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CARD-NOT-PRESENT FRAUD VICTIMIZATION: A ROUTINE ACTIVITIES APPROACH TO UNDERSTAND THE RISK FACTORS

Year 2020, Volume: 9 Issue: 1, 243 - 268, 12.05.2020
https://doi.org/10.28956/gbd.736179

Abstract

Banking cards, including credit cards, debit card, pre-paid debit cards and ATM cards, have become the primary payment method in online transactions. However, this popularity boosted the Card-not-present (CNP) fraud victimization. Despite numerous studies exploring technological solutions to prevent CNP fraud, there is a shortage of theoretically informed research exploring the online lifestyle correlates of CNP.
This study, which utilizes the dataset of Crime Survey for England and Wales 2014/2015, addresses this gap in the literature. Routine Activities Theory was used as the theoretical and conceptual framework in this present study. Bivariate and multivariate analyses results suggested that home users’ online lifestyle increases the risk of becoming a victim of CNP fraud. Buying goods or services, accessing online government services and online communication (email/instant messaging and chat rooms) emerged as risk factors. Illustrating the impact of technological vulnerabilities (mobile phones and public access computers) on the risk of CNP fraud victimization was another novel contribution of this study.
Additionally, personal guardianship measures, using complex passwords and different passwords, emerged as predictors of victimization. These results provide valuable implications for situational crime prevention efforts. Practical and theoretical implications of this study are further discussed.

References

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  • Akdemir, N. (2019). Understanding the Individual Level and Macro Level Causes of Economic Cybercrime Victimisation in the UK: A Contextual Vulnerabilities Approach to Examine Cybercrime Victimisation. Durham University.
  • Akdemir, N., Sungur, B., & Basaranel, B. U. (2020). Examining the Challenges of Policing Economic Cybercrime in the UK. The Journal of Security Sciences Special Edition, 111-132.
  • Anderson, R., & Murdoch, S. J. (2014). EMV: Why payment systems fail. Communications of the ACM, 57(6), 24-28.
  • Arango, C., Huynh, K. P., & Sabetti, L. (2015). Consumer payment choice: Merchant card acceptance versus pricing incentives. Journal of Banking & Finance, 55, 130-141.
  • Bossler, A. M., & Holt, T. J. (2009). Online Activities, Guardianship, and Malware Infection: An examination of Routine Activities Theory. International Journal of Cyber Criminology, 3(1), 400.
  • Bossler, A. M., & Holt, T. J. (2010). The effect of self-control on victimization in the cyberworld. Journal of Criminal Justice, 38(3), 227-236.
  • Bouchard, M., Wang, W., & Beauregard, E. (2012). Social capital, opportunity, and school-based victimization. Violence victims & Offenders, 27(5), 656-673.
  • Branco, B., Abreu, P., Gomes, A. S., Almeida, M. S., Ascensão, J. T., & Bizarro, P. J. a. p. a. (2020). Interleaved Sequence RNNs for Fraud Detection.
  • Bulakh, V., & Gupta, M. (2015). Characterizing credit card black markets on the web. Paper presented at the Proceedings of the 24th International Conference on World Wide Web.
  • Button, M., Nicholls, C. M., Kerr, J., & Owen, R. (2014). Online Frauds: Learning from Victims why They Fall for These Scams. Australian & New Zealand Journal of Criminology, 47(3), 391-408. doi:10.1177/0004865814521224
  • Ching, A. T., & Hayashi, F. (2010). Payment card rewards programs and consumer payment choice. Journal of Banking & Finance, 34(8), 1773-1787.
  • Choi, K.-s., Choo, K., & Sung, Y.-e. (2016). Demographic variables and risk factors in computer-crime: an empirical assessment. Cluster Computing, 19(1), 369-377.
  • Clarke, R. V. (1980). Situational crime prevention: Theory and practice. Brit. J. Criminology, 20, 136.
  • Clarke, R. V. (1995). Situational crime prevention. Crime and justice, 91-150.
  • Clarke, R. V., & Felson, M. (1998). Opportunity makes the thief: Practical theory for crime prevention. Retrieved from
  • Cohen, L. E., & Cantor, D. (1981). Residential burglary in the United States: Life-style and demographic factors associated with the probability of victimization. Journal of Research in Crime and Delinquency, 18(1), 113-127.
  • Cohen, L. E., & Felson, M. (1979). Social change and crime rate trends: A routine activity approach. American Sociological Review, 588-608.
  • Cohen, L. E., Kluegel, J. R., & Land, K. C. (1981). Social Inequality and Predatory Criminal Victimization: An Exposition and Test of a Formal Theory. American Sociological Review, 46(5), 505-524.
  • Corre, K., Barais, O., Sunyé, G., Frey, V., & Crom, J.-M. (2017). Why can’t users choose their identity providers on the web? Proceedings on Privacy Enhancing Technologies, 3, 72-86.
  • Cross, C., Richards, K., & Smith, R. G. (2016). Improving responses to online fraud victims: An examination of reporting and support.
  • Dytham, C. (2011). Choosing and using statistics: a biologist's guide: John Wiley & Sons. Field, A. (2009). Discovering statistics using SPSS: Sage publications.
  • Fisher, B. S., Daigle, L. E., & Cullen, F. T. (2010). What distinguishes single from recurrent sexual victims? The role of lifestyle‐routine activities and first‐incident characteristics. Justice Quarterly, 27(1), 102-129.
  • FTC. (2019). Consumer Sentinel Network. Retrieved from https://www.ftc.gov/system/files/documents/reports/consumer-sentinel-network-data-book-2018/consumer_sentinel_network_data_book_2018_0.pdf
  • Garg, V., & Nilizadeh, S. (2013). Craigslist scams and community composition: Investigating online fraud victimization. Paper presented at the Security and Privacy Workshops (SPW), 2013 IEEE.
  • Gillespie, A. A., & Magor, S. (2020). Tackling online fraud. Paper presented at the ERA Forum. Grabosky, P. N. (2001). Virtual Criminality: Old Wine in New Bottles? Social and Legal Studies, 10(2), 243-250.
  • Healey, J. F. (2014). Statistics: A Tool for Social Research (9 ed.). Belmont, CA: Wadsworth Publishing Company.
  • Hindelang, M. J., Gottfredson, M. R., & Garofalo, J. (1978). Victims of personal crime: An empirical foundation for a theory of personal victimization: Ballinger Cambridge, MA.
  • Ho, R. (2013). Handbook of univariate and multivariate data analysis with IBM SPSS: CRC Press. Holt, T. J. (2013). Examining the forces shaping cybercrime markets online. Social Science Computer Review, 31(2), 165-177.
  • Holt, T. J., & Bossler, A. (2016). Cybercrime in progress: Theory and prevention of technology-enabled offenses: Routledge.
  • Holt, T. J., & Bossler, A. M. (2013). Examining the Relationship between Routine Activities and Malware Infection Indicators. Journal of Contemporary Criminal Justice, 1043986213507401.
  • Holt, T. J., & Turner, M. G. (2012). Examining risks and protective factors of on-line identity theft. Deviant Behavior, 33(4), 308-323.
  • Holtfreter, K., Reisig, M., & Pratt, T. (2008). Low Self-Control, Routine Activities, and Fraud Victimization. Criminology, 46(1), 189-220.
  • Holtfreter, K., Reisig, M. D., Leeper Piquero, N., & Piquero, A. R. (2010). Low self-control and fraud: Offending, victimization, and their overlap. Criminal Justice and Behavior, 37(2), 188-203.
  • Howard, R. (2009). Cyber fraud: tactics, techniques and procedures: CRC press.
  • Hutchings, A., & Hayes, H. (2008). Routine activity theory and phishing victimisation: Who gets caught in the net. Current Issues Crim. Just., 20, 433.
  • Jackson, S. L. (2013). Statistics plain and simple: Cengage Learning.
  • Jansen, J., & Leukfeldt, R. (2015). How people help fraudsters steal their money: An analysis of 600 online banking fraud cases. Paper presented at the Socio-Technical Aspects in Security and Trust (STAST), 2015 Workshop on.
  • Jansen, J., & Van Schaik, P. J. C. i. H. B. (2018). Testing a model of precautionary online behaviour: The case of online banking. 87, 371-383.
  • Jordan, G., Leskovar, R., & Marič, M. (2018). Impact of fear of identity theft and perceived risk on online purchase intention. Organizacija, 51(2), 146-155.
  • Kahn, C. M., & Liñares-Zegarra, J. M. (2016). Identity theft and consumer payment choice: Does security really matter? Journal of Financial Services Research, 50(1), 121-159.
  • Kennedy, L. W., & Forde, D. R. J. C. (1990). Routine activities and crime: An analysis of victimization in Canada. 28(1), 137-152.
  • Kokh, M. T. (2019). Symantec Mobile Threat Defense: Using Mobile to Stay One Step Ahead of PC Attacks. Retrieved from https://symantec-blogs.broadcom.com/blogs/product-insights/symantec-mobile-threat-defense-using-mobile-stay-one-step-ahead-pc-attacks?es_p=10097396
  • Leukfeldt, E. R., & Yar, M. (2016). Applying routine activity theory to cybercrime: A theoretical and empirical analysis. Deviant Behavior, 37(3), 263-280.
  • Lipsman, A. (2019). Global Ecommerce 2019, Ecommerce Continues Strong Gains Amid Global Economic Uncertainty. Retrieved from https://www.emarketer.com/content/global-ecommerce-2019
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ÇEVRİMİÇİ BANKA KARTI DOLANDIRICILIĞI: RİSK FAKTÖRLERİNİ ANLAMADA RUTİN AKTİVİTELER YAKLAŞIMI

Year 2020, Volume: 9 Issue: 1, 243 - 268, 12.05.2020
https://doi.org/10.28956/gbd.736179

Abstract

Kredi kartları, banka kartı, ön ödemeli banka kartları ve ATM kartları dahil olmak üzere banka kartları, çevrimiçi işlemlerde öncelikli ödeme yöntemi haline gelmiştir. Ancak bu popülerlik banka kartlarının fiziksel olmayan kullanımı dolandıcılığı (BKFOK) mağduriyetinin artmasına neden olmuştur. BKFOK dolandırıcılığını önlemek için teknolojik çözümleri araştıran çok sayıda çalışmaya rağmen, BKFOK dolandırıcılığının çevrimiçi yaşam tarzı ilişkilerini araştıran teorik olarak temellendirilmiş araştırma eksikliği mevcuttur.
İngiltere ve Galler 2014/2015 Suç Araştırması veri setininin kullanıldığı çalışmamız, literatürdeki bu boşluk üzerinde kurgulanmıştır. Bu çalışmada teorik ve kavramsal çerçeve olarak Rutin Aktiviteler Teorisi kullanılmıştır. İki değişkenli ve çok değişkenli analiz sonuçları, ev kullanıcılarının çevrimiçi yaşam tarzının BKFOK Dolandırıcılığı kurbanı olma riskini artırdığını göstermektedir. Çevrimiçi mal veya hizmet satın almak, çevrimiçi devlet hizmetlerine erişmek ve çevrimiçi iletişim (e-posta / anlık mesajlaşma ve sohbet odaları) risk faktörleri olarak ortaya çıkmıştır. Teknolojik güvenlik açıklarının (cep telefonları ve kamusal alanda kullanılan açık erişim bilgisayarlar) BKFOK dolandırıcılığı mağduriyeti riski üzerindeki etkisini ortaya koymak, bu çalışmanın bir başka önemli katkısıdır.
Buna ek olarak, karmaşık parolalar ve farklı parolalar gibi bireysel güvenlik tedbirlerini kullanmak mağduriyetin tahminleyici faktörleri olarak ortaya çıkmıştır. Bu sonuçlar durumsal suç önleme çabaları için değerli sonuçlar vermektedir. Bu çalışmanın pratik ve teorik sonuçları metin içerisinde tartışılmaktadır.

References

  • Ahmad, Z., Zeki, A. M., & Olowolayemo, A. (2016). Security Failures in EMV Smart Card Payment Systems. Paper presented at the Information and Communication Technology for The Muslim World (ICT4M), 2016 6th International Conference on.
  • Akdemir, N. (2019). Understanding the Individual Level and Macro Level Causes of Economic Cybercrime Victimisation in the UK: A Contextual Vulnerabilities Approach to Examine Cybercrime Victimisation. Durham University.
  • Akdemir, N., Sungur, B., & Basaranel, B. U. (2020). Examining the Challenges of Policing Economic Cybercrime in the UK. The Journal of Security Sciences Special Edition, 111-132.
  • Anderson, R., & Murdoch, S. J. (2014). EMV: Why payment systems fail. Communications of the ACM, 57(6), 24-28.
  • Arango, C., Huynh, K. P., & Sabetti, L. (2015). Consumer payment choice: Merchant card acceptance versus pricing incentives. Journal of Banking & Finance, 55, 130-141.
  • Bossler, A. M., & Holt, T. J. (2009). Online Activities, Guardianship, and Malware Infection: An examination of Routine Activities Theory. International Journal of Cyber Criminology, 3(1), 400.
  • Bossler, A. M., & Holt, T. J. (2010). The effect of self-control on victimization in the cyberworld. Journal of Criminal Justice, 38(3), 227-236.
  • Bouchard, M., Wang, W., & Beauregard, E. (2012). Social capital, opportunity, and school-based victimization. Violence victims & Offenders, 27(5), 656-673.
  • Branco, B., Abreu, P., Gomes, A. S., Almeida, M. S., Ascensão, J. T., & Bizarro, P. J. a. p. a. (2020). Interleaved Sequence RNNs for Fraud Detection.
  • Bulakh, V., & Gupta, M. (2015). Characterizing credit card black markets on the web. Paper presented at the Proceedings of the 24th International Conference on World Wide Web.
  • Button, M., Nicholls, C. M., Kerr, J., & Owen, R. (2014). Online Frauds: Learning from Victims why They Fall for These Scams. Australian & New Zealand Journal of Criminology, 47(3), 391-408. doi:10.1177/0004865814521224
  • Ching, A. T., & Hayashi, F. (2010). Payment card rewards programs and consumer payment choice. Journal of Banking & Finance, 34(8), 1773-1787.
  • Choi, K.-s., Choo, K., & Sung, Y.-e. (2016). Demographic variables and risk factors in computer-crime: an empirical assessment. Cluster Computing, 19(1), 369-377.
  • Clarke, R. V. (1980). Situational crime prevention: Theory and practice. Brit. J. Criminology, 20, 136.
  • Clarke, R. V. (1995). Situational crime prevention. Crime and justice, 91-150.
  • Clarke, R. V., & Felson, M. (1998). Opportunity makes the thief: Practical theory for crime prevention. Retrieved from
  • Cohen, L. E., & Cantor, D. (1981). Residential burglary in the United States: Life-style and demographic factors associated with the probability of victimization. Journal of Research in Crime and Delinquency, 18(1), 113-127.
  • Cohen, L. E., & Felson, M. (1979). Social change and crime rate trends: A routine activity approach. American Sociological Review, 588-608.
  • Cohen, L. E., Kluegel, J. R., & Land, K. C. (1981). Social Inequality and Predatory Criminal Victimization: An Exposition and Test of a Formal Theory. American Sociological Review, 46(5), 505-524.
  • Corre, K., Barais, O., Sunyé, G., Frey, V., & Crom, J.-M. (2017). Why can’t users choose their identity providers on the web? Proceedings on Privacy Enhancing Technologies, 3, 72-86.
  • Cross, C., Richards, K., & Smith, R. G. (2016). Improving responses to online fraud victims: An examination of reporting and support.
  • Dytham, C. (2011). Choosing and using statistics: a biologist's guide: John Wiley & Sons. Field, A. (2009). Discovering statistics using SPSS: Sage publications.
  • Fisher, B. S., Daigle, L. E., & Cullen, F. T. (2010). What distinguishes single from recurrent sexual victims? The role of lifestyle‐routine activities and first‐incident characteristics. Justice Quarterly, 27(1), 102-129.
  • FTC. (2019). Consumer Sentinel Network. Retrieved from https://www.ftc.gov/system/files/documents/reports/consumer-sentinel-network-data-book-2018/consumer_sentinel_network_data_book_2018_0.pdf
  • Garg, V., & Nilizadeh, S. (2013). Craigslist scams and community composition: Investigating online fraud victimization. Paper presented at the Security and Privacy Workshops (SPW), 2013 IEEE.
  • Gillespie, A. A., & Magor, S. (2020). Tackling online fraud. Paper presented at the ERA Forum. Grabosky, P. N. (2001). Virtual Criminality: Old Wine in New Bottles? Social and Legal Studies, 10(2), 243-250.
  • Healey, J. F. (2014). Statistics: A Tool for Social Research (9 ed.). Belmont, CA: Wadsworth Publishing Company.
  • Hindelang, M. J., Gottfredson, M. R., & Garofalo, J. (1978). Victims of personal crime: An empirical foundation for a theory of personal victimization: Ballinger Cambridge, MA.
  • Ho, R. (2013). Handbook of univariate and multivariate data analysis with IBM SPSS: CRC Press. Holt, T. J. (2013). Examining the forces shaping cybercrime markets online. Social Science Computer Review, 31(2), 165-177.
  • Holt, T. J., & Bossler, A. (2016). Cybercrime in progress: Theory and prevention of technology-enabled offenses: Routledge.
  • Holt, T. J., & Bossler, A. M. (2013). Examining the Relationship between Routine Activities and Malware Infection Indicators. Journal of Contemporary Criminal Justice, 1043986213507401.
  • Holt, T. J., & Turner, M. G. (2012). Examining risks and protective factors of on-line identity theft. Deviant Behavior, 33(4), 308-323.
  • Holtfreter, K., Reisig, M., & Pratt, T. (2008). Low Self-Control, Routine Activities, and Fraud Victimization. Criminology, 46(1), 189-220.
  • Holtfreter, K., Reisig, M. D., Leeper Piquero, N., & Piquero, A. R. (2010). Low self-control and fraud: Offending, victimization, and their overlap. Criminal Justice and Behavior, 37(2), 188-203.
  • Howard, R. (2009). Cyber fraud: tactics, techniques and procedures: CRC press.
  • Hutchings, A., & Hayes, H. (2008). Routine activity theory and phishing victimisation: Who gets caught in the net. Current Issues Crim. Just., 20, 433.
  • Jackson, S. L. (2013). Statistics plain and simple: Cengage Learning.
  • Jansen, J., & Leukfeldt, R. (2015). How people help fraudsters steal their money: An analysis of 600 online banking fraud cases. Paper presented at the Socio-Technical Aspects in Security and Trust (STAST), 2015 Workshop on.
  • Jansen, J., & Van Schaik, P. J. C. i. H. B. (2018). Testing a model of precautionary online behaviour: The case of online banking. 87, 371-383.
  • Jordan, G., Leskovar, R., & Marič, M. (2018). Impact of fear of identity theft and perceived risk on online purchase intention. Organizacija, 51(2), 146-155.
  • Kahn, C. M., & Liñares-Zegarra, J. M. (2016). Identity theft and consumer payment choice: Does security really matter? Journal of Financial Services Research, 50(1), 121-159.
  • Kennedy, L. W., & Forde, D. R. J. C. (1990). Routine activities and crime: An analysis of victimization in Canada. 28(1), 137-152.
  • Kokh, M. T. (2019). Symantec Mobile Threat Defense: Using Mobile to Stay One Step Ahead of PC Attacks. Retrieved from https://symantec-blogs.broadcom.com/blogs/product-insights/symantec-mobile-threat-defense-using-mobile-stay-one-step-ahead-pc-attacks?es_p=10097396
  • Leukfeldt, E. R., & Yar, M. (2016). Applying routine activity theory to cybercrime: A theoretical and empirical analysis. Deviant Behavior, 37(3), 263-280.
  • Lipsman, A. (2019). Global Ecommerce 2019, Ecommerce Continues Strong Gains Amid Global Economic Uncertainty. Retrieved from https://www.emarketer.com/content/global-ecommerce-2019
  • Marcum, C. D., Higgins, G. E., & Ricketts, M. L. (2010). Potential factors of online victimization of youth: An examination of adolescent online behaviors utilizing routine activity theory. Deviant Behavior, 31(5), 381-410.
  • Meier, R. F., & Miethe, T. D. (1993). Understanding theories of criminal victimization. Crime and justice, 459-499.
  • Miethe, T. D., & McDowall, D. (1993). Contextual effects in models of criminal victimization. Social Forces, 71(3), 741-759.
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There are 83 citations in total.

Details

Primary Language English
Subjects Criminology, Sociology, International Relations
Journal Section Articles
Authors

Naci Akdemir 0000-0002-4288-6482

Serkan Yenal This is me 0000-0002-8188-5095

Publication Date May 12, 2020
Submission Date April 14, 2020
Published in Issue Year 2020 Volume: 9 Issue: 1

Cite

APA Akdemir, N., & Yenal, S. (2020). CARD-NOT-PRESENT FRAUD VICTIMIZATION: A ROUTINE ACTIVITIES APPROACH TO UNDERSTAND THE RISK FACTORS. Güvenlik Bilimleri Dergisi, 9(1), 243-268. https://doi.org/10.28956/gbd.736179

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