The Study of Fraud Detection in Financial and Credit Institutions with Real Data

The Study of Fraud Detection in Financial and Credit Institutions with Real Data

Soda Soltaniziba

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Sevda Soltaniziba

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Mohammad Ali Balafar

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α 1- Department of Computer Engineering, Germi Branch, Islamic Azad University, Germi, Iran 2- Department of Communications Engineering, Faculty of Elec

The Study of Fraud Detection in Financial and Credit Institutions with Real Data

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Abstract

This paper presents a review of data mining techniques for the fraud detection. Development of information systems such as data due to it has become a source of important organizations. Method and techniques are required for efficient access to data, sharing the data, extracting information from data and using this information. In recent years, data mining technology is an important method that it has changed to extract concepts from the data set. Scientific data mining and business intelligence technology is as a valuable and some what hidden to provide large volumes of data. This research studies using service analyzes software annual transactions related to 20000 account number of financial institutions in the country.The main data mining techniques used for financial fraud detection (FFD) are logistic models, neural networks and decision trees, all of which provide primarysolutions to the problems inherent in the detection and classification of fraudulent data. The proposed method is clustering clients based on client type. An appropriate rule for each cluster is determined by the behavior of group members in case of deviation from specified behavior will be known among suspected cases. The rules of the C5 have been applied in decision tree algorithm. Model is able to extract about a lot of the rules related to client behavior.

References

27 Cites in Article

Reference Format

M Agyemang,K Barker,R Alhajj (2006). A comprehensive survey of numeric and symbolic outlier mining techniques.
(2008). About the Association of Certified Fraud Examiners and the <i>Report to the Nations on Occupational Fraud and Abuse</i>.
R Bolton,D Hand (2001). Unsupervised profiling methods for fraud detection.
Richard Bolton,David Hand (2002). Statistical Fraud Detection: A Review.
P Brockett,R Derrig,L Golden,A Levine,M Alpert (2002). Fraud classification using principal component analysis of ridits.
Michael Cahill,Diane Lambert,José Pinheiro,Don Sun (2002). Detecting Fraud in the Real World.
P Chan,W Fan,A Prodromidis,S Stolfo (1999). Distributed data mining in credit card fraud detection.
M Cerullo,V Cerullo (1999). Using neural networks to predict financial reporting fraud.
(2006). Core Principles for Effective Banking Supervision.
C Domingos,R Gavalda,O Watanabe (2002). Adaptive sampling methods forscaling up knowledge discovery algorithms.
J Dorronsoro,F Ginel,C Sánchez,C Cruz (1997). Neural fraud detection in credit card operations.
K Fanning,K Cogger (1998). Neural network detection of management fraud using published financial data.
S Ghosh,D Reilly (1994). Credit card fraud detection with a neural-network, 27 th Annual Hawaii International.
Victoria Hodge,Jim Austin (2004). A Survey of Outlier Detection Methodologies.
J Kim,A Ong,R Overill (2003). Design of an artificial immune system as a novel anomaly detector for combating financial fraud in the retail sector.
E Kirkos,C Spathis,Y Manolopoulos (2007). Data mining techniques for the detection of fraudulent fi nancial statement s.
Sotiris Kotsiantis,Euaggelos Koumanakos,Dimitris Tzelepis,Vasilis Tampakas (2006). Predicting Fraudulent Financial Statements with Machine Learning Techniques.
Y Kou,C Lu,S Sir Wongwattana,Y Huang (2004). Survey of Fraud Detection Techniques.
J Luell (2005). Zurich Master of the Carnation.
S Maes,K Tuyls,B Vanschoenwinkel,B Manderick (1993). Credit card fraud detection using bayesian and neural networks.
John Major,Dan Riedinger (2002). EFD: A Hybrid Knowledge/Statistical‐Based System for the Detection of Fraud.
P Ortega,C Figueroa,G Ruz (2006). A medical claim fraud/abuse detection system based on data mining: a case study in Chile.
Foster Provost,Venkateswarlu Kolluri (1999). A Survey of Methods for Scaling Up Inductive Algorithms.
T Washio,H Motoda (2003). State of the art of graph-based data mining.
M Weatherford (2002). Mining for fraud.
G Weiss (2004). Mining with rarity: A Unifying Framework.
I Yeh,Che-Hui Lien (2008). The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients.

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Soda Soltaniziba. 2015. \u201cThe Study of Fraud Detection in Financial and Credit Institutions with Real Data\u201d. Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 15 (GJCST Volume 15 Issue C6): .

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GJCST Volume 15 Issue C6
Pg. 37- 45

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

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Classification

GJCST-C Classification: D.4.6 H.2.7

Submission ReceivedDecember 12, 2014
Peer Review Double Blind
Handling Editor
Accepted December 31, 2014
Published January 15, 2015

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August 27, 2015

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Article Matrices

Total Score: 82

Country: Iran

Subject: Global Journal of Computer Science and Technology - C: Software & Data Engineering

Authors: Sevda Soltaniziba, Mohammad Ali Balafar (PhD/Dr. count: 0)

View Count (all-time): 306

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Publish Date: 2015 08, Thu

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Total Views: 8237

Total Downloads: 2114

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