Nature-Inspired Techniques in the Context of Fraud Detection

Behdad, Mohammad; Barone, Luigi; Bennamoun, Mohammed; French, Tim

doi:10.1109/tsmcc.2012.2215851

Cited by 69 publications

(34 citation statements)

References 137 publications

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“…(Kahn and Roberds, 2008;Amori, 2008;Albrecht et al, 2011) Online fraud detection is difficult without automation of the transaction systems. (Behdad et al, 2012;Cavusoglu and Raghunathan, 2004) For online organisations, it is impractical to control frauds without efficient fraud detection system (Kundu et al, 2009) For effective online fraud management, an efficient fraud detection mechanism is necessary. (Chang and Chang, 2011).…”

Section: Findings Referencesmentioning

confidence: 99%

Investigating identity fraud management practices in e-tail sector: a systematic review

Soomro

Ahmed

Shah

2019

JEIM

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Purpose: Identity fraud is a growing issue for online retail organisations. The literature on this issue is scattered, and none of the studies presents a holistic view of identity fraud management practices in online retail context. Therefore, this study aims at investigating the identity fraud management practices and presents a comprehensive set of practices for e-tail sector.Methodology: A systematic literature review approach was adopted, and the articles were selected through pre-set inclusion criteria. We synthesised existing literature to investigate identity fraud management in e-tail sector. Findings:The research finds that literature on practices for identity fraud management is scattered. Findings also reveal that firms assume identity fraud issues as a technological challenge, which is one of the major reasons for a gap in effective management of identity frauds. This research suggests e-tailers to deal this issue as a management challenge and counter strategies should be developed in technological, human and organisational aspects. Research limitations:This study is limited to the published sources of data. Studies, based on empirical data, will be helpful to support the argument of this study, additionally future studies are recommended to include a wide number of databases.Originality: This research makes unique contributions by synthesising existing literature at each stage of fraud management and encompasses social, organisational and technological aspects. It will also help academicians understanding a holistic view of available research and opens new lines for future research.Practical implications: This research will help e-tail organisations to understand the whole of identity fraud management and help them develop and implement a comprehensive set of practices at each stage, for effective management identity frauds.

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Section: Findings Referencesmentioning

confidence: 99%

Investigating identity fraud management practices in e-tail sector: a systematic review

Soomro

Ahmed

Shah

2019

JEIM

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“…Credit card fraud, phishing and spam email are three prominent e-fraud types that has caused great damages to the global economy in recent times. Spam email refers to unsolicited bulk email [19], mostly sent by individuals trying to advertise products. Phishing refers to unsolicited emails, sent by individuals trying to obtain delicate information from users, usually for the purpose of fraud.…”

Section: E-fraud Detectionmentioning

confidence: 99%

On the Performance of Cuckoo Search and Bat Algorithms Based Instance Selection Techniques for SVM Speed Optimization with Application to e-Fraud Detection

Akinyelu¹,

Adewumi²

2018

KSII TIIS

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Support Vector Machine (SVM) is a well-known machine learning classification algorithm, which has been widely applied to many data mining problems, with good accuracy. However, SVM classification speed decreases with increase in dataset size. Some applications, like video surveillance and intrusion detection, requires a classifier to be trained very quickly, and on large datasets. Hence, this paper introduces two filter-based instance selection techniques for optimizing SVM training speed. Fast classification is often achieved at the expense of classification accuracy, and some applications, such as phishing and spam email classifiers, are very sensitive to slight drop in classification accuracy. Hence, this paper also introduces two wrapper-based instance selection techniques for improving SVM predictive accuracy and training speed. The wrapper and filter based techniques are inspired by Cuckoo Search Algorithm and Bat Algorithm. The proposed techniques are validated on three popular e-fraud types: credit card fraud, spam email and phishing email. In addition, the proposed techniques are validated on 20 other datasets provided by UCI data repository. Moreover, statistical analysis is performed and experimental results reveals that the filterbased and wrapper-based techniques significantly improved SVM classification speed. Also, results reveal that the wrapper-based techniques improved SVM predictive accuracy in most cases.

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“…Currently, because of the empirical limitations and complexity of classifier such as neural network, the research is developing mainly in the integration of classifier and other optimization algorithms and their collaborative application in detection and classification. For related studies, refer to [20][21][22][23][24][25][26][27][28][29][30].…”

Section: Detection and Classificationmentioning

confidence: 99%

Recent Advances of Intelligent Optimization Algorithm in Manufacturing

Tao

Laili

Zhang

2014

Springer Series in Advanced Manufacturing

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Due to its good versatility and independence, intelligent optimization algorithm has largely shortened the time of decision-making in large-scale optimization problems of manufacture. However, lower searching time often conflicts with the searching accuracy in most cases. To improve the problem solving capability, research in intelligent optimization algorithm based on different domain characteristics never stopped. From the view of manufacturing, this chapter classified and comprehensively analyzed all kinds of manufacturing optimization problems and their general methods, illustrated the application features and challenges of intelligent optimization algorithm in manufacturing, and summarized the development needs and trends of intelligent optimization algorithm in the field of manufacturing system.

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Nature-Inspired Techniques in the Context of Fraud Detection

Cited by 69 publications

References 137 publications

Investigating identity fraud management practices in e-tail sector: a systematic review

Investigating identity fraud management practices in e-tail sector: a systematic review

On the Performance of Cuckoo Search and Bat Algorithms Based Instance Selection Techniques for SVM Speed Optimization with Application to e-Fraud Detection

Recent Advances of Intelligent Optimization Algorithm in Manufacturing

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