Abstract
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.