HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Conference papers

Cost-Sensitive Decision Making for Online Fraud Management

Abstract : 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.
Document type :
Conference papers
Complete list of metadata

Cited literature [25 references]  Display  Hide  Download

Contributor : Hal Ifip Connect in order to contact the contributor
Submitted on : Friday, June 22, 2018 - 11:45:13 AM
Last modification on : Friday, June 22, 2018 - 12:00:50 PM
Long-term archiving on: : Monday, September 24, 2018 - 6:49:47 PM


Files produced by the author(s)


Distributed under a Creative Commons Attribution 4.0 International License



Mehmet Yildirim, Mert Ozer, Hasan Davulcu. Cost-Sensitive Decision Making for Online Fraud Management. 14th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), May 2018, Rhodes, Greece. pp.323-336, ⟨10.1007/978-3-319-92007-8_28⟩. ⟨hal-01821053⟩



Record views


Files downloads