Fraud Detection in Credit Card Transactions; Using Parallel Processing of Anomalies in Big Data
In parallel to the increasing use of electronic cards, especially in the banking industry, the volume of transactions using these cards has grown rapidly. Moreover, the financial nature of these cards has led to the desirability of fraud in this area. The present study with Map Reduce approach and p...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | fas |
| Published: |
University of Tehran
2016-10-01
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| Series: | Journal of Information Technology Management |
| Subjects: | |
| Online Access: | https://jitm.ut.ac.ir/article_57818_b29276e7cf5fa9d519b2b3ec7e4ffb8e.pdf |
| Summary: | In parallel to the increasing use of electronic cards, especially in the banking industry, the volume of transactions using these cards has grown rapidly. Moreover, the financial nature of these cards has led to the desirability of fraud in this area. The present study with Map Reduce approach and parallel processing, applied the Kohonen neural network model to detect abnormalities in bank card transactions. For this purpose, firstly it was proposed to classify all transactions into the fraudulent and legal which showed better performance compared with other methods. In the next step, we transformed the Kohonen model into the form of parallel task which demonstrated appropriate performance in terms of time; as expected to be well implemented in transactions with Big Data assumptions. |
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| ISSN: | 2008-5893 2423-5059 |