<![CDATA[This study evaluates the Local Outlier Factor (LOF) algorithm for credit card fraud detection, emphasizing its effectiveness with imbalanced datasets. Unlike traditional methods that struggle with the rarity and variability of fraudulent transactions, LOF uses local density deviations to identify anomalies. Through a rigorous methodology involving data preprocessing, parameter tuning, and comparison with other machine learning algorithms, LOF demonstrated a high recall rate and a balanced precision-recall trade-off, excelling at detecting subtle, localized fraud. Challenges like threshold setting and false positives were noted, with future research suggested on real-time system integration, algorithm combination, and advanced feature engineering. The study underscores LOF's strengths and limitations, contributing to enhanced fraud detection strategies]]>
