Online Banking Fraud Detection Based on Local and Global Behavior
Stephan Kovach, Wilson Vicente Ruggiero · 2011
This paper presents a fraud detection system proposed for online banking that is based on local and global observations of users' behavior. Differential analysis is used to obtain local evidence of fraud where a significant deviation from normal behavior indicates a potential fraud. This evidence is strengthened or weakened by the user's global behavior. In this case, the evidence of fraud is based on the number of accesses performed by the user and by a probability value that varies over time. The Dempster's rule of combination is applied to these evidences for final suspicion score of fraud. Our main contribution is a fraud detection method based on effective identification of devices used to access the accounts and assessing the likelihood of being a fraud by tracking the number of different accounts accessed by each device.