Numerical analysis of multivariate data for fraud detection

Fraud detection in financial transactions is crucial for minimizing financial losses and maintaining security in digital environments. This paper presents a methodology for fraud detection using numerical analysis of multivariate data, combining dimensionality reduction, anomaly detection, and clustering techniques. Initially, transaction data undergoes preprocessing to remove inconsistencies, followed by Principal Component Analysis (PCA) to reduce dimensionality, preserving essential patterns while simplifying computation. An Isolation Forest algorithm assigns anomaly scores to each transaction, helping to identify unusual behavior indicative of fraud. KMeans clustering further organizes transactions into groups, making it easier to spot clusters of potentially fraudulent activities based on shared attributes. Finally, a feature correlation matrix enhances model refinement by revealing interdependencies between features, optimizing detection accuracy. Through this multivariate analysis approach, the model efficiently flags suspicious transactions, achieving a balance between identifying fraud and minimizing false positives. This method shows promise for real-time applications in financial sectors where rapid, accurate fraud detection is essential.