The prevention of credit card fraud is an important commercial application for prediction methods and computational intelligence of pattern recognition approaches. Computational intelligence is an association of bio-inspired computational methodologies which are founded principally on neural networks, fuzzy systems, evolutionary computation, swarm intelligence, and probabilistic computation. The application of computational intelligence techniques in the support of service tasks such as anomaly detection and identification, pattern classification, diagnostics, prognostics, estimation and control has recently emerged in industrial and commercial environments. This paper presents an efficient system based on computational intelligence methodologies for the detection of fraud in the operation of real credit card data in E-commerce transactions. This new proposed method, called FControl® for fraud detection and classification, integrates concepts of hybrid intelligent systems based on neural networks, fuzzy systems and evolutionary computation approaches. The proposed method generates quality solutions in terms of prediction efficiency and success. The computational program of the FControl® system has been installed on an Intel Pentium IV 2.4 MHz (bi-processor and 4 Gbytes RAM) at the company Ciashop E-Commerce and is currently in use for fraud detection by 250 companies that offer E-commerce services.
information technology; E-commerce; artificial intelligence; fraud detection; credit card; neuro-fuzzy systems; evolutionary Computation
