Identification of non-typical international transactions on bank cards of individuals using machine learning methods
| Autor: | Elena Kripak, Jenny V. Domashova |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
International level
Computer science business.industry media_common.quotation_subject Generalized algorithm Machine learning computer.software_genre Payment Popularity Payment card Identification (information) General Earth and Planetary Sciences Classification methods Artificial intelligence business computer General Environmental Science Reputation media_common |
| Zdroj: | BICA |
| ISSN: | 1877-0509 |
| DOI: | 10.1016/j.procs.2021.06.023 |
| Popis: | The growing popularity of payment cards has led to the emergence of new types of illegal transactions with money. In particular, the widespread use of non-cash payments has allowed fraud to reach the international level. Therefore, financial institutions are interested in the development and implementation of new effective fraud monitoring systems that will minimize the risk of approving illegal transactions. The article presents the results of applying machine learning methods to detect fraudulent transactions with bank cards. The use of various classification methods in modeling the specified problem is investigated. Generalized algorithm for detecting fraudulent transactions has been developed, which makes it possible to detect atypical international money transfers in real time. Generalized algorithm for detecting atypical international transfers will allow timely detection of potential fraud cases, thereby reducing the total volume of losses from illegal transactions and minimizing the reputation damage caused to the organization. |
| Databáze: | OpenAIRE |
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