Machine Learning for Fraud Detection: Techniques, Applications, and Career Paths

Key takeaways

Machine learning for fraud detection uses advanced techniques to flag irregular activity and identify anomalies to alert organizations to fraud. Here are some important things to know:

• Cybercrime is equivalent to the third-largest economy in the world, with estimated losses and damages predicted at $10.5 trillion for 2025 [1]. 

• Machine learning algorithms for fraud detection span supervised learning, unsupervised learning, neural networks, and ensemble methods.

• You can work in fraud detection roles within industries such as banking, finance, health care, retail, and government sectors. 

Explore machine learning techniques for fraud detection and how you can use them to identify different types of fraudulent activity. If you’re ready to learn more, enroll in the IBM and ISC2 Cybersecurity Specialist Professional Certificate. You’ll have the opportunity to build job-ready skills needed to get your first cybersecurity role, plus prep for the ISC2 Certified in Cybersecurity (CC) exam employers look for. 

What is machine learning for fraud detection?

Fraud detection in machine learning uses algorithms to identify suspicious patterns of behavior indicative of fraudulent activity. These machine learning algorithms are trained on a combination of legitimate and fraudulent historical data, allowing the algorithm to use pattern recognition and anomaly detection to identify irregular transactions and act on suspicious activity before losses occur. Cybercrime has grown so large that if you considered it a country, it would earn a spot as the world’s third-largest economy, with global losses projected to reach $10.5 trillion by 2025 [1]. Because of this scale, organizations require advanced, data-driven methods to combat sophisticated fraud attacks. 

Historically, analysts used predefined rules, such as flagging certain amounts or locations, to detect potential fraud. While these rule-based systems caught known patterns, they didn’t easily adapt to new and emerging forms of fraud and were more prone to false positives. Machine learning algorithms can continually update based on new information, allowing these models to use large amounts of data to identify more complex fraudulent activity and adapt to new fraud schemes over time.

What machine learning algorithms are used for fraud detection?

Typically, machine learning algorithms used for fraud detection fall into one of several categories: supervised machine learning, unsupervised machine learning, ensemble methods, and neural networks. Choosing the right machine learning model for fraud detection depends on the type of training data you have and your overall goal. 

Supervised machine learning techniques

Supervised machine learning techniques rely on labeled data, such as examples of both fraudulent and legitimate transactions, to “teach” the algorithm how to correctly classify incoming information. Popular supervised machine learning techniques include:

• Logistic regression: Estimates the probability that a transaction is fraud (e.g., yes fraud/no fraud) based on a set of independent variables

• Decision trees: Split data into smaller, rule-based groups to predict whether a transaction is fraudulent based on a series of decision paths 

• Support vector machine (SVM): Separates fraudulent and nonfraudulent transactions by finding the best boundary (hyperplane) between the two groups for classification 

• K-nearest neighbors (KNN): Uses a classification method to group transactions by how similar they are to previous ones (i.e., their “nearest neighbor”) 

Unsupervised machine learning techniques 

Unsupervised machine learning techniques are useful when you don’t have labeled data and you want the algorithm to find unexpected or unusual activity by analyzing the natural structure of the data set. 

• K-means clustering: Groups similar transactions together to help identify outliers that deviate from standard activity

• Anomaly detection model (isolation forest, one-class SVM): Identifies unusual transactions that differ significantly from normal patterns, flagging them as potential fraud even when no labeled examples are available

Ensemble methods

Ensemble methods combine several types of machine learning models to enhance predictive power. Instead of using a single algorithm, ensemble approaches use the collective strength of several models. 

• Random forests: Build a series of independent decision trees and combine their outputs (known as bagging) to provide a more accurate estimation of the likelihood of fraud

• XGBoost (extreme gradient boosting): Builds multiple decision trees sequentially, with each tree correcting errors of the previous one to handle complex data sets effectively with high predictive accuracy

Neural networks 

Neural networks are advanced machine learning models inspired by the way humans process information. They use layers of nodes, or artificial neurons, to learn complex relationships. This adapts to fraud detection to learn transaction patterns and identify irregularities.

• Graph neural networks (GNNs): Analyze the relationship between entities (e.g., customers and merchants) to model connections and identify suspicious link patterns.

• Feedforward neural networks (FNNs): Take transaction features and pass them through multiple layers of processing nodes to classify whether the transaction is likely fraudulent.

What is the best AI model for fraud detection? 

The “best” artificial intelligence (AI) model for fraud detection depends on the type of data you have and your use case. For example, if you have labeled data, such as labeled normal and labeled fraudulent transactions or financial records, a supervised learning algorithm may provide adequate support to detect irregular activity. If you’d like your model to recognize unusual activity without prior examples, unsupervised learning algorithms can detect anomalies with minimal oversight. For more complex fraudulent activity, such as signature or image forgery, neural networks might provide the best pathway to flag this activity efficiently.

As fraud schemes and machine learning algorithms become more advanced, many organizations find success in combining supervised and unsupervised learning methods to maximize benefits, rather than relying on one approach. 

Learn more: Will AI Replace Cybersecurity? 

Who uses machine learning algorithms for fraud detection?

Professionals in industries that prioritize security and financial integrity use machine learning algorithms for fraud detection. While banking and financial institutions stand out in this area, organizations in health care, insurance, retail, and government sectors also rely on machine learning to protect against fraud. 

For example, in banking and finance, algorithms monitor activity and identify suspicious behavior. This includes identifying credit card fraud, tracing cryptocurrency use and blockchain transactions, flagging potential money laundering activity, and warning users of suspicious sites or platforms to protect them from scams.

In government and cybersecurity sectors, professionals use machine learning techniques to identify fraudulent activity, such as identity theft, social security scams, and unemployment scams. In retail and e-commerce sectors, professionals use similar techniques to prevent exploitation of return policies and refunds, and illegitimate purchases or other manipulation leading to store revenue loss. In health care, these methods help to identify health insurance fraud, both by providers and patients, by identifying irregularities in billing or reimbursement activity.

How machine learning improves fraud detection

Machine learning improves fraud detection through its ability to analyze large quantities of data, detect anomalies, and detect suspicious activity more efficiently than traditional rule-based systems. Every day, millions of transactions occur, from credit card transactions to health care authorizations. Monitoring this volume of activity manually is nearly impossible without professionals relying on machine learning algorithms to annotate and analyze data in real-time to identify suspicious activity quickly.

Machine learning algorithms thrive on having more training material to improve their accuracy, meaning they can adapt over time to stay ahead of evolving fraud tactics. This means that machine learning techniques for fraud detection continuously improve their ability to detect suspicious behavior and alert organizations, without needing manual oversight. In addition, because several types of machine learning algorithms for fraud detection exist, you can choose the method most suited to your data type, or even combine methods, for maximum efficacy over time.

Challenges and limitations of machine learning models for fraud detection

While powerful, machine learning techniques come with an inherent set of limitations that are important to stay aware of. For one, although machine learning systems continue to improve over time, they are not foolproof. In some instances, these models have produced hallucinations, meaning they generated false results. To mitigate this, you should focus on creating specialized models for specific tasks, rather than “catch-all” algorithms. This often requires an investment in resources over time. Discussing budget, planning, and implementation strategies to invest in new technologies within your organization may help to stay on top of fraud detection.

In addition, because you’re likely to use fraud detection in sensitive industries where privacy is of the utmost importance, ensuring you are collecting and processing data ethically should be a priority. To do this, pay careful attention to current regulatory environments, consumer privacy considerations, and data privacy laws.

Which professionals specialize in how to detect fraud?

Several types of professionals specialize in how to detect fraud, including fraud detection analysts, cybersecurity specialists, and data scientists. Depending on your area of specialization, you can find many roles related to fraud detection within accounting, auditing, government, and investigation sectors. 

Top skills to consider building include specialized expertise in anti-money laundering, auditing, the Bank Secrecy Act, financial services, and fraud investigation. Once you decide on your area, you can explore roles in the following areas:

• In accounting, you can explore careers as a forensic accountant, government accountant, or public accountant.

• In auditing, you can explore careers as an external or internal auditor.

• In government, you can explore careers as a compliance professional or risk management professional.

• In investigation, you can explore careers as a private- or public-sector investigator.

How to use machine learning for fraud detection

You can create your own machine learning model for fraud detection by exploring sample code, credit card transaction data sets, and Amazon Web Services (AWS) Well-Architected best practices. To start, it may help to familiarize yourself with architecture diagrams for self-learning machine learning models. From this, you can build a foundational understanding of how your code will learn from training data and apply it to future information. 

Once you do this, you can either write your own code or use sample code from GitHub (like this) to design your machine learning model. You can then run it with credit card transaction data or any type of relevant data, so your model can learn from relevant information. After you’ve designed and trained your model, you can take steps to optimize your parameters and evaluate your model's performance. This can help ensure you’re producing the most accurate results for your data. 

When designing your model, try to keep the Well-Architected best practices authored by AWS in mind, including operational excellence, security, reliability, performance efficiency, cost optimization, and sustainability. By doing this, you can design a model that can scale to match demand at a low cost, effectively handle errors and disrupted workflows, and remain secure against cyberattacks. 

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