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

Soda Soltaniziba
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Mohammad Ali Balafr
Mohammad Ali Balafr
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§ 1- Department of Computer Engineering, Germi Branch,...

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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.

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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, Mohammad Ali Balafr. 2015. "The Study of Fraud Detection in Financial and Credit Institutions with Real Data". Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 15 (GJCST Volume 15 Issue C6).

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords
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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
Version of record

v1.2

Issue date
August 27, 2015

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

Soda Soltaniziba
Soda Soltaniziba 1- Department of Computer Engineering, Germi Branch, Islamic Azad University, Germi, Iran 2- Department of Communications Engineering, Faculty of Elec
Mohammad Balafr
Mohammad Balafr
  • Submission ReceivedDecember 12, 2014
  • Peer Review Double Blind
    Handling Editor
  • Accepted December 31, 2014
  • Published January 15, 2015