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Journal articles on the topic 'Identity fraud'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 January 2023

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1

McElligott, G. "Identity fraud." British Dental Journal 204, no. 2 (2008): 51–52. http://dx.doi.org/10.1038/bdj.2008.16.

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2

McNulty, Tracey. "Fighting identity fraud." Working with Older People 8, no. 3 (2004): 25–26. http://dx.doi.org/10.1108/13663666200400039.

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3

Sproule, Susan, and Norm Archer. "Measuring identity theft and identity fraud." International Journal of Business Governance and Ethics 5, no. 1/2 (2010): 51. http://dx.doi.org/10.1504/ijbge.2010.029555.

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4

Hinde, Steven. "Identity theft & fraud." Computer Fraud & Security 2005, no. 6 (2005): 18–20. http://dx.doi.org/10.1016/s1361-3723(05)70223-2.

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5

Lee, Victor. "Biometrics and identity fraud." Biometric Technology Today 16, no. 2 (2008): 7–11. http://dx.doi.org/10.1016/s0969-4765(08)70059-6.

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6

Soomro, Zahoor Ahmed, Javed Ahmed, Mahmood Hussain Shah, and Khalil Khoumbati. "Investigating identity fraud management practices in e-tail sector: a systematic review." Journal of Enterprise Information Management 32, no. 2 (2019): 301–24. http://dx.doi.org/10.1108/jeim-06-2018-0110.

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PurposeIdentity 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 the online retail context. Therefore, the purpose of this paper is to investigate the identity fraud management practices and present a comprehensive set of practices for e-tail sector.Design/methodology/approachA systematic literature review approach was adopted, and the articles were selected through pre-set inclusion criteria. The authors synthesised existing literature to investigate i

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7

Koops, Bert-Jaap, and Ronald Leenes. "Identity theft, identity fraud and/or identity-related crime." Datenschutz und Datensicherheit - DuD 30, no. 9 (2006): 553–56. http://dx.doi.org/10.1007/s11623-006-0141-2.

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8

Grijpink, Jan. "Biometrics and identity fraud protection." Computer Law & Security Review 21, no. 3 (2005): 249–56. http://dx.doi.org/10.1016/j.clsr.2005.04.009.

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9

Furnell, Steven. "Identity impairment: The problems facing victims of identity fraud." Computer Fraud & Security 2007, no. 12 (2007): 6–11. http://dx.doi.org/10.1016/s1361-3723(07)70168-9.

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10

Archer, Norm. "Consumer identity theft prevention and identity fraud detection behaviours." Journal of Financial Crime 19, no. 1 (2011): 20–36. http://dx.doi.org/10.1108/13590791211190704.

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11

Kiberstis, Paula A. "Evading cancer drugs by identity fraud." Science 355, no. 6320 (2017): 35.16–37. http://dx.doi.org/10.1126/science.355.6320.35-p.

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12

de Vries, Bald. "Describing Identity Fraud: Towards a Common Definition." SCRIPT-ed 5, no. 3 (2008): 482–500. http://dx.doi.org/10.2966/scrip.050308.482.

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13

Amasiatu, Chioma Vivian, and Mahmood Hussain Shah. "First party fraud management: framework for the retail industry." International Journal of Retail & Distribution Management 46, no. 4 (2018): 350–63. http://dx.doi.org/10.1108/ijrdm-10-2016-0185.

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Purpose First party fraud in which consumers commit fraud against retailers is a growing problem. Research in this area is very limited which means that there is almost no guidance available to mitigate this problem. Existing fraud management frameworks focus on the management of other fraud, such as identity theft or employee instigated fraud. Due to the different nature of these frauds, these frameworks do not adequately address first party fraud. Therefore, the purpose of this paper is to propose an adapted version of the fraud management lifecycle framework which is specific to first party

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14

Bhardwaj, Surbhi, and Sonika Gupta. "Effects of Feature Selection with Machine Learning Algorithms in Detection of Credit Card Fraud." International Journal of Engineering Research in Computer Science and Engineering 9, no. 7 (2022): 46–51. http://dx.doi.org/10.36647/ijercse/09.07.art011.

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As an effect of developments in e-commerce systems and communication technologies, Credit cards have become the most common mode of payment for purchases. The payments through the credit cards also involve the risk of credit card fraud such as application fraud, identity theft, lost/stolen card misuse, and phishing. These frauds lead to huge losses and require automatic and real-time fraud detection. Many studies have used Machine Learning (ML) techniques to detect fraudulent transactions. This study focuses on proposing a framework for the detection of credit card frauds by applying machine l

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15

Ruth, Saskia M. van, and Daniel Granato. "Food Identity, Authenticity and Fraud: The Full Spectrum." Foods 6, no. 7 (2017): 49. http://dx.doi.org/10.3390/foods6070049.

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16

Evison, Martin. "DNA database could end problem of identity fraud." Nature 420, no. 6914 (2002): 359. http://dx.doi.org/10.1038/420359c.

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17

Porter, David. "Identity fraud: the stealth threat to UK plc." Computer Fraud & Security 2004, no. 7 (2004): 4–6. http://dx.doi.org/10.1016/s1361-3723(04)00086-7.

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18

Hope, Joan. "Implement gesture biometrics to stop online identity fraud." Recruiting & Retaining Adult Learners 17, no. 3 (2014): 8. http://dx.doi.org/10.1002/nsr.30006.

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19

Vivian Amasiatu, Chioma, and Mahmood Hussain Shah. "First party fraud: a review of the forms and motives of fraudulent consumer behaviours in e-tailing." International Journal of Retail & Distribution Management 42, no. 9 (2014): 805–17. http://dx.doi.org/10.1108/ijrdm-05-2013-0112.

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Purpose – First party fraud is fraud committed by an account holder or customer that does not involve the use of a stolen identity. This type of fraud has grown substantially in recent times due to increased online shopping and is becoming a major concern for online retail businesses, hereby referred to as e-tailers. The purpose of this paper is to provide insights into the motives and nature of first party fraud in e-tailing. Design/methodology/approach – Systematic literature review was used to synthesise existing research on first party fraud. The authors used scholarly literature as well a

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20

Ekolama, Solomon M., Sunny Orike, and Promise Elechi. "Preventing Cyber-Fraud in Nigeria’s Banking System Using Fraudaeck-AI." European Journal of Electrical Engineering and Computer Science 6, no. 6 (2022): 55–63. http://dx.doi.org/10.24018/ejece.2022.6.6.480.

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Preventing Cyber-Fraud in Nigeria's Banking System Using Fraudaeck-AI (Fraud analysis environment for cyber-fraud check) is an in-depth analysis of artificial intelligence (AI) assisted identity verification and authentication systems to prevent cyber fraud in Nigeria's banking system. The Fraudaeck (fraud analysis environment for cyber-fraud check) is a machine learning model developed to learn the interconnected subsystems of the communication network and the banking applications and how they function seamlessly to provide customers with ease and comfort of banking even on the go. An investi

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21

Smith, Alan D., and Allen R. Lias. "Identity Theft and E-Fraud as Critical CRM Concerns." International Journal of Enterprise Information Systems 1, no. 2 (2005): 17–36. http://dx.doi.org/10.4018/jeis.2005040102.

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22

Kendall-Raynor, Petra. "Identity fraud case prompts call for tougher recruitment checks." Nursing Standard 22, no. 36 (2008): 7. http://dx.doi.org/10.7748/ns.22.36.7.s8.

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23

Bi, Naheed. "Consumer identity theft: Introduction and fraud sources and detection." South Asian Journal of Marketing & Management Research 11, no. 10 (2021): 156–61. http://dx.doi.org/10.5958/2249-877x.2021.00088.6.

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24

Khan, Gulista. "Investigating identity fraud management practices in E-tail sector." South Asian Journal of Marketing & Management Research 11, no. 10 (2021): 138–43. http://dx.doi.org/10.5958/2249-877x.2021.00086.2.

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25

Pap, Andras L. "Ethno-racial identity (politics) by law: “Fraud” and “choice”." Nationalities Papers 45, no. 5 (2017): 968–87. http://dx.doi.org/10.1080/00905992.2017.1311846.

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Following an introduction to the changes in how ethno-racial identity is conceptualized in the social sciences and humanities by the destabilization of categorical frameworks, the author looks at how law reacts to these discussions and paradigm shifts, and argues that legal and administrative approaches face severe linguistic and conceptual limitations by operating within a “choice” and “fraud” binary. The article then questions if the free choice of identity exists as a principle of international minority protection law, a legal field that arguably represents a global political and ethical co

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26

Grijpink, Jan. "Identity fraud as a challenge to the constitutional state." Computer Law & Security Review 20, no. 1 (2004): 29–36. http://dx.doi.org/10.1016/s0267-3649(04)00006-8.

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27

Danquah, Paul, John Amoako Kani, and Dzifa Bibi. "Internet fraud: The influence of Identity Flexibility and Dissociative Anonymity." East African Journal of Information Technology 5, no. 1 (2022): 39–52. http://dx.doi.org/10.37284/eajit.5.1.673.

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The purpose of this paper is to investigate the influence of identity flexibility and dissociative anonymity on internet fraud, focusing on the cyber deception and theft. Four victims, three culprits, and an experimental website were used to collect data for case study analysis. The researchers employed qualitative and quantitative data analysis for the study. Content analysis of interview transcriptions and results obtained from experimental website were used as the main data collection sources and tools. This study found that victims of internet fraud were defrauded by culprits who clearly p

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28

Mathew, Alexander, Gayatri Moindi, Ketan Bende, and Neha Singh. "Credit Card Fraud Prediction System." Journal of Advance Research in Computer Science & Engineering (ISSN: 2456-3552) 2, no. 3 (2015): 30–35. http://dx.doi.org/10.53555/nncse.v2i3.481.

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Identity crime is common, and pricey, and credit card fraud is a specific case of identity crime. The existing systems of known fraud matching and business rules have restrictions. To remove these negative aspects in real world, this paper proposes a data mining approach: Communal Detection (CD) and Spike Detection (SD). CD finds real social relationships to reduce the suspicion score, and is impervious to fake social relationships. This approach on a fixed set of attributes is whitelist-oriented. SD increases the suspicion score by finding discrepancies in duplicates. These data mining approa

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29

Saunders, Kurt M., and Bruce Zucker. "Counteracting Identity Fraud in the Information Age: The Identity Theft and Assumption Deterrence Act." International Review of Law, Computers & Technology 13, no. 2 (1999): 183–92. http://dx.doi.org/10.1080/13600869955134.

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30

Lyu, Qiuyun, Hao Li, Renjie Zhou, Jilin Zhang, Nailiang Zhao, and Yan Liu. "BCFDPS: A Blockchain-Based Click Fraud Detection and Prevention Scheme for Online Advertising." Security and Communication Networks 2022 (April 29, 2022): 1–20. http://dx.doi.org/10.1155/2022/3043489.

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Online advertising, which depends on consumers’ click, creates revenue for media sites, publishers, and advertisers. However, click fraud by criminals, i.e., the ad is clicked either by malicious machines or hiring people, threatens this advertising system. To solve the problem, many schemes are proposed which are mainly based on machine learning or statistical analysis. Although these schemes mitigate the problem of click fraud, several problems still exist. For example, some fraudulent clicks are still in the wild since their schemes only discover the fraudulent clicks with a probability app

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31

Berghel, Hal. "Identity Theft and Financial Fraud: Some Strangeness in the Proportions." Computer 45, no. 1 (2012): 86–89. http://dx.doi.org/10.1109/mc.2012.16.

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32

Fu, Anthony Y., Xiaotie Deng, and Liu Wenyin. "REGAP: A Tool for Unicode-Based Web Identity Fraud Detection." Journal of Digital Forensic Practice 1, no. 2 (2006): 83–97. http://dx.doi.org/10.1080/15567280600995501.

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33

Kenneally, Erin. "Enabling the Enablers: An Identity Fraud Solution for Law Enforcement." Journal of Digital Forensic Practice 1, no. 3 (2006): 157–64. http://dx.doi.org/10.1080/15567280600995758.

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34

Miri‐Lavassani, Kayvan, Vinod Kumar, Bahar Movahedi, and Uma Kumar. "Developing an identity fraud measurement model: a factor analysis approach." Journal of Financial Crime 16, no. 4 (2009): 364–86. http://dx.doi.org/10.1108/13590790910993708.

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35

Verma, Pradeep, and Poornima Tyagi. "Analysis of Supervised Machine Learning Algorithms in the Context of Fraud Detection." ECS Transactions 107, no. 1 (2022): 7189–200. http://dx.doi.org/10.1149/10701.7189ecst.

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In today’s era, where ‘time’ is considered as ‘money,’ people are completely depending on e-commerce and online banking for their routine purchases, shopping, and financial transactions. This increasing dependency on e-commerce are increasing fraud in online transactions, and credit card fraud is one example. Such malicious and unethical practices may cause identity theft and monitory loss to the people across the world. In this research paper, our effort is to identify the best Supervised Machine Learning algorithm that helps in classifying fraudulent and non-fraudulent transactions under cre

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36

Bush, Sarah Sunn, and Lauren Prather. "Who's There? Election Observer Identity and the Local Credibility of Elections." International Organization 72, no. 3 (2018): 659–92. http://dx.doi.org/10.1017/s0020818318000140.

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AbstractPrior research has sought to understand the rise of election observers and their consequences for outcomes such as fraud, protest, and violence. These studies are important but they overlook a significant individual-level dynamic that observers themselves care about: the effect that election observers have on local attitudes about elections. We argue that the activities of election observers can enhance elections' local credibility, but only when locals perceive observers as being both capable of detecting fraud and unbiased in that pursuit. Not all observer groups are seen as equally

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37

Sharpe, Alex. "Queering Judgment." Journal of Criminal Law 81, no. 5 (2017): 417–35. http://dx.doi.org/10.1177/0022018317728828.

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This article is a response to a series of recent successful sexual offence prosecutions brought against transgender and other gender non-conforming people for gender identity fraud, and specifically to Leveson LJ’s judgment in the Court of Appeal decision of R v McNally. The decision is now the leading authority on sexual fraud generally, and gender identity fraud specifically, under English law. The response will take the form of an academic judgment, in this instance a dissenting or counter-judgment. The article will (i) present the facts of the case, (ii) provide some detail regarding the d

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38

Ramkumar, M., R. Supriya, K. Chaithanya, J. Veena, and A. SnehaLatha. "“Credit Card Fraud” Detection Using Data Analytics A Comparative Analysis." 1 8, no. 1 (2022): 24–29. http://dx.doi.org/10.46632/jemm/8/1/4.

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Fraud events take place frequently which results in a huge financial loss. Fraud detections are dynamic and are not easy to identity. Data mining plays a vital role in detection of “Credit card fraud” done in fraudulent online transactions. Fraudsters use latest advanced methods which is an advantage. This process becomes challenging based on two major reasons -firstly, the profiles of users keep changing constantly and secondly, the datasets required for this are highly confusing. The overall performance of “Credit card fraud” detections is improved by sampling approach on the dataset. This r

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39

Choi, Dahee, and Kyungho Lee. "An Artificial Intelligence Approach to Financial Fraud Detection under IoT Environment: A Survey and Implementation." Security and Communication Networks 2018 (September 25, 2018): 1–15. http://dx.doi.org/10.1155/2018/5483472.

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Financial fraud under IoT environment refers to the unauthorized use of mobile transaction using mobile platform through identity theft or credit card stealing to obtain money fraudulently. Financial fraud under IoT environment is the fast-growing issue through the emergence of smartphone and online transition services. In the real world, a highly accurate process of financial fraud detection under IoT environment is needed since financial fraud causes financial loss. Therefore, we have surveyed financial fraud methods using machine learning and deep learning methodology, mainly from 2016 to 2

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40

Omotubora, Adekemi. "Old Wine in New Bottles? Critical and Comparative Perspectives on Identity Crimes under the Nigerian Cybercrime Act 2015." African Journal of International and Comparative Law 27, no. 4 (2019): 609–28. http://dx.doi.org/10.3366/ajicl.2019.0293.

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This article critically analyses the provisions on identity theft and identity fraud in the Nigerian Cybercrime Act passed in 2015. The objective of the article is to provide answers to three pressing questions: What was the law on identity crimes before the Cybercrime Act? How has the law addressed the peculiar challenges of identity theft and identity fraud online? Are the provisions of the law adequate and if not how should the law otherwise respond? This article provides an in-depth legal analysis of the relevant provisions of the law and highlights the gaps in the law through comparative

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41

Vaithyasubramanian S., Saravanan D., and Kirubhashankar C. K. "Communal Fraud Detection Algorithm for Establishing Identity Thefts in Online Shopping." International Journal of e-Collaboration 17, no. 3 (2021): 75–84. http://dx.doi.org/10.4018/ijec.2021070105.

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In recent times, e-commerce sector is gaining popularity and expressing progressive growth. Due to increasing the demand of automation process and the reach of internet towards the end-users have poised this trust. In spite of the technology advancements, the privacy and security of e-commerce merchant as well as consumer data are constantly under threat. Identity theft, which is considered as more important security problems for end-users, is addressed by one time password generated instantly. This paper focuses on communal fraud detection algorithm for protecting identity theft in online sho

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42

S, Shalini. "Behavioral Based Credit Card Fraud Detection." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 3556–61. http://dx.doi.org/10.22214/ijraset.2021.37134.

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Credit card fraud is a significant threat in the BFSI sector. This credit card fraud detection system analyzes user behavioral patterns and their location to identify any unusual patterns. This consists of user characteristics, which includes user spending styles as well as standard user geographic places to verify his identity. One of the user behavior patterns includes spending habits, usage patterns, etc. This system deals with user credit card data for various characteristics, which includes user country, usual spending procedures. Based upon previous transactions information of that perso

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43

Savirimuthu, Joseph. "Identity Theft and Systems Theory: The Fraud Act 2006 in Perspective." SCRIPT-ed 4, no. 4 (2007): 436–61. http://dx.doi.org/10.2966/scrip.040407.436.

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44

Zajac, Jeffrey D. "Medical identity fraud in the United States: could it happen here?" Medical Journal of Australia 192, no. 3 (2010): 119. http://dx.doi.org/10.5694/j.1326-5377.2010.tb03447.x.

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45

Zhang, Zhaohui, Xinxin Zhou, Xiaobo Zhang, Lizhi Wang, and Pengwei Wang. "A Model Based on Convolutional Neural Network for Online Transaction Fraud Detection." Security and Communication Networks 2018 (August 6, 2018): 1–9. http://dx.doi.org/10.1155/2018/5680264.

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Using wireless mobile terminals has become the mainstream of Internet transactions, which can verify the identity of users by passwords, fingerprints, sounds, and images. However, once these identity data are stolen, traditional information security methods will not avoid online transaction fraud. The existing convolutional neural network model for fraud detection needs to generate many derivative features. This paper proposes a fraud detection model based on the convolutional neural network in the field of online transactions, which constructs an input feature sequencing layer that implements

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46

Supriyadi, Heri, Dominicus Savio Priyarsono, Noer Azam Achsani, and Trias Andati. "Preventing Internal Fraud in Microlending Business Processes with Machine Learning Models: Confirmatory Factor Analysis (CFA) and Extreme Gradient Boosting (XGBoost)." Asia Proceedings of Social Sciences 8, no. 1 (2021): 34–37. http://dx.doi.org/10.31580/apss.v8i1.1943.

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Internal fraud (occupational fraud) in the microcredit business has caused significant losses for the banking industry and financial institutions. The unique microcredit business process and the amount of loans provided were relatively small for each customer. The more extensive the credit portfolio in a bank /financial institution, it took many marketing personnel/microcredit analysts. Internal fraud was one type of operational risk that banks/financial institutions often face that focused on microcredit services. The most common types of fraud were Corruption and Asset Abuse (ACFE), such as

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47

Lewis, Patrick J. "A story of identity: a cautionary tale." AlterNative: An International Journal of Indigenous Peoples 13, no. 2 (2017): 114–21. http://dx.doi.org/10.1177/1177180117695417.

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This is a personal narrative drawing upon the multi-dimensionality of narrative and storytelling to make sense of lived-experience. The article is an experience that deals with Indigenous Identity fraud in the academy in the moment of Reconciliation with Indigenous People and the people of Canada. It is a story of White settler claims to being Indigenous so as to reposition themselves within university discourse of Indigenisation. The narrative weaves through the structures and practices of colonisation, the complexities of identity and racialisation, and the restraint of universities to take

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48

Bulatov, K. B., P. V. Bezmaternykh, D. P. Nikolaev, and V. V. Arlazarov. "Towards a unified framework for identity documents analysis and recognition." Computer Optics 46, no. 3 (2022): 436–54. http://dx.doi.org/10.18287/2412-6179-co-1024.

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Identity documents recognition is far beyond classical optical character recognition problems. Automated ID document recognition systems are tasked not only with the extraction of editable and transferable data but with performing identity validation and preventing fraud, with an increasingly high cost of error. A significant amount of research is directed to the creation of ID analysis systems with a specific focus for a subset of document types, or a particular mode of image acquisition, however, one of the challenges of the modern world is an increasing demand for identity document recognit

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49

Dzomira, Shewangu. "Plastic Money and Electronic Banking Services Espousal vis-a-viz Financial Identity Theft Fraud Risk Awareness in a Developing Country." Journal of Economics and Behavioral Studies 9, no. 5 (2017): 255–64. http://dx.doi.org/10.22610/jebs.v9i5.1928.

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Exploitation of plastic money coupled with electronic banking services has come as expediency to financial establishment customers in Zimbabwe. This paper sought to analyze plastic money and electronic banking services espousal vis-a-viz financial identity theft fraud risk awareness in Zimbabwe banking sector via banksâ€™ websites. The theoretical underpinning for this study is Routine Activity Theory. The study used qualitative content analysis research technique for examination of the text content data through the consistent taxonomy process of coding and classifying themes or patterns to su

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50

Chohan, Dr Rozina, Engr Murk Chohan, Irfan Mir Chohan, and Farhana Mir Chohan. "Overcoming Trusting Barriers in Inter-organizational Identity Theft Prevention in Knowledge Sharing: A Case of UK Retailing Industry." Revista Gestão Inovação e Tecnologias 11, no. 4 (2021): 4946–74. http://dx.doi.org/10.47059/revistageintec.v11i4.2515.

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Inter-organizational knowledge sharing basically connects two or more firms with one another in exchange relationship for skills, expertise and knowledgeable personnel. Knowledge plays a key role in organizational performance but it is given a very little attention in terms of information security and requires addressing security concerns due to following reasons: First, because retailing is the most victimized channel due to online trade. Second, if organizational criminal wings can compromise the information system of one firm, they can attack other firms too. Hence, identity theft is a coll

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