Data Mining for Fraud Detection System

Data Mining for Fraud Detection System

Copyright: © 2009 |Pages: 6
DOI: 10.4018/978-1-60566-010-3.ch065
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As a conseguence of expansion of modern technology, the number and scenario of fraud are increasing dramatically. Therefore, the reputation blemish and losses caused are primary motivations for technologies and methodologies for fraud detection that have been applied successfully in some economic activities. The detection involves monitoring the behavior of users based on huge data sets such as the logged data and user behavior. The aim of this contribution is to show some data mining techniques for fraud detection and prevention with applications in credit card and telecommunications, within a business of mining the data to achieve higher cost savings, and also in the interests of determining potential legal evidence. The problem is very difficult because fraudsters takes many different forms and are adaptive, so they will usually look for ways to avoid every security measures.
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Data Mining Approach

Data mining analyzes the huge volumes of transactions and billing data and seeks out patterns, trends and clusters that reveal fraud. The main steps for implementing this approach for fraud detection within a business organization are:

  • 1.

    Analyze the fraud objectives and the potential fraudsters, in order to converting them into data mining objectives;

  • 2.

    Data collection and understanding;

  • 3.

    Data cleaning and preparation for the algorithms;

  • 4.

    Experiment design;

  • 5.

    Evaluation results in order to review the process.

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