Fuzzy Logic Systems in Computational Intelligence for Adaptive Credit Card Fraud Detection

This study aimed to investigate the application of fuzzy logic systems in adaptive credit card fraud detection, highlighting their potential to enhance detection accuracy and reduce false positives. The significance of the present study was derived from the inability of prior fraud detection models to effectively combat new forms of fraud and fraudster behaviours. A qualitative research methodology was used, primarily document analysis and case studies in the selected organisations using fuzzy logic systems. The work showed that fuzzy logic systems increase detection accuracy by 20 percent and decrease false positives compared to traditional approaches. The sampled participants stated that these systems provided for how best to address uncertainty and ambiguity that characterised transactions. Fuzzy logic systems proved to make it easy for organisations to learn about past events and adapt the detection systems accordingly. The consequences for the field of fraud detection were profound because the analysis highlighted the importance of organisations to find more flexible and effective approaches to developing fraud detection methodologies than the strictly rule-based system. Lastly, the study pointed out that personnel should be trained in fuzzy logic and computational intelligence for these systems to be most effective. In conclusion, this research helped to expand the literature with regards to the application of fuzzy logic to improve fraud detection, and aided in promoting more credibility for financial organisations.