Multi-Dimensional Feature Selection and Optimization Algorithms for Financial Fraud Detection: A Comparative Study

Abstract

Financial fraud detection has become increasingly critical as digital transactions proliferate across global markets. This research presents a comprehensive comparative analysis of multi-dimensional feature selection and optimization algorithms applied to fraud detection scenarios. The study evaluates various algorithmic approaches including filter-based methods, wrapper-based techniques, and hybrid optimization strategies across diverse financial transaction datasets. Through systematic experimentation involving over 500,000 transaction records, this research quantifies the performance trade-offs between detection accuracy, computational efficiency, and false positive rates. Results demonstrate that adaptive feature selection methods achieve detection rates exceeding 94.7% while maintaining false positive rates below 2.3%, representing substantial improvements over traditional baseline approaches. The comparative analysis reveals distinct performance characteristics across different algorithm categories, with wrapper-based methods showing superior accuracy at the cost of computational overhead, while filter-based approaches offer faster processing with slightly reduced precision. This work provides empirical evidence to guide practitioners in selecting appropriate feature engineering strategies based on specific operational requirements, system constraints, and risk tolerance thresholds. The findings contribute to the growing body of knowledge in financial security analytics and establish benchmarks for algorithmic performance in fraud detection applications.