Data mining approach for subscription-fraud detection in telecommunication sector

Saravanan, P.; Subramaniyaswamy, V.; Sivaramakrishnan, N.; Prakash, Mr.A.Antony; Arunkumar, T.

doi:10.12988/ces.2014.4431

Cited by 10 publications

(2 citation statements)

References 4 publications

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“…Nevertheless, the performance of FDS is obstructed due to obstacles like concept drift, supports real time detection, skewed distribution, large amount of data, etc. [2]. Therefore, how to solve these issues deserves more attention.…”

Section: Introductionmentioning

confidence: 99%

Research on improvements of fraud detection system: basing on improved machine learning algorithms

Zhang

2023

ACE

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Nowadays, commercial fraud behaviors commonly occur in many industries. However, due to obstacles like concept drift, imbalanced dataset and uneven distribution of fraud entries, Fraud Detection System (FDS) fails to identify such behaviors. Among the problems mentioned above, most research focus on dealing with skewed dataset. This paper first presents common application scenarios of FDS which consist of credit card fraud, insurance fraud and supply chain fraud. Then, this study introduces five representative methods in dealing with problems mentioned above, which are K Nearest Neighbors-Synthetic Minority Oversampling Technique-Long Short-term Memory Networks (kNN-SMOTE-LSTM), Generative Adversarial Nets-AdaBoost-Decision tree (GAN-AdaBoost-DT), Wasserstein GAN-Kernel Density Estimation-Gradient Boosting DT (WGAN-KDE-GBDT), Time-LSTM (TLSTM) and Adaptive Synthetic Sampling-Sequential Forward Selection-Random Forest (ADASYN-SFS-RF). KNN-SMOTE-LSTM adopts KNN as an identifying classifier so as to only retain true samples. GAN-AdaBoost-DT generates new samples without referring to real transactions. WGAN-KDE-GBDT uses Wasserstein Distance as distance measurement instead, and thus improves training speed and guarantees successful generation. TLSTM tires to consider the weights of different time intervals and measures the similarity between the simulated behavior and the genuine behavior. ADASYN-SFS-RF employs SFS algorithm, basing on RF, to only reserve optimal subsets of features. Finally, result metrics prove that those improved algorithms do improve the overall performance of FDS, even if with limitations at some indicators.

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Section: Introductionmentioning

confidence: 99%

Research on improvements of fraud detection system: basing on improved machine learning algorithms

Zhang

2023

ACE

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“…There are cases where the connections are bought under domestic categories but the use is on a commercial scale. This causes substantial loss to the sector [2]. There is a need to adopt a data mining technique that will filter these fraudsters.…”

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confidence: 99%

Optimal Classifier for Fraud Detection in Telecommunication Industry

Amuji¹,

Chukwuemeka²,

Ogbuagu³

2019

OJOp

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Fraud is a major challenge facing telecommunication industry. A huge amount of revenues are lost to these fraudsters who have developed different techniques and strategies to defraud the service providers. For any service provider to remain in the industry, the expected loss from the activities of these fraudsters should be highly minimized if not eliminated completely. But due to the nature of huge data and millions of subscribers involved, it becomes very difficult to detect this group of people. For this purpose, there is a need for optimal classifier and predictive probability model that can capture both the present and past history of the subscribers and classify them accordingly. In this paper, we have developed some predictive models and an optimal classifier. We simulated a sample of eighty (80) subscribers: their number of calls and the duration of the calls and categorized it into four sub-samples with sample size of twenty (20) each. We obtained the prior and posterior probabilities of the groups. We group these posterior probability distributions into two sample multivariate data with two variates each. We develop linear classifier that discriminates between the genuine subscribers and fraudulent subscribers. The optimal classifier ( A B β + ) has a posterior probability of 0.7368, and we classify the subscribers based on this optimal point. This paper focused on domestic subscribers and the parameters of interest were the number of calls per hour and the duration of the calls.

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Mining frequent patterns to identify vertical handover parameters in cellular networks

Kashmar

Atieh

2017

J Ambient Intell Human Comput

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Data mining approach for subscription-fraud detection in telecommunication sector

Cited by 10 publications

References 4 publications

Research on improvements of fraud detection system: basing on improved machine learning algorithms

Research on improvements of fraud detection system: basing on improved machine learning algorithms

Optimal Classifier for Fraud Detection in Telecommunication Industry

Mining frequent patterns to identify vertical handover parameters in cellular networks

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