Automatic Card Fraud Detection Based on Decision Tree Algorithm

This paper delves into the analysis of card fraud within the banking system. Its aim is to gain a comprehensive understanding of fraud in the banking sector and explore effective detection techniques. The paper examines advanced techniques such as data analysis, automatic learning algorithms, and re...

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Veröffentlicht in:Applied artificial intelligence Jg. 38; H. 1
Hauptverfasser: Flondor, Elena, Donath, Liliana, Neamtu, Mihaela
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Philadelphia Taylor & Francis 31.12.2024
Taylor & Francis Ltd
Taylor & Francis Group
Schlagworte:
Algorithms
Banking
Data analysis
Decision trees
Effectiveness
Fraud
Machine learning
Qualitative analysis
Real time
ISSN:0883-9514, 1087-6545
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Zusammenfassung:This paper delves into the analysis of card fraud within the banking system. Its aim is to gain a comprehensive understanding of fraud in the banking sector and explore effective detection techniques. The paper examines advanced techniques such as data analysis, automatic learning algorithms, and real-time monitoring systems to detect suspicious patterns, anomalies, and deviations from normal behavior with precision. To achieve this, the research methodology employs a combination of qualitative and quantitative analysis. Furthermore, empirical research is conducted to evaluate the effectiveness of Machine Learning-based decision tree algorithms in identifying card fraud using real-world datasets. By understanding the nature of fraud and implementing robust detection methods, banks can safeguard their operations, assets, and customers, and uphold trust in the banking system.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0883-9514
1087-6545
DOI:10.1080/08839514.2024.2385249