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A Survey on Outlier Detection in Financial Transactions
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 108 - Number 17 |
| Year of Publication: 2014 |
| Authors: Pradnya Kanhere, H. K. Khanuja |
10.5120/19004-0502
|
Pradnya Kanhere, H. K. Khanuja . A Survey on Outlier Detection in Financial Transactions. International Journal of Computer Applications. 108, 17 ( December 2014), 23-25. DOI=10.5120/19004-0502
Abstract
Outlier detection is a very important concept in the data mining. It is useful in data analysis. Nowadays, a direct mapping can be found between the data outliers and real world anomalies. Hence the outlier detection techniques can be applied to detect the abnormal activities in the real world. Outlier detection has been researched within various application domains and knowledge disciplines. This survey provides an overview of existing outlier detection techniques that can be applied in the financial domain. It mainly focuses on the idea of detecting the suspicious or outlier financial transactions.
References
- Outlier detection- Jiawei Han, Micheline Kamber, Jian Pei, "Data Mining: Concepts and Techniques"
- Harmeet Kaur Khanuja, Dattatraya S. Adane, "Forensic Analysis for Monitoring Database Transactions", Springer, Computer and Information Science Volume 467, pp 201-210, 2014
- Quratulain Rajput, Nida Sadaf Khan, Asma Larik, Sajjad Haider, "Ontology Based Expert-System for Suspicious Transactions Detection", Canadian Center of Science and Education, Computer and Information Science; Vol. 7, No. 1, 2014
- Anu, Dr. Rajan Vohra, "Identifying Suspicious Transactions in Financial Intelligence Services", International Journal of Computer Science & Management Studies, Vol. 14, Issue 07, 2014
- Mahesh Kharote, V. P. Kshirsagar, "Data Mining Model for Money Laundering Detection in Financial Domain", International Journal of Computer Applications (0975 – 8887), Volume 85 – No 16, 2014
- Nida S. Khan, Asma S. Larik, Quratulain Rajput, Sajjad Haider, "A Bayesian approach for suspicious financial activity reporting", International Journal of Computers and Applications, Vol. 35, No. 4, 2013
- Dang Khoa Cao and Phuc Do, "Applying Data Mining in Money Laundering Detection for the Vietnamese Banking Industry", Springer-Verlag Berlin Heidelberg, 2012
- Edgar Alonso Lopez-Rojas, Stefan Axelsson, "Money Laundering Detection using Synthetic Data", Linkping Electronic Conference Proceedings, No. 71, 2012
- Deysy Galeana Prez Manuel Meja Lavalle, "Outlier Detection Applying an Innovative User Transaction Modeling with Automatic Explanation", IEEE, Electronics, Robotics and Automotive Mechanics Conference, 2011
- Asma S. Larik, Sajjad Haider, "Clustering based Anomalous Transaction Reporting", Elsevier, ScienceDirect Procedia Computer Science 3 (2011) 606610, 2010
- Saleha Raza, Sajjad Haider, "Suspicious activity reporting using dynamic bayesian networks", ScienceDirect Procedia Computer Science 3(2011) 987-991, Elsevier, 2010
- Gao Zengan, "Application of Cluster-Based Local Outlier Factor Algorithm in Anti-Money Laundering", IEEE, 2009
- Suvasini Panigrahi, Shamik Sural,A. K. Majumdar, "Detection of Intrusive Activity in Databases by Combining Multiple Evidences and Belief Update", IEEE, 2009
- Xingqi Wang, Guang Dong, "Research on Money Laundering Detection based on Improved Minimum Spanning Tree Clustering and Its Application", IEEE, 2009
- Varun Chandola, Arindam Banarjee, Vipin Kumar, "Outlier Detection : A Survey", University of Minnesota
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