Advancing Fraud Detection in Banking: Real-Time Applications of Explainable AI (XAI)

EasyChair Preprint 15717

Abstract

Technological and regulatory changes have transformed the digital footprints of the banking sector. Today, credit card
transactions are providing ten times more data available for fraud detection practices than it was previously available. These larger datasets, combined with the limitations of traditional fraud detection methods, creates an opportunity to adopt Artificial Intelligence (AI) techniques. The effectiveness of AI models in the banking industry for fraud detection is proven but practitioners are slow to adopt this advancement. This was because of the concerns over transparency, trust, and the complexity of integrating these models into existing systems. This paper aims to argue in favor of Explainable Artificial Intelligence (XAI) for fraud detection in the banking sector. XAI enhances transparency, builds trust, and provides clear insights by making AI decisions interpretable and understandable, allowing users to see how and why decisions are made. This paper will explore real-time applications of XAI in the banking sector. It will also highlight the key regulatory changes necessary for effectively integrating AI techniques into banking practices. Lastly, it will encourage future researchers to investigate various aspects of XAI and its potential contribution to improving fraud detection in the banking industry.

Keyphrases: Artificial Intelligence, Banking, Explainable Artificial Intelligence, credit card, fraud detection

@booklet{EasyChair:15717,
  author    = {Sina Ahmadi},
  title     = {Advancing Fraud Detection in Banking: Real-Time Applications of Explainable AI (XAI)},
  howpublished = {EasyChair Preprint 15717},
  year      = {EasyChair, 2025}}