✨ Executive Summary

In this project, I analyzed a dataset of over 284,000 credit card transactions, where only 0.17% were fraudulent — roughly 1 fraud for every 600 legitimate transactions. Fraudulent activity is rare but costly, making early detection critical for both financial institutions and customers.

Because accuracy is misleading in such an imbalanced dataset, I focused on Precision, Recall, F1‑Score, and ROC‑AUC to evaluate performance. This ensured the models prioritized catching fraud while keeping false positives under control.

I tested multiple approaches, including Logistic Regression, Random Forest, and XGBoost, and applied SMOTE resampling to balance the data. Ensemble models performed best, achieving strong recall and ROC‑AUC scores. Feature importance analysis also highlighted transaction patterns most associated with fraud.

The key takeaway is that fraud detection is about finding the right trade‑off between security and customer experience. I recommend deploying an ensemble‑based fraud detection pipeline with regular retraining, threshold tuning, and real‑time monitoring to reduce financial losses while minimizing customer disruption.

📌 Project Overview

This case study demonstrates how machine learning can be applied to financial fraud detection. Fraudulent transactions cost the financial industry billions each year, making accurate detection critical for both security and customer trust. The dataset is highly imbalanced — with nearly 600 legitimate transactions for every 1 fraud case — making it a strong example of handling real‑world challenges in classification problems.

• Domain: Finance / Fraud Detection
• Dataset: 284,807 transactions, 492 fraud cases (0.17%)
• Tools Used: Python (pandas, scikit‑learn, imbalanced‑learn, matplotlib, seaborn), Jupyter Notebook for reproducible workflows, and visualization outputs (confusion matrix, ROC/PR curves)
• Deliverables: Clean dataset, reproducible notebook, model comparisons, evaluation metrics, visuals, and actionable recommendations

Scope: The dataset covers anonymized European credit card transactions over two days, with 28 PCA‑transformed features plus Time, Amount, and Class.

❓ Business Question

Primary Question:

How can we detect fraudulent transactions in real time while minimizing false positives?

Context:

Fraudulent transactions create significant financial losses and erode customer trust. Even a small percentage of missed fraud can translate into millions in losses for banks and merchants. Detecting fraud early is critical, but false positives — legitimate transactions incorrectly flagged as fraud — can frustrate customers, increase support costs, and damage brand reputation.