Evaluation of deep neural networks for reduction of credit card fraud alerts
Identifiers
Permanent link (URI): http://hdl.handle.net/10017/60624DOI: 10.1109/ACCESS.2020.3026222
ISSN: 2169-3536
Publisher
IEEE
Date
2020-09-23Bibliographic citation
San Miguel Carrasco, R. & Sicilia Urbán, M.A. 2020, "Evaluation of deep neural networks for reduction of credit card fraud alerts", IEEE Access, vol. 8, pp. 186421-186432.
Keywords
Neural networks
Deep learning
Fraud detection
Alert reduction
Document type
info:eu-repo/semantics/article
Version
info:eu-repo/semantics/publishedVersion
Publisher's version
https://doi.org/10.1109/ACCESS.2020.3026222Rights
Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
Access rights
info:eu-repo/semantics/openAccess
Abstract
Fraud detection systems support advanced detection techniques based on complex rules, statistical modelling and machine learning. However, alerts triggered by these systems still require expert judgement to either confirm a fraud case or discard a false positive. Reducing the number of false positives that fraud analysts investigate, by automating their detection with computer-assisted techniques, can lead to significant cost efficiencies. Alert reduction has been achieved with different techniques in related fields like intrusion detection. Furthermore, deep learning has been used to accomplish this task in other fields. In our paper, a set of deep neural networks have been tested to measure their ability to detect false positives, by processing alerts triggered by a fraud detection system. The performance achieved by each neural network setting is presented and discussed. The optimal setting allowed to capture 91.79% of total fraud cases with 35.16% less alerts. Obtained alert reduction rate would entail a significant reduction in cost of human labor, because alerts classified as false positives by the neural network wouldn't require human inspection.
Files in this item
Files | Size | Format |
|
---|---|---|---|
Evaluation_San_Miguel_IEEE_Acc ... | 2.453Mb |
|
Files | Size | Format |
|
---|---|---|---|
Evaluation_San_Miguel_IEEE_Acc ... | 2.453Mb |
|
Collections
- CCOMPUT - Artículos [86]