TY - GEN
T1 - Cost-sensitive decision making for online fraud management
AU - Yildirim, Mehmet Yigit
AU - Ozer, Mert
AU - Davulcu, Hasan
N1 - Publisher Copyright:
© IFIP International Federation for Information Processing 2018 Published by Springer International Publishing AG 2018. All Rights Reserved.
PY - 2018
Y1 - 2018
N2 - Every online transaction comes with a risk and it is the merchant’s liability to detect and stop fraudulent transactions. Merchants utilize various mechanisms to prevent and manage fraud such as automated fraud detection systems and manual transaction reviews by expert fraud analysts. Many proposed solutions mostly focus on fraud detection accuracy and ignore financial considerations. Also, highly effective manual review process is overlooked. We propose Profit Optimizing Neural Risk Manager (PONRM), a decision maker that (a) constitutes optimal collaboration between machine learning models and human expertise under industrial constraints, (b) is cost and profit sensitive. We suggest directions on how to characterize fraudulent behavior and assess the risk of a transaction. We show that our framework outperforms cost-sensitive and cost-insensitive baselines on three real-world merchant datasets.
AB - Every online transaction comes with a risk and it is the merchant’s liability to detect and stop fraudulent transactions. Merchants utilize various mechanisms to prevent and manage fraud such as automated fraud detection systems and manual transaction reviews by expert fraud analysts. Many proposed solutions mostly focus on fraud detection accuracy and ignore financial considerations. Also, highly effective manual review process is overlooked. We propose Profit Optimizing Neural Risk Manager (PONRM), a decision maker that (a) constitutes optimal collaboration between machine learning models and human expertise under industrial constraints, (b) is cost and profit sensitive. We suggest directions on how to characterize fraudulent behavior and assess the risk of a transaction. We show that our framework outperforms cost-sensitive and cost-insensitive baselines on three real-world merchant datasets.
KW - Cost-sensitive learning
KW - E-commerce
KW - Fraud detection
KW - Risk management
UR - http://www.scopus.com/inward/record.url?scp=85049595509&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85049595509&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-92007-8_28
DO - 10.1007/978-3-319-92007-8_28
M3 - Conference contribution
AN - SCOPUS:85049595509
SN - 9783319920061
T3 - IFIP Advances in Information and Communication Technology
SP - 323
EP - 336
BT - Artificial Intelligence Applications and Innovations - 14th IFIP WG 12.5 International Conference, AIAI 2018, Proceedings
A2 - Plagianakos, Vassilis
A2 - Maglogiannis, Ilias
A2 - Iliadis, Lazaros
PB - Springer New York LLC
T2 - 14th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2018
Y2 - 25 May 2018 through 27 May 2018
ER -