Review on Credit Card Fraud Detection using Data Mining Classification Techniques & Machine Learning Algorithms
Data mining (DM) involves a core algorithm that enables data deeper than basic insights and knowledge. In fact, data mining is more part of knowledge discovery process. Credit card (CC) providers provide multiple cards to their customers. All credit card users must be genuine and sincere. Giving a card to any kind of mistake can lead to a financial crisis. Due to the rapid growth in cashless transactions, it is unlikely, Fake transactions can also be increased. A fraudulent transaction can be identified by studying credit cards of various behaviors as a previous transaction history data set. If there is any deviation from the available cost pattern, it is a bogus transaction. DM & machine learning techniques (MLT) are widely applied in credit card fraud detection (CCFD). In this survey paper we show an indication of various widely available DM & MLT for detecting credit card fraud
Year of publication: |
2020
|
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Authors: | Goyal, Rahul |
Other Persons: | Manjhvar, Amit Kumar (contributor) |
Publisher: |
[2020]: [S.l.] : SSRN |
Subject: | Data Mining | Data mining | Zahlungsverkehr | Payment system | Informationstechnik | Information technology | Kreditkarte | Credit card | Klassifikation | Classification | Betrug | Fraud | Algorithmus | Algorithm | Künstliche Intelligenz | Artificial intelligence |
Saved in:
freely available
Extent: | 1 Online-Ressource (4 p) |
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Type of publication: | Book / Working Paper |
Language: | English |
Notes: | In: IJRAR - International Journal of Research and Analytical Reviews (IJRAR), E-ISSN 2348-1269, P- ISSN 2349-5138, Volume.7, Issue 1, Page No pp.972-975, January 2020 Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments January 7, 2020 erstellt |
Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://www.econbiz.de/10012825175
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