Will Machine Learning be Your Secret Weapon Against Both First- & Third-Party Fraud?
It’s no secret that digital fraud is getting worse by the year. Recent data released by LexisNexis Risk Solutions found that North American eCommerce merchants spent $4.61 to investigate and resolve every dollar lost to fraud; the highest on record.
Chief among the culprits behind the rise in online fraud is the rapid proliferation of generative artificial intelligence. Text-to-image tools and chatbots are enabling anyone with an internet connection to spin up difficult-to-spot deepfakes and convincing spam texts. These dual threats are making scams like new account fraud and account takeover increasingly common.
The statistics speak for themselves: deepfake attacks exploded by fourfold between 2020 and 2024, and today make up 7% of all fraud attacks.
So, what can merchants do to counter the rise in AI-enabled fraud? Well, fighting fire with fire by implementing an AI and machine learning-based approach to fraud detection could be the answer.
Recommended reading
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- Choosing a 3-D Secure Solution: 7 Critical Features for 2025
- Fraud Management Explained: Top 10 Service Providers of 2025
- Frightful Fraudsters are Prowling This Halloween
What is Fraud Detection With Machine Learning?
Machine learning means a computer uses experience and data to automatically improve its own capabilities. The program tests incoming information to see if it either contradicts or reinforces an existing algorithm, then adjusts accordingly. The more data the machine receives, the more reliable its predictions will be.
The machine learning model of fraud detection is a technology-powered strategy that compares incoming information against historical data prior to approving a transaction. The machine learning model uses sophisticated algorithms to analyze results, effectively “learning” from each new input.
Conventional (“Rules-Based”) Fraud Detection vs. Machine Learning
Machine learning fraud detection models can be trained using examples of good and bad transactions. And, with more training, it’s able to identify fraud activity in real-time with more information. Thus, the system gets better and more accurate over time.
The model calculates a score that reflects the transaction’s fraud risk based on everything it learns. The final score is compiled from multiple elements, with different factors weighted more heavily than others, and is used to make a decision: accept the transaction, reject it, or flag it for manual review by a human.
The entire decisioning process typically takes less than a second. The customer is totally unaware that it even happened. The information extracted from the transaction is then fed back into the model, further refining the algorithm.
The entire decisioning process typically takes less than a second. The customer is totally unaware that it even happened. The information extracted from the transaction is then fed back into the model, further refining the algorithm.
Advantages of Using Machine Learning to Detect Credit Card Fraud
As we established, machine learning fraud detection technology works by comparing new information against what it already knows. Humans go about the decision-making process in the same way, so why rely on a machine?
Well, ML technology offers a few key advantages:
- It’s Faster: Robust machine learning algorithms can analyze complex transaction data and render a risk score in a split second.
- It’s More Accurate: ML models analyze much more information than a human can. They’re able to detect even subtle fraud patterns, free of human bias or error.
- It Gets Better: With good input, machine learning models will improve with each transaction. A machine learning fraud detection system grows with your business.
- It’s Proactive: ML models learn from bad actors and normal behavior. The algorithm can proactively identify fraud before a bad transaction gets processed.
- It Saves Money: A computer can run more comprehensive data checks than a room full of human analysts. It lets you reallocate staff where they’re most needed.
- It’s Adaptive: Legacy systems depend on preset, static responses. A fraud detection model, however, is designed to adapt to new information on its own.
Done correctly, fraud detection machine learning is a highly effective way to identify and prevent fraud. That doesn’t mean it’s perfect, though.
How Does Fraud Detection With Machine Learning Work?
Fraud detection machine learning can be thought of as a five-step process involving data ingestion, feature extraction, prediction, and a final output. The entire process takes less than a second to complete.
Machine learning fraud models make fraud decisions in real-time. The process of data ingestion, analysis, and output all occur at the blink of an eye. In total, the workflow takes less than one second to complete.
Here is how it works in practice:
Step #1 | Data Ingestion
First, the fraud detection system takes in thousands of available data points associated with a transaction. Everything from IP addresses, email addresses and billing details to device fingerprints, geolocation data, and behavioral information (like touchscreen gestures or typing speeds) are taken into account.
Step #2 | Feature Extraction
Next, the system prepares the raw data for analysis. Through a process called feature extraction, the model processes, transforms, and aggregates the datapoints into a format that can be understood by the machine learning model. For example, the system may examine the domain associated with an email address by recording features like the domain’s age, risk level, and data breach history.
Step #3 | Prediction
Now, the core analysis occurs. The extracted features are run through the trained algorithm which assesses and weighs each feature simultaneously. Depending on the model’s training data, a new account using a proxy server might carry a heavy weight, while an address verification service (AVS) mismatch might carry a lighter weight. The system processes all these factors and reduces them down to a simplified fraud risk score, typically on a scale of 0 to 100.
Step #4 | Rule Application
Your pre-set fraud logic and risk thresholds are layered on. For instance, you may have a preexisting rule that flags all transactions over $2,000 for manual review, regardless of risk score. To reduce buyer friction, you may have another rule that automatically accepts transactions from a trusted customer, regardless of red flags present.
Step #5 | Output & Delivery
Based on the fraud score from Step 3 and your custom rules in Step 4, the system outputs one of three decisions: accept the transaction, decline it, or flag it for review by a live human. This final decision is then relayed to your payment gateway or eCommerce platform so that they may complete the transaction in question.
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What Data Points are Used?
Machine learning-based fraud detection analyzes data points including transaction details, behavioral indicators, device and network information, and the buyer’s transaction history.
A machine learning-based fraud detection system is powerful because it is able to analyze a vast array of data from every transaction in real time. These data points include:
Common Machine Learning Models for Fraud Detection
We've explored general fraud situations. Now let's dive into building machine learning applications and investigate typical and advanced methods for crafting fraud detection engines.
Anomaly Detection
Anomaly detection, a prevalent anti-fraud approach in data science, categorizes data objects into two groups: normal distribution and outliers. Outliers transactions are those that deviate from the norm and may be fraudulent.
- Are clients using services as expected?
- Are user actions and transactions typical?
- Are there inconsistencies in user-provided information?
This approach provides simple binary answers, useful for situations like requesting additional verification for suspicious transactions. While it may not expose fraud, it supports existing rule-based systems.
Supervised Learning
Supervised learning trains algorithms using labeled historical data. The goal is to predict target variables in future data.
Supervised learning models help create and improve business applications, including:
- Image and Object Recognition: Identifying and classifying objects from videos or images
- Predictive Analytics: Building systems that offer insights into business data points, enabling informed decision-making
- Customer Sentiment Analysis: Extracting and classifying information from large data volumes for understanding customer interactions
- Spam Detection: Using algorithms to manage spam and non-spam communications effectively
Unsupervised Learning
Unsupervised learning models process unlabeled data, classify it into subsets and detect hidden relationships between data item variables. This process includes:
- 1. Clustering: Grouping unlabeled data based on similarities or differences
- 2. Association Rulesets: Discovering correlations between dataset variables
- 3. Dimensionality Reduction: Reducing data inputs while maintaining dataset integrity
Unsupervised learning allows for rapid pattern detection in large data volumes. Common real-world applications of unsupervised learning are:
- Computer Vision: Performing visual perception tasks, such as object recognition
- Medical Imaging: Facilitating quick and accurate patient diagnosis in radiology and pathology
- Customer Personas: Creating accurate buyer persona profiles to tailor product messaging
- Recommendation Engines: Developing efficient cross-selling strategies based on past purchase behavior data
Advanced systems can detect anomalies and recognize patterns that signify specific fraud scenarios. Anomaly detection, supervised learning, and unsupervised learning are widely used in anti-fraud systems, either individually or combined, to create more sophisticated anomaly detection algorithms.
Whitebox Machine Learning Systems
Whitebox machine learning models are transparent and interpretable, but potentially less accurate when compared to blackbox models.
When evaluating machine learning models for fraud detection, you may come across the terms “whitebox” and “blackbox.” Your choice of one or the other will have significant implications for performance, control, and transparency.
A whitebox model prioritizes transparency and interpretability. These systems are built on more straightforward, interpretable algorithms like decision trees or logistic regression, allowing you to debug and fine-tune the model with greater ease.
Whitebox Model Pros
Whitebox Model Cons
Blackbox Machine Learning Systems
Blackbox models are opaque when stacked against whitebox systems. But, they can be more accurate and more powerful.
A blackbox model, on the other hand, is designed for maximum predictive power. These systems often use highly complex algorithms, like neural networks, that analyze thousands of data points to find intricate and non-linear connections.
Blackbox Model Pros
Blackbox Model Cons
Industry Uses for Machine Learning in Fraud Detection
Machine learning models are already known for their ability to help businesses break down data and create specific, measurable, attainable, realistic, and timely (or “SMART”) goals and action plans. AI-driven fraud prevention transcends industries, requiring only data to function effectively.
Machine learning fraud detection implementation can already be seen in a number of sectors, including:
Securing Digital Wallets & Combating ATO Attacks
As Buy Now Pay Later (BNPL) accounts evolve into online digital wallets, the risk of account takeover (ATO) attacks increases. Fraudsters can exploit compromised accounts to make illegal purchases. The key to safeguarding these accounts lies in understanding user login patterns, which can vary significantly based on factors like market and seasonality. Using machine learning to analyze login data can improve user authentication and enhance account security.
Compliance & Fraud Detection for Financial Institutions
Fintech firms, traditional financial institutions, and insurance providers must adhere to stringent compliance requirements to avoid regulatory penalties. They need to ensure they are interacting with genuine users, not fraudsters. To stay competitive, these institutions must act swiftly, which can sometimes lead to fraudulent profiles slipping through the cracks. Implementing a machine learning system can provide invaluable insights to distinguish between legitimate and fake user profiles.
Tackling Bonus Abuse and Multi-Accounting
Online gaming platforms and betting sites must ensure their players are genuine while also offering enticing rewards to new customers. This dual objective creates opportunities for fraudsters to engage in multi-accounting, claiming signup bonuses, and collusive play. Machine learning systems can analyze data to identify suspicious user behavior, detecting poker bots, cheating players, and low-quality traffic from dishonest affiliates.
eCommerce Fraud Prevention for Online Retail
Scrutinizing thousands of transactions can be a daunting task for eCommerce fraud managers. Machine learning can help identify the reasons why certain transactions weren't initially flagged as fraudulent. Leveraging machine learning can reveal which products are frequently targeted by fraudsters. It can also point out high-risk shipping information, and which card payments should be blocked to reduce chargeback rates.
Streamlining Gateway Security
Manually reviewing every transaction is impractical for payment gateways, especially when speed is critical. Processing thousands of transactions quickly makes human intervention virtually impossible. Machine learning engines can serve as a fraud monitoring analytics system and be trained to detect fraudulent transactions and prevent chargeback costs (specifically, for “non-authorized” chargebacks).
Implementation Guide: Getting Started with ML Fraud Detection
To get started, you will need as much transaction data as possible. This will establish a baseline for acceptable customer behavior. If your data set is too small for accurate learning, some providers will create “starter sets” of data from businesses similar to yours.
Next, the machine learning fraud detection system will pull specific data points from each transaction and add them to the model. This may include personal customer information, order and payment details, the location and network of the order, and so on. For a fraud detection model, all this information will need to be labeled as “good” or “bad.”
You now have the raw data to build your model. However, you still need to create an algorithm that helps the machine recognize the difference between “good” and “bad” transactions. Basically, you have to teach it the parameters for determining the legitimacy of a transaction.
Step #1 | Data Collection
The first step is to gather data from various sources, such as transactional data, user behavior, and historical fraud cases. This data forms the basis for training and evaluating machine learning models.
Step #2 | Data Preprocessing
Raw data needs to be cleaned and preprocessed to ensure that it is suitable for machine learning algorithms. This step may involve handling missing values, removing outliers, and converting categorical variables into numerical values.
Step #3 | Feature Engineering
This is the process of extracting relevant features or variables from the raw data. Features can be basic attributes (e.g., transaction amount, time, and location) or more complex, derived attributes that capture specific patterns indicative of fraud.
Step #4 | Data Splitting
The preprocessed data is divided into training and testing sets. The training set is used to build the model, while the testing set is reserved for evaluating its performance.
Step #5 | Model Selection
There are various machine learning algorithms suitable for fraud detection, including logistic regression, decision trees, random forests, support vector machines, and neural networks. The choice depends on the problem's nature, data characteristics, and desired performance.
Step #6 | Model Training
The chosen algorithm is “taught” based on the training dataset, where it learns to identify patterns and relationships between input features and the target variable (fraud or non-fraud).
Step #7 | Model Evaluation
The model's performance is assessed on the testing dataset using evaluation metrics such as precision, recall, F1 score, and area under the ROC curve (AUC-ROC). These metrics help determine the model's ability to correctly classify fraudulent and non-fraudulent transactions.
Step #8 | Hyperparameter Tuning
The model's performance may be improved by adjusting its hyperparameters, which are settings that influence the learning process. This step typically involves a search over a range of hyperparameter values to find the combination that yields the best performance.
Step #9 | Model Deployment
Once the model has been trained and evaluated, it can be deployed into a production environment, where it will monitor and analyze transactions in real-time. When the model detects a potentially fraudulent transaction, it can flag it for further investigation or automatically block the transaction.
Step #10 | Model Maintenance
Fraud patterns evolve over time, so it's crucial to regularly update the model with new data and retrain it to maintain its effectiveness. This process may involve continuous monitoring of the model's performance, incorporating new fraud cases, and adjusting hyperparameters as needed.
Are There Any Disadvantages to Adopting Machine Learning?
Fraudsters work tirelessly to find new ways to subvert the system. For example, finding new ways to mimic typical customer behavior. This can make any fraud indicators much more subtle and harder to recognize. There’s also the fact that machines are only as good as the input they have.
Any bad data can impact the algorithm results. And since transaction info is fed back into the model, it can cause serious issues over time. For example, if your system misidentifies a friendly fraud incident as genuine criminal fraud, that skews the decisioning matrix. Inaccurate data leads to bad decision making, so the ML system will keep making the same mistake over and over.
Despite the system’s benefits, there are instances where traditional manual reviews may be more suitable than automated systems:
- Limited Control: “Black box” machine learning engines can occasionally make errors without detection. This lack of transparency and control can be concerning for businesses, particularly when these mistakes have significant consequences.
- False Positives: Because rules only allow “yes” or “no” decisions, it’s not uncommon for legitimate orders to get marked as fraud. This is a serious concern, as false declines cost merchants $443 billion every year.
- Absence of Human Insight: Understanding the underlying reasons behind suspicious user actions can sometimes require human intuition and psychological analysis. This is something that automated systems may struggle to replicate.
- Friendly Fraud: Since first-party abusers typically exhibit normal, non-fraudulent behavior patterns, it becomes difficult for machine learning algorithms to distinguish between genuine and friendly fraud transactions.
- High-Value Transactions: Manual reviews can be more reliable for high-stakes transactions. In these cases, human reviewers can provide an additional layer of scrutiny to verify the legitimacy of the transaction and minimize the risk of fraud.
- Implementation Challenges: Overhauling your existing rules-based fraud detection system and replacing it with a machine learning model can be cumbersome, time-intensive, and expensive. Development, integration, testing, and staff training can cost thousands. So, failed implementation is a real (and costly) risk.
- Ongoing Considerations: The work’s not over even after your new machine learning model is up and running. Ongoing data storage, testing, fine-tuning, retraining, and performance monitoring obligations will require significant amounts of time, attention, money, and specialized expertise.
While AI-driven fraud prevention offers numerous advantages, there are situations where manual reviews remain the preferred choice. Balancing the use of technology with human expertise can help businesses effectively mitigate risks and maintain a robust fraud prevention strategy.
The Proper Role of Machine Learning Technology
Machine learning has an important role to play in ensuring data integrity and identifying post-transaction threats like friendly fraud, return fraud, and cyber shoplifting. That role is very different from conventional fraud detection machine learning, though.
Using machine learning for more intelligent chargeback source detection lets you:
- Look beyond reason codes to find the true sources of chargebacks
- Be more proactive about future disputes
- Identify new revenue opportunities
- Reduce fees, overhead, and other costs
- Eliminate false positives and accept more transactions
An end-to-end solution powered by machine learning fraud detection technology is the only way to see true revenue recovery and sustainable growth.
FAQs
How can machine learning detect fraud?
The program tests incoming information to see if it either contradicts or reinforces an existing algorithm, then adjusts accordingly. The more data the machine receives, the more reliable its predictions will be.
What are the three different real-time machine learning fraud detection methods?
Three common models for real-time machine learning fraud detection include anomaly detection, which is aimed at detecting data outliers that deviate from normal patterns, as well as supervised and unsupervised machine learning. The former involves an algorithm trained to recognize patterns and determine outcomes, while the latter relies on an algorithm that processes unlabeled data to detect hidden relations between data points.
How can banks use machine learning for fraud detection?
Machine learning fraud detection offers advantages over traditional, rules-based fraud solutions as well. Legacy systems depend on absolute “yes/no” answers. This means someone must constantly monitor, review, and update the technology manually. A fraud detection machine learning model, however, is designed to adapt to new information on its own.
How does machine learning work in fraud detection?
Once trained and deployed, machine learning fraud detection models analyze your fraud surface for anomalies. These are events that deviate significantly from normal login, checkout, or purchase patterns. Upon detecting an outlier, your fraud detection model may forward the activity for manual review or block the transaction or user outright.
What is the best machine learning algorithm for fraud detection?
Because each fraud environment is unique, no single machine learning algorithm can be considered “best.” That said, random forests, neural networks, extreme gradient boosting (XGBoost), logistic regression, and decision trees have shown to be effective for fraud detection.
Which algorithm is used for fraud detection?
Algorithms like neural networks, random forests, and extreme gradient boosting (XGBoost) are popular choices for fraud detection applications.
Can deep learning be used for fraud detection?
Yes, deep learning techniques like convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs) and autoencoders are especially useful in detecting complex fraud patterns.
