ENHANCING CYBERSECURITY OF PLASTIC CARD TRANSACTIONS IN THE “MY CARD” MOBILE APPLICATION
DOI:
https://doi.org/10.5281/zenodo.18859691Abstract
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-method
approach, incorporating quantitative analysis of transaction data, surveys of 520 users, and machine learning algorithms
for 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 transactions
significantly reduces the risk of financial fraud. Digital behavioral indicators, such as transaction frequency, location
deviation, and device fingerprinting, exhibit strong predictive power in fraud identification. The proposed model can be
practically implemented in mobile banking systems, increasing customer trust, reducing operational risks, and reinforcing
the security of the national payment system. This study represents one of the first empirical, data-driven fraud detection
frameworks adapted to Uzbekistan’s digital payment ecosystem.
Keywords
fraud detection, real-time analytics, digital banking, cybersecurity, anomaly detection, My Card, financial technology.References
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