F1-score were lower than those of the optimized Gradient Boosting approach, highlighting the limitations of simpler models when identifying complex fraud patterns.
Support Vector Machine
SVMs, advanced yet simple in implementation, derive optimal hyperplanes that maximize a margin between classes. They utilize kernel functions to project input data onto high-dimensional feature spaces, wherein it is easier to separate instances linearly. This makes SVMs particularly effective regarding non-linear classification problems such as financial and fraud detection systems.
SVMs have demonstrated comparable accuracy to neural networks in some fraud detection scenarios while showing lower susceptibility to overfitting, which can make them a more reliable option for long-term fraud detection performance.
Besides, an imbalance class weighted SVM-based fraud detection model is more suitable for working with real-world credit card transactional data (which is an imbalance in nature) and shows higher accuracy rates in the fraud detection problem than Naive Bayes, Decision Tree, and Back Propagation Neural Network classifiers.
It should be noted that while SVMs work great in complicated domains with distinct margins of separation, their performance on large data sets is generally average. If there is noise in data, it can hamper SVM’s accuracy tremendously, so when there are many overlapping classes of more data (and we need to count independent evidence), other supervised algorithms would probably make a better choice.
Long Short-Term Memory (deep learning fraud prevention and detection)
LSTM is a type of Recurrent Neural Network architecture that was designed specifically to learn long-range dependencies; it tackles the vanishing error problem (which RNNs are particularly prone to due to using the same processing units on every layer) by applying constant error carousels to enforce a constant error flow within cells. The model’s fundamental property is that it has multiplicative gates determining when to grant access to cells and which parts of input to ignore.
LSTMs are challenging to integrate into real-world applications learning in learning for fraud detection at this point, so they have not yet become a mainstream tool for financial fraud detection among banks. However, there are already scientific papers published that formulate credit card fraud detection as a sequence classification task for which LSTMs, due to their unique properties, are a perfect solution.
Recent research has shown that LSTM models are well suited to fraud detection because they learn temporal dependencies across sequences of transactions rather than evaluating each transaction independently. This enables them to capture behavioral patterns that traditional Machine Learning models may miss, making them particularly effective for sequential fraud detection tasks.
Also, PayPal uses Machine Learning to analyze customer behavior and transaction patterns as part of its payment fraud detection systems. The company combines AI and ML models with large-scale data analysis to evaluate signals such as device information, account activity, purchasing patterns, and transaction history when assessing potential fraud risks.
Instead of relying only on individual transactions, Machine Learning-based fraud detection systems can analyze broader behavioral patterns across multiple data points. PayPal’s fraud prevention solutions use risk scoring and ML models to identify suspicious activity, adapt to evolving fraud tactics, and support faster fraud investigation and prevention processes.
What are unsupervised Machine Learning methods for fraud detection?
Unsupervised Machine Learning methods are widely used in fraud detection to identify unusual patterns and anomalies in transaction data without relying on labeled examples. Techniques such as K-means and Self-Organizing Maps (SOM) help uncover potential fraud by grouping similar transactions and highlighting behaviors that deviate from expected patterns. K-means, one of the oldest and most widely used Machine Learning techniques, partitions unlabeled data into clusters, while SOM reduces high-dimensional data to one- or two-dimensional representations for easier pattern analysis.
K-means
One of the oldest, most well-known unsupervised techniques, K-means, is still widely used. The method involves partitioning instances of unlabeled data into a number (K) of clusters to minimize the square distance between the data objects and centroid in each group.
The basic flow of the algorithm goes like this: we pick K (the number of clusters the algorithm will be trying to produce), and the model chooses, at random, K of points to be the centers of these clusters.
Then, each centroid claims all the data points closest to it, and after the results of the first attempt at clustering are obtained, the algorithm recomputes the centroids by averaging the cluster points. It then keeps looping through these two actions until the convergence is reached.
The model’s weak point is that it is ultra-sensitive to the initial center points and thus vulnerable to outliers. Also, the knowledge of someone with deep financial expertise would be needed to pick the optimal value for K.
That being said, several studies describe the successful application of K-means to anomaly identification tasks in financial fraud detection. In credit card fraud scenarios, researchers have used the algorithm to analyze transaction data, including attributes such as transaction amounts, merchant categories, dates, and locations, and divide data points into clusters based on behavioral similarities. This allows the model to identify groups of transactions that differ from normal activity and may indicate fraudulent behavior.
The results of these studies show that K-means can support fraud detection by identifying suspicious transaction patterns while maintaining a relatively low false positive rate. However, because fraudulent activities often represent only a small portion of financial transaction data, K-means is typically combined with other Machine Learning techniques to improve fraud detection capabilities.
More advanced approaches involve combining K-means with other Machine Learning models to improve fraud detection performance. Hybrid frameworks can use K-means to analyze historical financial data, categorize customer behavior patterns, and identify potentially risky clusters before applying additional models that estimate the likelihood of fraudulent transactions. These approaches allow financial institutions to combine anomaly detection with predictive modeling when developing Machine Learning-based fraud detection systems.
Self-organizing Map (SOM)
This unsupervised deep learning method is used for the clustering of high-dimensional data. It tries to project data down (the data does not need to be linear) to one- or two-dimensional surfaces while capturing as much information about the dataset’s inner structure as possible.
Here is how it works: we first find a neuron in the network that has similar weights to the input feature values (the input vector is sampled at random), and then we calculate the neighborhood of that neuron.
After we have found the best matching unit, we update the weights of the neuron and the neurons closest to it to make them more like the input vector (the more intimate the neurons are, the more their weights are modified, the farther away, the less.) We repeat this by sampling a new input vector each time to go through the entire dataset.
The neurons representing input instances act similarly to centroids in K-Means, which is why some call SOM a constrained K-means.
Due to its inherent capability to reduce dimensionality, the algorithm is uniquely poised to deal with high-dimensional inputs such as transaction data. When applied to the detection of abnormal transactional activities, the model first groups data into categories of “fraudulent” and “legitimate” through self-organization (which is the iterative updating of neurons’ weights to capture the best possible input representations) and then, after being given a new instance, assigns it to one of the groups based on how similar the input is to genuine or fraudulent transactions.
An SOM-based approach has also been explored for identifying payment fraud by visualizing multidimensional transaction data, including records that reflect sequential activities across users’ payment methods. By applying a threshold-based system after generating SOM visualizations, researchers have demonstrated the potential of self-organizing maps for improving transaction classification and identifying suspicious payment patterns.
K-means and SOM have proven effective in identifying fraudulent activities in financial data. K-means has been successfully applied to anomaly identification tasks, and SOM has shown clear benefits in visualizing transaction classifications in fraud scenarios. However, the choice of method depends on the specific characteristics of the data and the nature of the fraud detection problem at hand.
FAQ
Conclusion
Various methods have been proposed for fraud prevention with Machine Learning in the fraud detection Machine Learning itself, both supervised and unsupervised. The supervised approaches rely on explicit transaction labels, i.e., machines need to repeatedly be shown what genuine transactions look like during training to distinguish the fraudulent ones later.
In contrast, unsupervised models capture normal data distribution in unlabeled data sets when they are being trained. And then, when given a new data instance, they try to determine whether the sample is legitimate or abnormal (suspicious) based on the patterns and structures they have derived.
In this article, we have reviewed 7 ML models. Still, there needs to be a telling which method will suit your processes and data science best in a particular setting without doing research and experimentation. We would have to assess what data and features you have readily available to figure out which model can help you detect fraud efficiently.
Contact us to discuss your fraud detection strategy and explore how Machine Learning can strengthen your organization’s fraud prevention capabilities.
Your business results matter
Achieve them with minimized risk through our bespoke innovation capabilities. Fill in the form below.