On atypical database transactions: identification of probable frauds using machine learning for user profiling

Abstract

The paper proposes a framework for deriving users' profiles of typical behaviour and detecting atypical transactions which may constitute fraudulent events or simply a change in user's behaviour. The anomaly detection problem is presented and previous attempts to address it are discussed. The proposed approach proves that individual user profiles can be constructed and provides an algorithm that derives user profiles and an algorithm to identify atypical transactions. Lower and upper bounds for the number of misclassifications are also provided. An evaluation of this approach is discussed and some issues for further research are outlined.