Fraudulent Behavior Forecast in Telecom Industry Based on Data Mining Technology *

By Wu, Sen; Kang, Naidong et al. | Communications of the IIMA, December 2007 | Go to article overview

Fraudulent Behavior Forecast in Telecom Industry Based on Data Mining Technology *


Wu, Sen, Kang, Naidong, Yang, Liu, Communications of the IIMA


ABSTRACT

Outlier analysis in data mining is to find the deviation and abnormal data in the databases. By finding and explaining the outliers, we can apply them to detecting the business frauds effectively. This paper analyzes the common characteristics of fraudulent behavior of customers in telecom industry systematically. Based on the outlier-finding by clustering in data mining, we propose an effective solution to forecast the customers who are maliciously in arrears. Coupled with the actual application of forecasting the customers who are maliciously in arrears in telecom industry, we propose the specific method to forecast this kind of customers by using Kohonen neural network clustering algorithm.

INTRODUCTION

The behavior of malicious arrearage in telecom industry in China is common and has lasted for a long time. The malicious arrearage increases the bad account ratio in telecom companies and makes the profit untrue and the state-owed assets lost.

Telecom industry is data-intensive. After many years of development, telecom companies have the detail records of both the customers and the real-time calls. Therefore, by analyzing the data on hand, companies can know the purchase habits and the nature attributes of customers. Based on the analysis of customers' behavior, credit and risk, operators can build a system to avoid frauds. Through this system, operators can manage customers on-line at any time. Once a customer has abnormal phone call, operator gives alarm to service staff to take necessary actions, in case the customer leaves in arrears maliciously.

Based on the clustering technique in data mining, this paper focuses on the problem of finding abnormal outliers in large data set by using Kohonen neural network clustering algorithm, and gives the specific method to forecast the behavior of malicious arrearage.

Clustering Technique in Data Mining

(1) Knowledge discovery by clustering

From business aspect, data mining is a new method to process business information. Its main characteristic is that it selects, transforms and analyzes transaction data in business databases, extracts crucial information to help making business decisions. A common problem all companies face is that valuable information is limited though the data is rich. Therefore it is just like to collect gold from mine for company to gain information from the large data set, so data mining gets its name(Han and Kamber, 2001). Company can enhance its competency by deep analysis of the information.

Outlier detection in data mining is to find the deviation in databases, this kind of exploration of deviation or abnormal pattern has important meaning in related fields such as forecasting the customers who are maliciously in arrears in telecom industry.

We could use clustering to accomplish the outlier detection. Knowledge discovery by clustering is one of the important functions of data mining. From the aspect of data mining, clustering research extracts valuable knowledge from large data sets intelligently and automatically. Knowledge discovery by clustering was proposed along with the development of databases and the emergence of data mining and Knowledge discovery technology. Knowledge discovery by clustering is applied in many areas, such as: forecast of bankruptcy, pattern recognition, marketing, market segmentation and so on. The abnormal data implies outliers in clustering. Through the finding and explaining of the outliers, we can extract the features of abnormal data, so apply them to detect business frauds effectively.

(2) Kohonen neural network clustering algorithm

We compared three kinds of clustering algorithms by experiment, Kohonen neural network algorithm, two-step clustering algorithm, and K-means algorithm. We figure out that Kohonen neural network algorithm is the most effective algorithm to find outliers.

Kohonen neural network was proposed in 1981. …

The rest of this article is only available to active members of Questia

Already a member? Log in now.

Notes for this article

Add a new note
If you are trying to select text to create highlights or citations, remember that you must now click or tap on the first word, and then click or tap on the last word.
One moment ...
Default project is now your active project.
Project items
Notes
Cite this article

Cited article

Style
Citations are available only to our active members.
Buy instant access to cite pages or passages in MLA 8, MLA 7, APA and Chicago citation styles.

(Einhorn, 1992, p. 25)

(Einhorn 25)

(Einhorn 25)

1. Lois J. Einhorn, Abraham Lincoln, the Orator: Penetrating the Lincoln Legend (Westport, CT: Greenwood Press, 1992), 25, http://www.questia.com/read/27419298.

Note: primary sources have slightly different requirements for citation. Please see these guidelines for more information.

Cited article

Fraudulent Behavior Forecast in Telecom Industry Based on Data Mining Technology *
Settings

Settings

Typeface
Text size Smaller Larger Reset View mode
Search within

Search within this article

Look up

Look up a word

  • Dictionary
  • Thesaurus
Please submit a word or phrase above.
Print this page

Print this page

Why can't I print more than one page at a time?

Help
Full screen
Items saved from this article
  • Highlights & Notes
  • Citations
Some of your highlights are legacy items.

Highlights saved before July 30, 2012 will not be displayed on their respective source pages.

You can easily re-create the highlights by opening the book page or article, selecting the text, and clicking “Highlight.”

matching results for page

    Questia reader help

    How to highlight and cite specific passages

    1. Click or tap the first word you want to select.
    2. Click or tap the last word you want to select, and you’ll see everything in between get selected.
    3. You’ll then get a menu of options like creating a highlight or a citation from that passage of text.

    OK, got it!

    Cited passage

    Style
    Citations are available only to our active members.
    Buy instant access to cite pages or passages in MLA 8, MLA 7, APA and Chicago citation styles.

    "Portraying himself as an honest, ordinary person helped Lincoln identify with his audiences." (Einhorn, 1992, p. 25).

    "Portraying himself as an honest, ordinary person helped Lincoln identify with his audiences." (Einhorn 25)

    "Portraying himself as an honest, ordinary person helped Lincoln identify with his audiences." (Einhorn 25)

    "Portraying himself as an honest, ordinary person helped Lincoln identify with his audiences."1

    1. Lois J. Einhorn, Abraham Lincoln, the Orator: Penetrating the Lincoln Legend (Westport, CT: Greenwood Press, 1992), 25, http://www.questia.com/read/27419298.

    Cited passage

    Thanks for trying Questia!

    Please continue trying out our research tools, but please note, full functionality is available only to our active members.

    Your work will be lost once you leave this Web page.

    Buy instant access to save your work.

    Already a member? Log in now.

    Search by... Author
    Show... All Results Primary Sources Peer-reviewed

    Oops!

    An unknown error has occurred. Please click the button below to reload the page. If the problem persists, please try again in a little while.