Scalable Machine Learning Solutions for High-Volume Financial Transaction Fraud Detection

More reliable and intelligent detection systems are required because of the rise in fraudulent activities brought on by the volume of digital financial transactions. In this work, the data used is from a publicly accessible dataset with more than a million transaction records to investigate a machine learning strategy to identify hidden patterns in the fraud transaction. Data preprocessing included applying Z-score normalization, eliminating outliers using the IQR method, and handling missing values according to the skewness of each attribute. The selection of important features was guided by correlation analysis using Chi-square tests and Pearson coefficients. This study implemented multiple supervised learning techniques, comprising Random Forest, Logistic Regression, K-Nearest Neighbors, and Gradient Boost to evaluate and compare their effectiveness in accurately detecting fraudulent transactions.