Detecting Fraudulent Transactions Using Machine Learning

With the rapid growth of online shopping, e-commerce platforms face the growing challenge of transaction fraud — unauthorized purchases, fake refund claims, account hijacking, and promotional abuse. Traditional rule-based fraud detection systems struggle to keep up with evolving fraud techniques. Machine learning can help by detecting anomalies based on behavior patterns, transaction histories, and customer profiles, allowing real-time intervention before financial losses occur.

Using transaction datasets containing labeled records of fraudulent and legitimate purchases, machine learning models like Random Forests, XGBoost, Isolation Forest, and Autoencoders can detect anomalies. Behavioral features like average purchase value, frequency, location, device fingerprints, and session times are engineered to improve fraud identification. Continuous model training ensures adaptation to new fraud strategies, strengthening the platform’s security over time.

Collect datasets containing online transaction data with fraud labels. Preprocessing steps include feature extraction (transaction amount, location, device ID, session length), encoding categorical features, and addressing class imbalance. Train supervised classification models to detect fraud or unsupervised models to detect anomalies. Model evaluation uses metrics like precision, recall, and F1-score, ensuring that fraudulent transactions are identified accurately without overly flagging legitimate ones.

• Collect or simulate e-commerce transaction datasets with labeled legitimate and fraudulent transactions.
• Engineer behavioral features like average purchase frequency, cart size variance, geolocation discrepancies, etc.
• Train supervised models like XGBoost or Random Forest, or unsupervised models like Isolation Forest or Autoencoders for anomaly detection.
• Evaluate models using recall and precision to balance minimizing false negatives (missed fraud) and false positives (flagging legit users).
• Deploy an intelligent fraud detection API that alerts security teams or automatically blocks suspicious transactions in real-time.

• Kaggle Credit Card Fraud Detection Dataset
• Anomaly Detection with Isolation Forest
• Real-time Fraud Detection Case Studies