Soheila Ehramikar - Dr. TCM, M.Sc.

Along with the rise of credit card use, fraud is on the rise. In Canada, credit card fraud occurrences have been rising sharply, from $147 million in 1998 to 366 million in 2010,causing 150 increase in losses in 12 years. To address fraud, financial institutions (FIs) are employing preventive measures and fraud detection systems one of them called FDS. Although FDS has shown good results in reducing fraud occurrences, the majority of cases (approximately 90%) being flagged by this system are False Positives resulting in substantial investigation costs and cardholder inconvenience. The possibilities of enhancing the current operation by post processing the FDS output constitute the objective of this work. The data used for the analysis was provided by one of the major Canadian banks. Based on several variations and combinations of features and training class distributions, different models (more than fifty) were developed to explore the influence of these parameters on the performance of the desired system. The results indicate that the employed approach and the prototype developed have very good potential to improve on the existing system leading to significant savings for the FIs.

Financial Institutions, Banking industry, Credit Card Fraud losses, Credit card Fraud Detection Systems, Artificial Intelligence