Credit Card Fraud Detection Using Machine Learning: Methods & Project

Here is a fraud detection model with 99.83 percent accuracy: it predicts "not fraud" for every single transaction. On the standard Kaggle credit card fraud dataset, which contains 284,807 transactions of which only 492 are fraudulent (0.172 percent), a model that always predicts the majority class scores near-perfect accuracy while catching exactly zero frauds. This is not a hypothetical scenario. It is what happens when you train a default classifier on imbalanced data without understanding what accuracy actually measures.

Credit card fraud detection using machine learning is the project that teaches you why accuracy can lie, how to handle extreme class imbalance correctly, and why evaluation metrics are not optional extras. It is also one of the most common final-year project choices and one of the most frequently botched in tutorials, where the SMOTE-before-splitting data leakage error inflates results and hides the real challenge.

This guide builds a fraud detector the right way: the balancing strategy comparison done correctly (fold-safe, no leakage), supervised and unsupervised approaches compared head to head, and evaluation framed in business cost rather than abstract metrics. According to the Nilson Report's 2023 analysis, global card fraud losses reached $33.06 billion in 2022 and are projected to exceed $43 billion by 2026. The stakes are real, and the techniques you learn here transfer directly to production systems.

The Hook: 99.8% Accurate and Completely Useless

Let us demonstrate the problem numerically before solving it.

99.83 percent accuracy. Zero frauds detected. This is why accuracy is the wrong metric for fraud detection, and why this project is a masterclass in imbalanced classification.

Understanding the Data (PCA Features and the Imbalance)

The standard benchmark dataset for credit card fraud detection is the ULB Credit Card Fraud Detection dataset on Kaggle, made available by the Free University of Brussels. Here are the exact figures:

The 28 features labeled V1 through V28 are the result of a PCA transformation applied by the original researchers to protect cardholder privacy. This means you cannot interpret them as "transaction amount" or "time of day" in any direct sense. Only the Time and Amount columns retain their original meaning.

Key EDA observations:

• Fraudulent transactions tend to have lower amounts on average (median fraud amount is approximately $25 vs$22 for legitimate transactions, but the distribution differs)
• The Time feature shows fraud transactions are more evenly distributed across the 48-hour window, while legitimate transactions follow a day/night pattern
• Several PCA features (particularly V14, V17, V12, and V10) show noticeably different distributions between fraud and legitimate classes

The PCA anonymization is actually beneficial for learning purposes. It forces you to rely on the model to find discriminative patterns rather than hand-crafting features based on domain knowledge, which is often not available in real fraud detection scenarios either.

Supervised Models: Random Forest and XGBoost

With the balancing strategy chosen, the next decision is which algorithm to use. The two strongest supervised approaches for this dataset are Random Forest and XGBoost.

Random Forest

Random Forest is the reliable default for tabular fraud detection. It handles class imbalance through the class_weight parameter, provides feature importance, and rarely overfits.

For the full algorithm explanation, the Random Forest guide covers bagging, feature randomness, and hyperparameter tuning in detail.

XGBoost

XGBoost typically outperforms Random Forest on this dataset by 2 to 4 percentage points on F1, at the cost of more hyperparameters to tune.
from xgboost import XGBClassifier

The scale_pos_weight parameter is XGBoost's equivalent of class weights. Set it to the ratio of negative to positive examples (approximately 578:1 for this dataset). The XGBoost guide covers gradient boosting mechanics, regularization, and early stopping.

Performance comparison:

XGBoost wins on all metrics and trains faster on this dataset. The existing fraud detection tutorial covers additional approaches and feature engineering strategies.

Build these models hands-on: Scaler's free Supervised Learning course covers Random Forest, XGBoost, and class imbalance handling with structured exercises and real datasets.

The Unsupervised Angle: Isolation Forest

Not every fraud detection scenario has labeled data. In many real-world systems, you know which transactions were flagged but not which were genuinely fraudulent (labels arrive weeks or months later through chargebacks). This is where unsupervised anomaly detection enters.

Isolation Forest detects anomalies by building random trees that isolate data points. Anomalies are easier to isolate (require fewer splits) because they are few and different.
from sklearn.ensemble import IsolationForest

Supervised vs Unsupervised comparison:

The supervised approach wins decisively when labels are available, which they are for this benchmark dataset. But Isolation Forest is the right tool when labels are delayed, incomplete, or unavailable. The anomaly detection guide covers the full landscape of unsupervised approaches including Isolation Forest, Local Outlier Factor, and One-Class SVM.

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Evaluation That Is Not a Lie: PR-AUC and the Cost Matrix

Why PR-AUC Over ROC-AUC

On highly imbalanced data, ROC-AUC can be misleadingly high because the false positive rate denominator (total negatives) is so large that even many false positives produce a low rate. Precision-Recall AUC is the correct metric because both precision and recall focus on the minority class.

A model with ROC-AUC of 0.97 can have a PR-AUC of only 0.65 on the same data. The PR-AUC tells you the real story about how well your model identifies the minority class. For the full metrics comparison, the evaluation metrics guide covers when each metric applies and how to interpret them.

The Business Cost Matrix

Abstract metrics matter less than business outcomes. Here is the cost framing that production fraud teams actually use:

With these costs, you can calculate the optimal decision threshold. If a missed fraud costs approximately $120 and a false alarm costs approximately $10, then the cost ratio is 12:1. Your threshold should be tuned so that you accept up to 12 false alarms for every fraud you catch.

# Threshold tuning based on cost ratio
from sklearn.metrics import precision_recall_curve

Write-Up, Viva Defense, and Real-World Context

Report Structure

A strong project report for credit card fraud detection should follow this structure:

• Problem framing: Why fraud detection matters, the global cost, the imbalance challenge
• Dataset description: ULB dataset characteristics, PCA features, the 0.172 percent fraud rate
• Methodology: Balancing strategy comparison (with the fold-safe SMOTE correction), algorithm comparison, evaluation metric justification
• Results: Head-to-head tables for balancing strategies and algorithms, PR curves, threshold analysis
• Business interpretation: Cost matrix analysis, recommended threshold, projected savings
• Limitations and future work: Temporal drift, adversarial adaptation, real-time serving requirements

The Question Bank (Viva-Ready Answers)

"Why not use accuracy?" With 0.172 percent fraud, a model that predicts "not fraud" for everything scores 99.83 percent accuracy while catching zero frauds. Accuracy treats all errors equally, but a missed fraud costs approximately $120 while a false alarm costs approximately$10. Precision, recall, F1, and PR-AUC focus evaluation on the minority class that actually matters.

"Why fold-safe SMOTE?" Applying SMOTE to the entire dataset before train-test splitting creates synthetic samples that interpolate between test-set points. These synthetic training samples carry information from the test set into training, inflating results by 5 to 8 percentage points. The correct approach applies SMOTE only within each training fold of cross-validation, using imblearn.pipeline.Pipeline to enforce this.

"Why Random Forest or XGBoost over logistic regression?" The PCA-transformed features have complex non-linear interactions that linear models cannot capture. Tree-based models handle these interactions naturally. However, logistic regression with class weights is a valid baseline and trains in seconds, which makes it useful as a quick comparison point.

"How would this work in production?" Production fraud detection runs in real time with sub-100-millisecond latency requirements. The model would be served behind a low-latency API, with features computed at transaction time. Models are retrained frequently (daily or weekly) because fraud patterns evolve. The cost threshold is tuned based on business feedback and updated regularly.

Imbalanced problems are where real ML careers live. From fraud detection to medical diagnosis to defect identification, the hardest and highest-value problems all have imbalanced data. Scaler's AI and ML Program trains you on these production challenges with mentor-led guidance, real datasets, and industry-relevant projects.