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How Artificial Intelligence and machine learning research impacts payment card fraud detection: A survey and industry benchmark

Ryman-Tubb, Nick F., Krause, Paul and Garn, Wolfgang (2018) How Artificial Intelligence and machine learning research impacts payment card fraud detection: A survey and industry benchmark Engineering Applications of Artificial Intelligence, 76. pp. 130-157.

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The core goal of this paper is to identify guidance on how the research community can better transition their research into payment card fraud detection towards a transformation away from the current unacceptable levels of payment card fraud. Payment card fraud is a serious and long-term threat to society (Ryman-Tubb and d’Avila Garcez, 2010) with an economic impact forecast to be $416bn in 2017 (see Appendix A).1 The proceeds of this fraud are known to finance terrorism, arms and drug crime. Until recently the patterns of fraud (fraud vectors) have slowly evolved and the criminals modus operandi (MO) has remained unsophisticated. Disruptive technologies such as smartphones, mobile payments, cloud computing and contactless payments have emerged almost simultaneously with large-scale data breaches. This has led to a growth in new fraud vectors, so that the existing methods for detection are becoming less effective. This in turn makes further research in this domain important. In this context, a timely survey of published methods for payment card fraud detection is presented with the focus on methods that use AI and machine learning. The purpose of the survey is to consistently benchmark payment card fraud detection methods for industry using transactional volumes in 2017. This benchmark will show that only eight methods have a practical performance to be deployed in industry despite the body of research. The key challenges in the application of artificial intelligence and machine learning to fraud detection are discerned. Future directions are discussed and it is suggested that a cognitive computing approach is a promising research direction while encouraging industry data philanthropy.

Item Type: Article
Divisions : Faculty of Arts and Social Sciences > Surrey Business School
Authors :
Ryman-Tubb, Nick
Date : 22 September 2018
DOI : 10.1016/j.engappai.2018.07.008
Uncontrolled Keywords : Fraud detection; Financial crime; AI; Machine learningP; ayments card; Cyber-crime; Translational research
Depositing User : Clive Harris
Date Deposited : 14 Nov 2018 09:57
Last Modified : 14 Nov 2018 09:57

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