We propose a semi-supervised online banking fraud analysis and decision support approach. During a training phase, it builds a profile for each customer based on past transactions. At runtime, it supports the analyst by ranking unforeseen transactions that deviate from the learned profiles. It uses methods whose output has a immediate statistical meaning that provide the analyst with an easy-to-understand model of each customer’s spending habits. First, we quantify the anomaly of each transaction with respect to the customer historical profile. Second, we find global clusters of customers with similar spending habits. Third, we use a temporal threshold system that measures the anomaly of the current spending pattern of each customer, with respect to his or her past spending behavior. As a result, we mitigate the undertraining due to the lack of historical data for building of well-trained profiles (of fresh users), and the users that change their (spending) habits over time. Our evaluation on real-world data shows that our approach correctly ranks complex frauds as “top priority”.
@inproceedings{Carminati2015BankSealer_An,
title = {{BankSealer: An Online Banking Fraud Analysis and Decision Support System}},
author = {Carminati, Michele and Caron, Roberto and Maggi, Federico and Epifani, Ilenia and Zanero, Stefano},
booktitle = {Proceedings of the 29th Internation Conference on Systems Security and Privacy Protection},
series = {IFIP Advances in Information and Communication Technology},
month = {June},
year = {2014},
copyright = {©2014 IFIP International Federation for Information Processing},
isbn = {978-3-642-55414-8 978-3-642-55415-5},
language = {en},
pages = {380--394},
publisher = {Springer Berlin Heidelberg},
url = {http://link.springer.com/chapter/10.1007/978-3-642-55415-5_32}
}