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Analysis of Pattern Identification Using Graph Database for Fraud Detection


Affiliations
1 Department of Information Technology, College of Technology, Govind Ballabh Pant University of Agriculture and Technology, Pantnagar-263145, Uttarakhand, India
 

Internet is the main tool for e-business. E-transaction is made faster by Internet. With the increase of e-transaction internet fraud or e-business fraud is increasing. Credit fraud in the banking sector is a growing concern. Few sort of card (debit/credit) fraud is decreasing by providing detection and prevention system from banks and government. But card-not-present fraud losses are increasing at higher rate because of online transaction as there is no chance to use Chip and PIN as well as card is not used face-to-face. Card-not-present fraud losses are growing in an un-protective and un-detective way. This paper seeks to investigate the current debate regarding the fraud in the banking sector and vulnerabilities in online banking and to study some possible remedial actions to detect and prevent credit fraud. The research also reveals lots of channels of fraud in online banking which are increasing day by day. These kinds of fraud are the main barriers for the e-business in the banking sector. This paper devised a new approach for fraud detection in these sector with help of graph database&by matching pattern of previous frauds.

Keywords

Frauds, Bank Frauds, Online/Offline Frauds, Fraud Detection, Fraud Pattern.
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  • Kashyap N.K., “Evaluation of Proposed Algorithm with Preceding GMT for Fraudulence Diagnosis”. Orient.J. Comp. Sci. and Technol; 9(2). Available from:http://www.computerscijournal.org/?p=3661

  • Navneet Kumar Kashyap, Binay Kumar Pandey, H. L. Mandoria & Ashok Kumar,”A Comprehensive Study Of Various Kinds Of Frauds & It’s Impact”, International Journal of Computer Science Engineering and Information Technology Research (IJCSEITR) ISSN(P): 2249-6831; ISSN(E): 2249-7943 Vol. 6, Issue 3, Jun 2016, 47-58,

  • Navneet Kumar Kashyap, Binay Kumar Pandey, H. L. Mandoria & Ashok Kumar, “A Review Of Leading Database: Relational & Non-Relational Database”, I-Manager’s Journal On Information Technology (JIT) ISSN (P): 2277-5110; ISSN (E): 2277-5250, (Accepted On May 31, 2016)

  • Navneet Kumar Kashyap, Binay Kumar Pandey, H. L. Mandoria & Ashok Kumar, “Comprehensive Study of Different Pattern Recognition Techniques”, i-manager’s Journal on Pattern Recognition (JPR)ISSN(P): 2349-7912; ISSN(E): 2350-112X, vol. 2, No. 4, 42-49 ( Accepted on JUNE 9, 2016)

  • Navneet Kumar Kashyap, Binay Kumar Pandey, H. L. Mandoria & Ashok Kumar, “GRAPH MINING USING gSpan: GRAPH BASED SUBSTRUTURE PATTERN MINING”, International Journal of Applied Research on Information Technology and Computing (IJARITAC), ISSN(P):0975-8070; ISSN(E): 0975-8089, Vol. 7, No. 2, August 2016 ,( Accepted on JUNE 13, 2016).


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  • Analysis of Pattern Identification Using Graph Database for Fraud Detection

Abstract Views: 153  |  PDF Views: 4

Authors

Navneet Kr. Kashyap
Department of Information Technology, College of Technology, Govind Ballabh Pant University of Agriculture and Technology, Pantnagar-263145, Uttarakhand, India
B. K. Pandey
Department of Information Technology, College of Technology, Govind Ballabh Pant University of Agriculture and Technology, Pantnagar-263145, Uttarakhand, India
H. L. Mandoria
Department of Information Technology, College of Technology, Govind Ballabh Pant University of Agriculture and Technology, Pantnagar-263145, Uttarakhand, India

Abstract


Internet is the main tool for e-business. E-transaction is made faster by Internet. With the increase of e-transaction internet fraud or e-business fraud is increasing. Credit fraud in the banking sector is a growing concern. Few sort of card (debit/credit) fraud is decreasing by providing detection and prevention system from banks and government. But card-not-present fraud losses are increasing at higher rate because of online transaction as there is no chance to use Chip and PIN as well as card is not used face-to-face. Card-not-present fraud losses are growing in an un-protective and un-detective way. This paper seeks to investigate the current debate regarding the fraud in the banking sector and vulnerabilities in online banking and to study some possible remedial actions to detect and prevent credit fraud. The research also reveals lots of channels of fraud in online banking which are increasing day by day. These kinds of fraud are the main barriers for the e-business in the banking sector. This paper devised a new approach for fraud detection in these sector with help of graph database&by matching pattern of previous frauds.

Keywords


Frauds, Bank Frauds, Online/Offline Frauds, Fraud Detection, Fraud Pattern.

References