How to optimize ML fraud detection: A guide to monitoring & performance

Fraud detection is a mainstream machine learning (ML) use case. In recent years, the demand for AI-powered fraud detection systems has been increasing as fraud cases are soaring and fraudsters are using more advanced techniques. 

In 2021, consumers reportedly lost USD 5.8 billion to fraud. The Federal Trade Commission data shows that around 2.8 million consumers reported fraud. Organizations are employing various techniques to detect fraud in their processes and operations.

This article will explain various fraud detection techniques using data monitoring and ML predictive models. In the end, we’ll also explain how Aporia improves the performance of fraud detection models.

What is fraud detection & what are various types of fraud?

Fraud detection is a process of identifying a crime that aims to deceive people for the sake of acquiring financial gains illegally. Fraud has many types, some of which commonly include:

• Credit card fraud: One of the most common fraud types in the finance industry, reaching $43 billion by 2026. It involves acquiring someone’s credit card information for making transactions or providing false information for obtaining a new credit card.
• Insurance fraud: Hard fraud involves deliberately fabricating claims or incidents to receive financial benefits, while soft fraud involves embellishing legitimate claims to secure a larger payout than warranted.
• Product warranty fraud: The fraudulent claim on a product warranty made with ill-intention to deceive the warranty provider.
• Healthcare fraud: Deliberately falsifying medical records and healthcare claims for financial gain or other benefits.
• Telecommunications fraud: Illegally exploiting telecom service providers to commit various types of fraud, including but not limited to bypass fraud, SIMBox fraud, and PBX hacking.
• Identity theft: Illegally acquiring another person’s personal information for the purpose of conducting financial transactions, obtaining credit, or committing other forms of fraud.

Most fraudulent schemes are often carefully organized and evolve. Detecting fraud requires thorough investigation and confirmation, usually conducted via automated fraud detection algorithms purpose-built for various fraud categories.

Fraud can also be detected via human experts who carefully analyze each process and workflow that fraudsters could exploit. However, this approach is labor-intensive, costly, and at times ineffective.

On the contrary, AI-based fraud detection systems are widely adopted to identify various fraud types because they offer more precision, cost-effectiveness, and operational efficiency.

Let’s discuss different aspects of fraud detection and prevention techniques.

Fraud detection & prevention techniques – How to monitor fraud models?

Fraud detection and prevention involve investigating data and making robust predictive models based on historical information. Let’s look at some of these techniques below.

Data monitoring techniques for fraud detection

Transactional, contractual, and account data is thoroughly analyzed in fraud detection using various data monitoring techniques, some of which include:

1. Descriptive statistics

It involves calculating statistical attributes of data to understand its general behavior and patterns. Descriptive statistics include measuring mean, median, mode, and skewness values.

2. Missing values

Missing information in data often indicates some form of data corruption or mishandling. Various statistical techniques are available to handle missing values, including data imputation (replace missing data with a suitable value) or deletion.

3. Outlier detection

Outliers are over-the-top extreme values present in the data. Such values could be an indication of fraudulent records. It is important to identify outliers and treat them as needed. Various statistical and visual measures can help identify outliers, such as z-score and box plots. 

Another outlier detection solution includes using models that are relatively robust against outliers, such as neural networks and SVMs. 

4. Benford’s law

Benford’s law numerically measures and visually describes the frequency distribution of the first digit of natural numbers, like a six-digit payment amount. It is commonly used as a screening test for fraud detection. 

Benford law assumes that numbers like payment records are not random. So it finds a pattern in the first digit of these payment records. If a fraudster submits numerous fake entries, Benford’s law will screen those entries as anomalous. 

Predictive models for fraud detection

AI predictive modeling techniques are very effective in detecting fraud. These techniques are usually of two types: regression and classification. 

A taxonomy of various fraud detection ML models. Tiwari, Pooja, et al. Credit Card Fraud Detection Using Machine Learning: A Study. Image Source

Regression techniques predict the amount of fraud, while classification techniques usually identify if a certain record is fraudulent or not. Or they can determine the severity of the fraud, like low, medium, and high. 

Let’s discuss some of the predictive modeling techniques for fraud detection below. A detailed taxonomy of fraud detection models is shown in the image above.

1. Linear regression

Linear regression is one of the most widely used statistical algorithms to predict the amount of fraud. It draws a straight line that minimizes the distance between the line and the data points. It can analyze the patterns available in the input variables (data features) to predict the appropriate output variable (amount of fraud).

For instance, in insurance fraud, data features can include the claimant’s age, policy percentage participation, reported accident type, claimed amount, etc. The linear regression model can predict the claimed insurance amount to observe the difference between the predicted amount and the actual claimed amount.

2. Logistic regression

Logistic regression classifies records as “fraud” or “not-fraud” by analyzing the input records. It is a binary classifier that ranges between the probability distribution of 0 and 1. A threshold value (usually 0.5) is set, which signifies how the two classes, “fraud” and “not-fraud,” are interpreted by the classifier.

3. Decision tree

In fraud analytics, the use of decision trees is manifold. Its tree-like structure places the most appropriate data features at the top. Such features contribute more towards fraud prediction. 

A decision tree can be used with other models, such as logistic regression, for further refinement of prediction results. For instance, the decision tree could identify high-value data features which can be fed to a logistic regression model to fine-tune its fraud detection results.

4. Neural networks

Neural networks can learn the hidden patterns in complex data to classify records as fraudulent or safe. They have greater generalization capabilities compared to other predictive models. 

A neural network’s base unit is a neuron resembling a simple logistic regression model. The neural network could increase or decrease the number of neurons vertically and horizontally to increase its processing and generalization power. These neurons stack up to form layers. 

Neural networks usually have three layers: the input layer, the hidden layer, and the output layer. All the magical stuff happens in the hidden layer.

Neural networks such as graph neural networks or autoencoder neural networks can detect fraud effectively.

5. Ensemble methods

Ensemble methods use multiple models to build a more powerful prediction model. The basic intuition of ensemble methods is to train weak models and use their prediction results to create a robust model that compensates for the deficiencies of the weaker models. Each weaker model covers a portion of the input data. 

Ensemble methods are of three types: Bagging, Boosting, and Stacking, as illustrated below. 

Random forest is the most widely used example of the bagging ensemble method. In a fraud detection scenario, a random forest combines the results of various weak decision trees to make better fraud predictions.

ML monitoring metrics for evaluating fraud detection predictive models

Predictive models are only effective if they give favorable results on unseen real-world data. To evaluate the robustness of the model, analysts use various monitoring metrics that measure the model’s performance on real data. 

Two of the most commonly used monitoring metrics for fraud detection predictive models are:

1. Confusion Matrix

A confusion matrix is a report or table which summarizes the results of a classification model. For instance, in a binary classification fraud detection task, the confusion matrix would record the number of accurate and inaccurate predictions. 

Specifically, it records the following four attributes: True Positive, True Negative, False Positive, and False Negative.

Let’s consider 100 records of credit card data, out of which 30 records are fraudulent. An ideal model should predict 70 records as “not-fraud” and 30 as “fraud.” Let’s map it to the four attributes of the confusion matrix:

• True Positive (TP): Total number of records where the observed or actual record is “fraud” and the predicted value label is also “fraud.”
• True Negative (TN): Total number of records where the observed or actual record is “not-fraud” and the predicted value label is also “not-fraud.”
• False Positive (FP): Total number of records where the observed or actual record is “not-fraud” and the predicted value label is “fraud.”
• False Negative (FN): Total number of records where the observed or actual record is “fraud” and the predicted value label is “not-fraud.”

Based on these values, analysts can derive various performance metrics like accuracy, precision, recall, specificity, and f1-score. Together, these metrics provide a holistic overview of the prediction results.

2. AUC-ROC Curve

Area Under The Curve-Receiver Operating Characteristics (AUC-ROC) curve visually tracks the performance of the classification model at various threshold values. The ROC curve is plotted against two attributes: 

• True Positive Rate (TPR): Also termed as recall or sensitivity. TPR is plotted on the y-axis. It is formulated as:

TPR = TP / TP + FN

• False Positive Rate (FPR): FPR is plotted on the x-axis. It is formulated based on the specificity metric. FPR is formulated as:

FPR = 1 – Specificity = 1 – TN / TN + FP = FP / TN + FP

The ROC curve plots AUC threshold values between 0 and 1. A fraud detection predictive model that gives an AUC score closer to 1 represents an accurate model, while an AUC score closer to 0 represents an inaccurate model.

Improving fraud detection model performance with Aporia

As fraud detection techniques evolve to use increasingly sophisticated machine learning models, the need for robust monitoring and observability becomes paramount. Aporia offers a suite of features that not only enhances the performance of your fraud detection models but also ensures they are running optimally in a production environment.

Visibility

Aporia’s customizable dashboards allow you to keep a real-time pulse on your fraud detection models. Tailor your dashboard to showcase the metrics that are most crucial for your use-case, such as precision, recall, and F1 score. With a clear visualization of how your model is performing, you can take immediate action if you notice any anomalies, thereby maintaining a high level of security and efficiency.

Drift monitoring

Aporia’s monitoring features offer live alerts that notify you of any drift or hallucinations in model performance directly to Slack, MS Teams, or email. By supporting every use case, Aporia enables you to easily customize monitoring for your fraud detection model. These early warnings enable you to take proactive measures to recalibrate or fine-tune your model, ensuring that it remains effective over time.

Production Investigation Room

Sometimes the metrics don’t tell the whole story, and a deeper dive is required to understand the intricacies of model behavior. Aporia’s Production IR feature lets you investigate and explore production data in a collaborative, notebook-like experience. Whether you’re troubleshooting a false negative or exploring why a particular type of transaction is consistently flagged, the Production IR offers a nuanced analysis that can pinpoint the root cause. Also, leverage explainability tools to understand feature impact, communicate model performance to business and product stakeholders, and ensure the transparency of your fraud detection. 

Monitor ML Models For Every Use Case

Fraud detection is a typical ML monitoring use case. Analysts, data scientists, and engineers can set various metrics and threshold values to track and monitor the performance of their fraud detection models, gain insights to improve performance, and stay ahead of fraud risk.

Aporia is a robust ML monitoring and explainability tool that can track your models for various use cases. Besides fraud detection, it includes the following use cases:

• Churn prediction
• Lead scoring
• Demand forecasting
• NLP, LLM, GenAI, CV
• Recommendation & Ranking
• Pricing
• Credit Risk
• Customer LTV

If you want to see Aporia in action, book a demo today. For a more hands-on feel, enter our sandbox and see how Aporia works. 

Noa Azaria

How-ToMachine Learning