PUBLISHED PAPERS #12.01
| Rasul Jafarov. Self-Adaptive Fraud Detection Systems for Card-Based Top-Up Operation |
|---|
| Abstract. In an increasingly digital world, the rise of electronic transactions has simultaneously led to an increase in fraudulent activity, particularly in card-based top-up operations. Such operations, where users top up their digital accounts, are often a target due to the ephemeral nature of these transactions and the anonymity they offer. This duality of progress and risk requires innovative solutions that can adapt to evolving threats. Self-adapting fraud detection systems are at the forefront of this challenge. They use artificial intelligence and machine learning algorithms to detect and mitigate fraudulent activity dynamically and in real time. By analyzing behavioral patterns and transaction anomalies, these systems provide a robust response to the ever-changing fraud landscape, ensuring both user safety and operational integrity. Ultimately, implementing such systems not only protects consumers but also promotes trust in digital financial transactions. |
| Keywords: Fraud Detection, Logistic Regression, Financial Transactions, Self-Adaptive Systems |
 Download PDF |
| DOI: https://doi.org/10.30546/MaCoSEP2025.060 |