Leveraging Missing Values in Call Detail Record Using Naïve Bayes for Fraud Analysis

Khairul Nizam Bin Baharim, M. S. Kamaruddin, Faeizah Jusof
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引用次数: 4

Abstract

In Telecom Fraud Management System (TFMS), the corrupted and missing values in call detail record (CDR) information are filtrated by rule-based classifier into the rejection proportion, as most of fraud detection approaches such as customer profiling and learning-based fraud detection technique were demanded clean information. Additionally, very few research works had discussed the issue of fraud detection in the area of corrupted CDR, due to its possibility yielded by lack of telecom equipment. In this paper, we present the leveraging missing values method, using the Naive Bayes approach posterior to rule-based classifier to analyze the probability of corrupted and missing values in CDR which then consequently led to the discovery of useable record lies in rejected CDR. This approach differs from other missing value handling and fraud analysis research where it does not attempt to treat the missing values; instead, leveraging it into the probabilistic model for fraud analysis. In this paper, we also reveal an impact of the missing values in telecommunication services and significantly vacant an avenues future work in fraud detection research.
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利用Naïve贝叶斯欺诈分析呼叫详细记录中的缺失值
在电信欺诈管理系统(TFMS)中,由于客户分析和基于学习的欺诈检测技术等大多数欺诈检测方法都要求信息干净,因此基于规则的分类器将话单信息中的损坏值和缺失值过滤到拒绝比例中。此外,很少有研究工作讨论损坏的CDR领域的欺诈检测问题,因为缺乏电信设备会产生这种可能性。在本文中,我们提出了利用缺失值方法,使用基于规则的分类器后验的朴素贝叶斯方法来分析CDR中损坏和缺失值的概率,从而导致在被拒绝的CDR中发现可用的记录。这种方法不同于其他缺失值处理和欺诈分析研究,它不试图处理缺失值;相反,将其运用到欺诈分析的概率模型中。在本文中,我们还揭示了电信服务中缺失值的影响,以及欺诈检测研究中未来工作的重大空缺。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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