Using social network knowledge for detecting spider constructions in social security fraud

Vlasselaer, Véronique Van; Meskens, Jan; Dromme, Dries Van; Baesens, Bart

doi:10.1145/2492517.2500292

Cited by 15 publications

(7 citation statements)

References 13 publications

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“…As noted early, several studies have applied SMOTE in classification problems using social network data. For example, it was used to create a model that predicts social security fraud detection in Belgium [ 86 ]. In another case the technique was utilized to reduce some of the effects of class imbalance among Trust and Distrust classes in social network online services [ 84 ].…”

Section: Discussionmentioning

confidence: 99%

“…Depending upon the amount of over–sampling required, neighbours from the k nearest neighbours are randomly chosen” ([ 59 ] [p. 328]). SMOTE has been successfully used to balance classes in classification problems involving social network data [ 84 – 86 ]. Here, for the WC case SMOTE obtained 42 synthetic observations for class 1 and 70 for class 0 while in the CN case, the corresponding numbers were 84 and 140.…”

Section: Methodsmentioning

confidence: 99%

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Modeling Verdict Outcomes Using Social Network Measures: The Watergate and Caviar Network Cases

et al. 2016

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Modelling criminal trial verdict outcomes using social network measures is an emerging research area in quantitative criminology. Few studies have yet analyzed which of these measures are the most important for verdict modelling or which data classification techniques perform best for this application. To compare the performance of different techniques in classifying members of a criminal network, this article applies three different machine learning classifiers–Logistic Regression, Naïve Bayes and Random Forest–with a range of social network measures and the necessary databases to model the verdicts in two real–world cases: the U.S. Watergate Conspiracy of the 1970’s and the now–defunct Canada–based international drug trafficking ring known as the Caviar Network. In both cases it was found that the Random Forest classifier did better than either Logistic Regression or Naïve Bayes, and its superior performance was statistically significant. This being so, Random Forest was used not only for classification but also to assess the importance of the measures. For the Watergate case, the most important one proved to be betweenness centrality while for the Caviar Network, it was the effective size of the network. These results are significant because they show that an approach combining machine learning with social network analysis not only can generate accurate classification models but also helps quantify the importance social network variables in modelling verdict outcomes. We conclude our analysis with a discussion and some suggestions for future work in verdict modelling using social network measures.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Modeling Verdict Outcomes Using Social Network Measures: The Watergate and Caviar Network Cases

et al. 2016

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“…Hence, the list of classification and approaches for hybrid level are discussed/ presented in Tables 12 and 13. Abdi and Hashemi (2015) Boosting, MDOBoost C4.5 MAUC, G-Mean, Recall UCI, KEEL repository Liu et al (2009) Undersampling AdaBoost AUC, F-measure, G-Mean UCI repository Huang and Zhang (2016) AdaBoost, SMOTE AdaBoost Precision, Recall, Specificity, SKEMPI Accuracy, F-measure Gazzah et al (2015) Hybrid Approach SVM TN, TP Faces94, SID signature databases Bai, 2013;Van Vlasselaer et al, 2013; 3. Very popular due to its simplicity and efficiency.…”

Section: Ensemble (Or) Hybrid Methodsmentioning

confidence: 99%

A Wide Scale Classification of Class Imbalance Problem and its Solutions: A Systematic Literature Review

Rekha¹,

Tyagi²,

Reddy³

2019

Journal of Computer Science

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In today's world, most of the data (real world) is present in imbalanced form by nature. This is because of not having efficient algorithms to put this data (i.e., generated data by billion of internetconnected devices (IoTs)) in respective format. Imbalanced data poses a great challenge to (both) data mining and machine learning algorithms. The imbalanced dataset consists of a majority class and a minority class, where the majority class takes the lead over the minority class. Generally, several standard learning algorithms assume the balanced class distribution or equal misclassification costs. If prediction is performed by these learning algorithms on imbalanced data, the accuracy will be high for majority classes, i.e., resulting in poor performance. To overcome this problem (or improving accuracy of deision/prediction-making process), data mining and machine learning researchers have addressed the problem of imbalanced data using datalevel, algorithmic level and ensemble or hybrid methods. This article presents a systematic literature review and analyze the results of more than 400 research papers published between 2002-2017 (till June 2017), resulting in a broader and elaborate investigation of the literature in this area of research. Note that extension of this article/work will contain till December 2018 research articles, which will be published in June 2019 (now these more papers/articles did not include due to no. of pages/space issues). The systematic analysis of the research literature has focus on the key role of Data Intrinsic Problems in classification, handling the imbalanced data and the techniques used to overcome the skewed distribution. Furthermore, this article reveals patterns, trends and gaps in the existing literature and discusses briefly the next generation research directions in this area.

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“…Telecom fraud detection systems are usually based on anomaly detection, where behaviour is compared with past behaviour of subscribers (Yesuf et al, 2017). Van Vlasselaer et al (2013) state that because of the many domainspecific characteristics of different fraud types, it becomes important to use a domain-specific solution. Our method provides the necessary flexibility due to the incorporation of expert knowledge.…”

Section: Business Problemmentioning

confidence: 99%

From one-class to two-class classification by incorporating expert knowledge: Novelty detection in human behaviour

Oosterlinck

Benoit

Baecke

2020

European Journal of Operational Research

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One-class classification is the standard procedure for novelty detection. Novelty detection aims to identify observations that deviate from a determined normal behaviour. Only instances of one class are known, whereas so called novelties are unlabelled. Traditional novelty detection applies methods from the field of outlier detection. These standard one-class classification approaches have limited performance in many real business cases. The traditional techniques are mainly developed for industrial problems such as machine condition monitoring. When applying these to human behaviour, the performance drops significantly. This paper proposes a method that improves existing approaches by creating semi-synthetic novelties in order to have labelled data for the two classes. Expert knowledge is incorporated in the initial phase of this data generation process. The method was deployed on a real-life test case where the goal was to detect fraudulent subscriptions to a telecom family plan. This research demonstrates that the two-class expert model outperforms a one-class model on the semi-synthetic dataset. In a next step the model was validated on a real dataset. A fraud detection team of the company manually checked the top predicted novelties. The results show that incorporating expert knowledge to transform a one-class problem into a two-class problem is a valuable method.

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Using social network knowledge for detecting spider constructions in social security fraud

Cited by 15 publications

References 13 publications

Modeling Verdict Outcomes Using Social Network Measures: The Watergate and Caviar Network Cases

Modeling Verdict Outcomes Using Social Network Measures: The Watergate and Caviar Network Cases

A Wide Scale Classification of Class Imbalance Problem and its Solutions: A Systematic Literature Review

From one-class to two-class classification by incorporating expert knowledge: Novelty detection in human behaviour

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