Implementing Hybrid AI And Fuzzy Logic Systems for Real-Time Fraud Detection in Banking

This study presents a comparative evaluation of various fraud detection techniques in the banking sector,focusing on traditional methods, advanced AI-based approaches, and hybrid systems incorporatingfuzzy logic. Traditional rule-based and statistical methods are benchmarked against machine learningmodels such as Support Vector Machines (SVM) and Random Forest, as well as deep learning techniqueslike neural networks. The hybrid system, integrating AI with fuzzy logic, is also assessed. Experimentalresults reveal that while traditional methods offer moderate performance, machine learning and deeplearning models significantly improve accuracy, precision, and recall in fraud detection. The hybridAI and fuzzy logic system outperforms all other techniques, achieving the highest accuracy and recallrates despite a longer processing time. This comprehensive analysis highlights the superior effectivenessof advanced and hybrid methods in handling the complexities of real-time fraud detection, offeringvaluable insights for enhancing security measures in the banking industry.