This paper describes a smart-card based solution that further extends the concept of static access control list by performing real-time analysis over user's access patterns, thus allowing early detection of abnormal card usages. A list of irregular behaviors are identified and classified to formulate a logical model by using pure empirical analysis. Based on this model, a real-time suspicious action detection prototype is developed, allowing rapid alert and reaction towards irregular behaviors. The later part of this paper will focus on data analysis over access information of either identified or anonymous user. Results can be used to refine the logical model and reveal user consumption behavior information for marketing purpose as well. Finally, example scenarios on some applications on electronic commerce are discussed.