The purpose of this study is to develop an intelligent real-time anomaly detection model within the “My Card”mobile application to enhance the cybersecurity of plastic card transactions. The research utilizes a mixed-methodapproach, incorporating quantitative analysis of transaction data, surveys of 520 users, and machine learning algorithmsfor detecting anomalous transactions. Fraud patterns were evaluated through statistical analysis, correlation analysis,and supervised classification algorithms. The findings demonstrate that real-time detection of anomalous transactionssignificantly reduces the risk of financial fraud. Digital behavioral indicators, such as transaction frequency, locationdeviation, and device fingerprinting, exhibit strong predictive power in fraud identification. The proposed model can bepractically implemented in mobile banking systems, increasing customer trust, reducing operational risks, and reinforcingthe security of the national payment system. This study represents one of the first empirical, data-driven fraud detectionframeworks adapted to Uzbekistan’s digital payment ecosystem.
| Mualliflar | Kurbonbekova , Mohichehra, Mashrapova , Sabina |
|---|---|
| Jurnal | Innovation science and technologiy |
| Nashr sanasi | 2025-10-01 |
| Jild | 1 |
| Son | 10 |
| Til | Ingliz |
| DOI | 10.5281/zenodo.18859691 |
DOI: 10.5281/zenodo.18859691 · Maqolaning asl sahifasi
fraud detection, real-time analytics, digital banking, cybersecurity, anomaly detection, My Card, financial technology.
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