Credit Card Risk Detection based on Feature-Filter and Fraud Identification
| Autor: | Nourddine Enneya, Naoufal Rtayli |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
Credit card fraud 02 engineering and technology Filter (signal processing) computer.software_genre Random forest Identification (information) Credit card Feature (computer vision) 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Anomaly detection Data mining Isolation (database systems) computer |
| Zdroj: | 2019 Third International Conference on Intelligent Computing in Data Sciences (ICDS). |
| Popis: | Credit card fraud can destabilise economies, reduce confidence between customers and banks and affect other individuals or companies negatively. The primordial objective of banks is to identify fraudulent transactions with a high level of accuracy to reduce the training time and the costs of the manual investigation activity. This paper proposes a credit card fraud detection method using Random Forest as dimensionality reduction algorithm and Isolation Forest as a fraud detection algorithm. The method is applied to a large dataset in purpose to investigate their fraud detection accuracy. The experimental results of this study confirms the advantages and effectiveness of the proposed method in different criteria: accuracy, sensitivity and training time. |
| Databáze: | OpenAIRE |
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