Opportunities in Real Time Fraud Detection: An Explainable Artificial Intelligence (XAI) Research Agenda

Regulatory and technological changes have recently transformed the digital footprint of credit card transactions, providing at least ten times the amount of data available for fraud detection practices that were previously available for analysis. This newly enhanced dataset challenges the scalability of traditional rule-based fraud detection methods and creates an opportunity for wider adoption of artificial intelligence (AI) techniques. However, the opacity of AI models, combined with the high stakes involved in the finance industry, means practitioners have been slow to adapt. In response, this paper argues for more researchers to engage with investigations into the use of Explainable Artificial Intelligence (XAI) techniques for credit card fraud detection. Firstly, it sheds light on recent regulatory changes which are pivotal in driving the adoption of new machine learning (ML) techniques. Secondly, it examines the operating environment for credit card transactions, an understanding of which is crucial for the ability to operationalise solutions. Finally, it proposes a research agenda comprised of four key areas of investigation for XAI, arguing that further work would contribute towards a step-change in fraud detection practices.

@article{Mill2023,
  title     = {Opportunities in Real Time Fraud Detection: An Explainable Artificial Intelligence (XAI) Research Agenda},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {5},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Eleanor Mill and Wolfgang Garn and Nick Ryman-Tubb and Chris Turner},
  doi       = {10.14569/IJACSA.2023.01405121},
  url       = {https://doi.org/10.14569/IJACSA.2023.01405121}
}