Fintech: Self Organizing Maps for Fraud Detection
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Published:2024-08
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Page:121-135
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ISSN:
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Container-title:Emerging Technology, Environment and Social Justice- A Sustainable Approach
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language:
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Author:
Arora Amit KumarORCID, Gupta ShwetaORCID
Publisher
QTanalytics India
Reference15 articles.
1. Abdulsattar, K., & Hammad, M. (2020). Fraudulent Transaction Detection in FinTech using Machine Learning Algorithms. International Conference on Innovation and Intelligence for Informatics, Computing and Technologies. https://doi.org/10.1109/3ICT51146.2020.9312025 2. Alkhalil, Z., Hewage, C., Nawaf, L., & Khan, I. (2021). Phishing Attacks: A Recent Comprehensive Study and a New Anatomy. Frontiers in Computer Science, 3. https://doi.org/10.3389/fcomp.2021.563060 3. Bansal, M., Goyal, A., & Choudhary, A. (2022). A comparative analysis of K-Nearest Neighbor, Genetic, Support Vector Machine, Decision Tree, and Long Short Term Memory algorithms in machine learning. Decision Analytics Journal, 3. https://doi.org/10.1016/j.dajour.2022.100071 4. Birgitta Dresp-Langley John Mwangi Wandeto, H. O. N., Dresp-langley, B., Wandeto, J. M., & Nyongesa, H. O. (2018). Using the quantization error from Self Organizing Map (SOM) output for fast detection of critical variations in image time series. Des Données à la Décision -From Data to Decision, 2(1), 1-32. https://www.openscience.fr/Using-the-quantization-error-from-Self-Organizing-Map-SOM -output-for-fast 5. Chicco, G., Napoli, R., & Piglione, F. (2003). Application of clustering algorithms and Self Organising Maps to classify electricity customers. IEEE Bologna PowerTech -Conference Proceedings, 1, 373-379. https://doi.org/10.1109/PTC.2003.1304160
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