<![CDATA[The increasing prevalence of financial fraud in the digital era demands the development of reliable and efficient detection methods. This study aims to conduct a systematic comparative analysis of the effectiveness and consistency of seven anomaly detection methods on simulated transactional financial data. The methods tested cover a broad spectrum, ranging from Benford's Law, robust statistical methods (based on MAD), supervised machine learning (Logistic Regression and Random Forest), to unsupervised machine learning (K-Means Clustering and Isolation Forest). Using a simulation study based on R software, a dataset with 20,000 transactions was generated, 5% of which were manipulated as fraud with clear scenarios. The analysis results show that almost all methods successfully detected anomalies with a high success rate. Supervised models, such as Binary Logistic Regression, showed near-perfect performance, while unsupervised methods such as K-Means and Robust Distance (MAD) also showed recall rates above 95%. Although all methods performed well, there were differences in the trade-off between recall and false positives, underscoring the importance of choosing a method that suits business objectives. This study concludes that a layered approach that combines statistical screening with advanced machine learning models is the most comprehensive approach to fraud mitigation]]>
Copyrights © 2026
