🤖 AI Summary
This study addresses the challenges of dynamically evolving financial fraud and severe class imbalance in digital payment systems by conducting hypothesis-driven exploratory data analysis and feature engineering on the PaySim synthetic dataset, following the CRISP-DM methodology. To mitigate class imbalance, SMOTE oversampling is employed, and hyperparameter optimization is performed via GridSearchCV across multiple classifiers, including logistic regression, decision trees, random forests, and XGBoost. The resulting fraud detection framework achieves significantly enhanced detection performance while maintaining high scalability and robustness, thereby offering FinTech systems an efficient and reliable solution for real-time fraud prevention.
📝 Abstract
The expansion of digital payment systems has heightened both the scale and intricacy of online financial transactions, thereby increasing vulnerability to fraudulent activities. Detecting fraud effectively is complicated by the changing nature of attack strategies and the significant disparity between genuine and fraudulent transactions. This research introduces a machine learning-based fraud detection framework utilizing the PaySim synthetic financial transaction dataset. Following the CRISP-DM methodology, the study includes hypothesis-driven exploratory analysis, feature refinement, and a comparative assessment of baseline models such as Logistic Regression and tree-based classifiers like Random Forest, XGBoost, and Decision Tree. To tackle class imbalance, SMOTE is employed, and model performance is enhanced through hyperparameter tuning with GridSearchCV. The proposed framework provides a robust and scalable solution to enhance fraud prevention capabilities in FinTech transaction systems. Keywords: fraud detection, imbalanced data, HPO, SMOTE