AI-Driven Fraud Detection in UK Digital Payment Systems: Challenges and Solutions

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@article{1709827,
      author = {Adepeju Deborah Bello, Oluwaseyi Babatunde Oguntola, Ayodeji Temitope Ajibade, Akindayo Akindolani, Oluwadamilola Ayoola; Ajifolawe Mubaraq Bello},
      title = {AI-Driven Fraud Detection in UK Digital Payment Systems: Challenges and Solutions},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {9},
      number = {1},
      pages = {1329-1343},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1709827.pdf},
      abstract = {This study presents a systematic literature review on the use of Artificial Intelligence (AI) in fraud detection within the digital payment systems of the United Kingdom. As more people embrace the use of contactless cards, mobile wallets and peer-to-peer payment methods, the UK is on the edge of more sophisticated fraud. This study therefore consolidates peer-reviewed articles and authoritative grey literature on machine learning, deep learning and a hybrid-based model performance in fraud detection. It also investigates operational challenges, such as data asymmetry, model transparency, and regulations like the UK GDPR and FCA sandbox scheme. The findings from this study indicate the necessity of explainable AI, more diverse public datasets, and collaborative regulatory frameworks. This study recommends strategic testing of AI for fraud detection in controlled settings and implementation of a human-in-the-loop system. This paper enriches the discussion of fintech in the UK, explaining the existing possibilities, limitations, and future directions toward having a trustworthy and scalable AI fraud detection system.},
      keywords = {Artificial Intelligence, Digital Payment Systems, Fraud Detection, Machine Learning, United Kingdom. },
      month = {July},
      doi = {https://doi.org/10.64388/IREV9I1-1709827-4729}
  }