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Study of Fraud Detection using Big Data Approach


Affiliations
1 Calcutta Institute of Engineering and Management, Kolkata - 700040, West Bengal, India
2 Kolkata Municipal Corporation, Kolkata - 700013, West Bengal, India
 

Fraud is increasing proportionally with the expansion of cutting edge technology and the e-globalization that cause loss of billions of dollar worldwide each year. In spite of having modern technology and worldwide superhighway communication we are failed to achieve our goal of secure e-globalization. To achieve our goal we need an efficient and effective fraud detection system. Fraud detection is a method of isolating illegal acts that are increasing worldwide. The aim of fraud detection system is to reveal the nature of fraudsters by applying appropriate methodology and specific domain knowledge. The amount of data produced in fraud detection growing large day by day. This cause difficulty to analyze huge amount of data that require more knowledge to gain. Today, in real world to create an efficient fraud detection system it is not enough to apply only data mining technique because data has become an indispensable part of every economy, industry, organization, business function and individual. The Big Data conceive unique computational and statistical challenges, including scalability and storage bottleneck, noise accumulation, spurious correlation and measurement errors. This paper includes different types of fraud that we may face in our everyday life and how the big data can improve the acceptability of fraud detection system now a days.

Keywords

Big Data, Data Mining, Fraud Detection.
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  • Van Vlasselaer V, Eliassi-Rad T, Akoglu L, Snoeck M, Baesens B. GOTCHA! Network-based Fraud Detection for Social Security Fraud. Submitted to Management Science manuscript MS-14-00232, 2015.
  • Punde A, Daundkar K, Shelar S. A Review: Data Mining For Big Data. International Journal of Advanced Research in Computer Engineering and Technology (IJARCET). 2014 Oct; 3(10).
  • David JM, Balakrishnan K. Prediction of Learning Disabilities in School-Age Children Using SVM and Decision Tree. Int J of Computer Science and Information Technology. 2011; 2(2):829–35. ISSN0975-9646.
  • Halevi G, Moed H. The Evolution of Big Data as a Research and Scientific Topic: Overview of the Literature. Research Trends. Special Issue on Big Data. 2012; 30:3–6.
  • Ularu EG, Puican FC, Apostu A, Velicanu M. Perspectives on Big Data and Big Data Analytics. Database Systems Journal. 2012; 3(4):3–14.
  • Punde A, Daundkar K, Shelar S. A Review: Data Mining for Big Data. International Journal of Advanced Research in Computer Engineering and Technology (IJARCET). 2014 Oct; 3(10).
  • Jaseena KU, David JM. Issues, Challenges, and Solutions: Big Data Mining. Netcom, CSIT, GRAPH-HOC, SPTM – 2014. 2014; 131–40. © CS & IT-CSCP 2014.
  • Bolton R, Hand D. Statistical Fraud Detection: A Review. Statistical Science. 2002; 17(3): 235–55.
  • Bologa A-R, Bologa R, Florea A. Big Data and Specific Analysis Methods for Insurance Fraud Detection. Database System Journal. 2013; 4(4):30–9.
  • Phua C, Lee V, Smith K, Gayler R. A comprehensive survey of data mining-based fraud detection research. Xiv preprintar Xiv: 1009.6119, 2010.
  • Halevi G, Moed H. The evolution of big data as a research and scientific topic: overview of the literature. Research Trends. Special Issue on Big Data. 2012; 30:3–6.

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  • Study of Fraud Detection using Big Data Approach

Abstract Views: 403  |  PDF Views: 110

Authors

Debdutta Pal
Calcutta Institute of Engineering and Management, Kolkata - 700040, West Bengal, India
Supriyo Pal
Kolkata Municipal Corporation, Kolkata - 700013, West Bengal, India

Abstract


Fraud is increasing proportionally with the expansion of cutting edge technology and the e-globalization that cause loss of billions of dollar worldwide each year. In spite of having modern technology and worldwide superhighway communication we are failed to achieve our goal of secure e-globalization. To achieve our goal we need an efficient and effective fraud detection system. Fraud detection is a method of isolating illegal acts that are increasing worldwide. The aim of fraud detection system is to reveal the nature of fraudsters by applying appropriate methodology and specific domain knowledge. The amount of data produced in fraud detection growing large day by day. This cause difficulty to analyze huge amount of data that require more knowledge to gain. Today, in real world to create an efficient fraud detection system it is not enough to apply only data mining technique because data has become an indispensable part of every economy, industry, organization, business function and individual. The Big Data conceive unique computational and statistical challenges, including scalability and storage bottleneck, noise accumulation, spurious correlation and measurement errors. This paper includes different types of fraud that we may face in our everyday life and how the big data can improve the acceptability of fraud detection system now a days.

Keywords


Big Data, Data Mining, Fraud Detection.

References