An Intelligent Fraud Monitoring Model for Protecting Small and Medium Enterprises in Digital Markets

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@article{1713554,
      author = {Nafiu Ikeoluwa Hammed, Gbenga Olumide Omoegun, Oghenemaiga Elebe, Oladapo Fadayomi, Adepeju Deborah Bello},
      title = {An Intelligent Fraud Monitoring Model for Protecting Small and Medium Enterprises in Digital Markets},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {7},
      pages = {1109-1123},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1713554.pdf},
      abstract = {Small and medium enterprises (SMEs) constitute the backbone of modern digital economies, contributing substantially to employment, innovation, and economic growth. However, the rise of digital marketplaces has exposed SMEs to heightened fraud risks, including payment fraud, account takeovers, phishing, and supply chain manipulation. Traditional fraud detection mechanisms often lack the adaptability, speed, and predictive capability necessary to mitigate sophisticated digital threats, leaving SMEs vulnerable to financial losses and reputational damage. This paper proposes an intelligent fraud monitoring model tailored for SMEs in digital markets, integrating machine learning, anomaly detection, and real-time analytics to proactively identify fraudulent patterns. The model leverages historical transactional data, behavioral profiling, and risk scoring to enhance detection accuracy while minimizing false positives. A comprehensive literature review is presented to contextualize recent advances in fraud monitoring, including hybrid analytical approaches, data-driven predictive models, and adaptive cybersecurity strategies. The paper concludes with a conceptual framework for implementing the intelligent monitoring system, highlighting its potential to strengthen SME resilience, inform operational strategies, and support sustainable digital commerce.},
      keywords = {Digital Fraud, SMEs, Intelligent Monitoring, Machine Learning, Anomaly Detection, Risk Assessment},
      month = {January},
      doi = {https://doi.org/10.64388/IREV9I7-1713554}
  }