Mitigating Financial Fraud and Cybercrime: A Systematic Literature Study

Mardiana Ruslan

doi:10.62207/mnp8nd05

Authors

Mardiana Ruslan Universitas Muhammadiyah Luwuk, Sulawesi Tengah Author

DOI:

https://doi.org/10.62207/mnp8nd05

Keywords:

Cyber Security, Limitations, Fraud Detection Systems, Response Strategies, Future Research, Advanced Technology, Social Impact, Policy Implications, Cross-Border Cooperation.

Abstract

This research synthesizes and discusses various aspects of cyber security measures, fraud detection systems, response strategies, and future research directions. This research explores the limitations faced in cyber security research, including methodological constraints, data limitations, and challenges in generalizing results. Future research directions are proposed, focusing on the development of advanced technologies, the social and psychological impact of cybercrime, policy and legal implications, and cross-border cooperation. By addressing these limitations and pursuing future research directions, this research aims to improve the quality and relevance of cyber security research to effectively counter cyber threats.

References

Abbasi, A., Albrecht, C., Vance, A., & Hansen, J. (2012). Metafraud: a meta-learning framework for detecting financial fraud. Mis Quarterly, 36(4), 1293. https://doi.org/10.2307/41703508

Aboud, A. and Robinson, B. (2020). Fraudulent financial reporting and data analytics: an explanatory study from ireland. Accounting Research Journal, 35(1), 21-36. https://doi.org/10.1108/arj-04-2020-0079

Adger, W. (2006). Vulnerability. Global Environmental Change, 16(3), 268-281. https://doi.org/10.1016/j.gloenvcha.2006.02.006

Ahmed, M., Ansar, K., Muckley, C., Khan, A., Anjum, A., & Talha, M. (2021). A semantic rule based digital fraud detection. Peerj Computer Science, 7, e649. https://doi.org/10.7717/peerj-cs.649

Akinbowale, O., Klingelhöfer, H., & Zerihun, M. (2020). Analysis of cyber-crime effects on the banking sector using the balanced score card: a survey of literature. Journal of Financial Crime, 27(3), 945-958. https://doi.org/10.1108/jfc-03-2020-0037

Alade, O., Amusan, E., Adedeji, O., & Adebayo, S. (2021). Cybercrime and underground attack technologies: perspectives from the nigerian banking sector... https://doi.org/10.22624/aims/isteams-2021/v27p6

Alastal, A. and Shaqfa, A. (2023). Enhancing police officers’ cybercrime investigation skills using a checklist tool. Journal of Data Analysis and Information Processing, 11(02), 121-143. https://doi.org/10.4236/jdaip.2023.112008

Alfian, A. (2023). Fraud analytics practices in public-sector transactions: a systematic review. Journal of Public Budgeting Accounting & Financial Management, 35(5), 685-710. https://doi.org/10.1108/jpbafm-11-2022-0175

Ali, L., Ali, F., Surendran, P., & Thomas, B. (2017). The effects of cyber threats on customer’s behaviour in e-banking services. International Journal of E-Education E-Business E-Management and E-Learning, 7(1), 70-78. https://doi.org/10.17706/ijeeee.2017.7.1.70-78

Arri, H. (2022). Real-time credit card fraud detection using machine learning. Interantional Journal of Scientific Research in Engineering and Management, 06(04). https://doi.org/10.55041/ijsrem12659

Barreto, P. and Naehrig, M. (2006). Pairing-friendly elliptic curves of prime order., 319-331. https://doi.org/10.1007/11693383_22

Bertoni, G., Daemen, J., Peeters, M., & Assche, G. (2012). Duplexing the sponge: single-pass authenticated encryption and other applications., 320-337. https://doi.org/10.1007/978-3-642-28496-0_19

Bolton, R. and Hand, D. (2002). Statistical fraud detection: a review. Statistical Science, 17(3). https://doi.org/10.1214/ss/1042727940

Borwell, J., Jansen, J., & Stol, W. (2021). The psychological and financial impact of cybercrime victimization: a novel application of the shattered assumptions theory. Social Science Computer Review, 40(4), 933-954. https://doi.org/10.1177/0894439320983828

Bossler, A. and Holt, T. (2012). Patrol officers' perceived role in responding to cybercrime. Policing an International Journal, 35(1), 165-181. https://doi.org/10.1108/13639511211215504

Boyle, D., DeZoort, F., & Hermanson, D. (2015). The effect of alternative fraud model use on auditors’ fraud risk judgments. Journal of Accounting and Public Policy, 34(6), 578-596. https://doi.org/10.1016/j.jaccpubpol.2015.05.006

Brakerski, Z. and Vaikuntanathan, V. (2011). Fully homomorphic encryption from ring-lwe and security for key dependent messages., 505-524. https://doi.org/10.1007/978-3-642-22792-9_29

Buil‐Gil, D., Miró-Llinares, F., Moneva, A., Kemp, S., & Díaz-Castaño, N. (2020). Cybercrime and shifts in opportunities during covid-19: a preliminary analysis in the uk. European Societies, 23(sup1), S47-S59. https://doi.org/10.1080/14616696.2020.1804973

Burton, A., Cooper, C., Dar, A., Mathews, L., & Tripathi, K. (2022). Exploring how, why and in what contexts older adults are at risk of financial cybercrime victimisation: a realist review. Experimental Gerontology, 159, 111678. https://doi.org/10.1016/j.exger.2021.111678

Button, M., Nicholls, C., Kerr, J., & Owen, R. (2015). Online fraud victims in england and wales: victims' views on sentencing and the opportunity for restorative justice?. The Howard Journal of Criminal Justice, 54(2), 193-211. https://doi.org/10.1111/hojo.12123

Cai, Y. and Zhu, D. (2016). Fraud detections for online businesses: a perspective from blockchain technology. Financial Innovation, 2(1). https://doi.org/10.1186/s40854-016-0039-4

Carcillo, F., Pozzolo, A., Borgne, Y., Caelen, O., Mazzer, Y., & Bontempi, G. (2018). Scarff : a scalable framework for streaming credit card fraud detection with spark. Information Fusion, 41, 182-194. https://doi.org/10.1016/j.inffus.2017.09.005

Chen, B., Kuo, W., & Wuu, L. (2012). Robust smart‐card‐based remote user password authentication scheme. International Journal of Communication Systems, 27(2), 377-389. https://doi.org/10.1002/dac.2368

Chen, S., Chundong, G., Jiang, D., Ding, F., Ma, T., Zhang, S., … & Li, S. (2021). The spatiotemporal pattern and driving factors of cyber fraud crime in china. Isprs International Journal of Geo-Information, 10(12), 802. https://doi.org/10.3390/ijgi10120802

Chen, S., Ding, F., Jiang, D., Dong, J., Zhang, S., Guo, Q., … & Chundong, G. (2023). Exploring the global geography of cybercrime and its driving forces. Humanities and Social Sciences Communications, 10(1). https://doi.org/10.1057/s41599-023-01560-x

Chen, Y. and Wu, Z. (2022). Financial fraud detection of listed companies in china: a machine learning approach. Sustainability, 15(1), 105. https://doi.org/10.3390/su15010105

Cole, T. (2023). How are financial institutions enabling online fraud? a developmental online financial fraud policy review. Journal of Financial Crime, 30(6), 1458-1473. https://doi.org/10.1108/jfc-10-2022-0261

Drew, J. (2020). A study of cybercrime victimisation and prevention: exploring the use of online crime prevention behaviours and strategies. Journal of Criminological Research Policy and Practice, 6(1), 17-33. https://doi.org/10.1108/jcrpp-12-2019-0070

Duffin, D. and Djohan, D. (2022). The analysis of fraud hexagon towards earnings management. Jurnal Impresi Indonesia, 1(4), 328-340. https://doi.org/10.36418/jii.v1i4.54

Dumchykov, M., Utkina, M., & Bondarenko, O. (2022). Cybercrime as a threat to the national security of the baltic states and ukraine: the comparative analysis. International Journal of Safety and Security Engineering, 12(4), 481-490. https://doi.org/10.18280/ijsse.120409

Galinec, D., Možnik, D., & Guberina, B. (2017). Cybersecurity and cyber defence: national level strategic approach. Automatika, 58(3), 273-286. https://doi.org/10.1080/00051144.2017.1407022

Goel, S., Williams, K., & Dincelli, E. (2017). Got phished? internet security and human vulnerability. Journal of the Association for Information Systems, 18(1), 22-44. https://doi.org/10.17705/1jais.00447

Goldstein, M. and Uchida, S. (2016). A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data. Plos One, 11(4), e0152173. https://doi.org/10.1371/journal.pone.0152173

Golladay, K. (2022). Financial fraud victimization: an examination of distress and financial complications. Journal of Financial Crime, 30(6), 1606-1628. https://doi.org/10.1108/jfc-08-2022-0207

Gope, P. and Sikdar, B. (2019). Lightweight and privacy-preserving two-factor authentication scheme for iot devices. Ieee Internet of Things Journal, 6(1), 580-589. https://doi.org/10.1109/jiot.2018.2846299

Halbouni, S., Obeid, N., & Garbou, A. (2016). Corporate governance and information technology in fraud prevention and detection. Managerial Auditing Journal, 31(6/7), 589-628. https://doi.org/10.1108/maj-02-2015-1163

Hasan, S., Rahman, R., Abdillah, S., & Omar, N. (2015). Perception and awareness of young internet users towards cybercrime: evidence from malaysia. Journal of Social Sciences, 11(4), 395-404. https://doi.org/10.3844/jssp.2015.395.404

Holt, T. and Lee, J. (2019). Policing cybercrime through law enforcement and industry mechanisms., 644-662. https://doi.org/10.1093/oxfordhb/9780198812746.013.34

Hussein, A., Khairy, R., Najeeb, S., & Alrikabi, H. (2021). Credit card fraud detection using fuzzy rough nearest neighbor and sequential minimal optimization with logistic regression. International Journal of Interactive Mobile Technologies (Ijim), 15(05), 24. https://doi.org/10.3991/ijim.v15i05.17173

Iqbal, M., Abd-Alrazaq, A., & Househ, M. (2022). Artificial intelligence solutions to detect fraud in healthcare settings: a scoping review.. https://doi.org/10.3233/shti220649

Jampen, D., Gür, G., Sutter, T., & Tellenbach, B. (2020). Don’t click: towards an effective anti-phishing training. a comparative literature review. Human-Centric Computing and Information Sciences, 10(1). https://doi.org/10.1186/s13673-020-00237-7

Jan, C. (2018). An effective financial statements fraud detection model for the sustainable development of financial markets: evidence from taiwan. Sustainability, 10(2), 513. https://doi.org/10.3390/su10020513

Jurgovsky, J., Granitzer, M., Ziegler, K., Calabretto, S., Portier, P., He-Guelton, L., … & Caelen, O. (2018). Sequence classification for credit-card fraud detection. Expert Systems With Applications, 100, 234-245. https://doi.org/10.1016/j.eswa.2018.01.037

Kamaliah, K., Marjuni, N., Mohamed, N., Sanusi, Z., Anugerah, R., & Mara, U. (2018). Effectiveness of monitoring mechanisms and mitigation of fraud incidents in the public sector. Administratie Si Management Public, (30), 82-95. https://doi.org/10.24818/amp/2018.30-06

Khan, S., Saleh, T., Dorasamy, M., Khan, N., Leng, O., & Vergara, R. (2022). A systematic literature review on cybercrime legislation. F1000research, 11, 971. https://doi.org/10.12688/f1000research.123098.1

Khormuji, M., Bazrafkan, M., Sharifian, M., Mirabedini, S., & Harounabadi, A. (2014). Credit card fraud detection with a cascade artificial neural network and imperialist competitive algorithm. International Journal of Computer Applications, 96(25), 1-9. https://doi.org/10.5120/16947-6736

Koohang, A., Anderson, J., Nord, J., & Paliszkiewicz, J. (2019). Building an awareness-centered information security policy compliance model. Industrial Management & Data Systems, 120(1), 231-247. https://doi.org/10.1108/imds-07-2019-0412

Kou, Y., Lu, C., Sirwongwattana, S., & Huang, Y. Survey of fraud detection techniques.. https://doi.org/10.1109/icnsc.2004.1297040

Koziarski, J. and Lee, J. (2020). Connecting evidence-based policing and cybercrime.. https://doi.org/10.21428/cb6ab371.40515372

Kumaraguru, P., Sheng, S., Acquisti, A., Cranor, L., & Hong, J. (2010). Teaching johnny not to fall for phish. Acm Transactions on Internet Technology, 10(2), 1-31. https://doi.org/10.1145/1754393.1754396

Lee, S., Faloutsos, C., Chae, D., & Kim, S. (2017). Fraud detection in comparison-shopping services: patterns and anomalies in user click behaviors. Ieice Transactions on Information and Systems, E100.D(10), 2659-2663. https://doi.org/10.1587/transinf.2017edl8094

Lichtenberg, P., Sugarman, M., Paulson, D., Ficker, L., & Rahman‐Filipiak, A. (2015). Psychological and functional vulnerability predicts fraud cases in older adults: results of a longitudinal study. Clinical Gerontologist, 39(1), 48-63. https://doi.org/10.1080/07317115.2015.1101632

López-Alt, A., Tromer, E., & Vaikuntanathan, V. (2012). On-the-fly multiparty computation on the cloud via multikey fully homomorphic encryption.. https://doi.org/10.1145/2213977.2214086

Malik, M. and Islam, U. (2019). Cybercrime: an emerging threat to the banking sector of pakistan. Journal of Financial Crime, 26(1), 50-60. https://doi.org/10.1108/jfc-11-2017-0118

McBride, T., Ekstrom, M., Lusty, L., Sexton, J., & Townsend, A. (2020). Data integrity: recovering from ransomware and other destructive events.. https://doi.org/10.6028/nist.sp.1800-11

Mugari, I. (2023). Trends, impacts and responses to cybercrime in the zimbabwean retail sector. Safer Communities, 22(4), 254-265. https://doi.org/10.1108/sc-03-2023-0011

Nakanishi, I. and Maruoka, T. (2019). Biometrics using electroencephalograms stimulated by personal ultrasound and multidimensional nonlinear features. Electronics, 9(1), 24. https://doi.org/10.3390/electronics9010024

Narsimha, B., Raghavendran, C., Rajyalakshmi, P., Reddy, G., Bhargavi, M., & Naresh, P. (2022). Cyber defense in the age of artificial intelligence and machine learning for financial fraud detection application. International Journal of Electrical and Electronics Research, 10(2), 87-92. https://doi.org/10.37391/ijeer.100206

Nelson, M. (2009). A model and literature review of professional skepticism in auditing. Auditing a Journal of Practice & Theory, 28(2), 1-34. https://doi.org/10.2308/aud.2009.28.2.1

Ng, M., Widanaralalage, K., Buchanan, T., & Coopamootoo, K. (2022). Cybercrimes in the aftermath of covid-19: present concerns and future directions. Journal of Concurrent Disorders. https://doi.org/10.54127/lwvw7835

Nicholls, J., Kuppa, A., & Le-Khac, N. (2021). Financial cybercrime: a comprehensive survey of deep learning approaches to tackle the evolving financial crime landscape. Ieee Access, 9, 163965-163986. https://doi.org/10.1109/access.2021.3134076

Ningrum, N., Batubara, K., & Hapsari, A. (2022). Overcoming fraud and cybercrime: the role of integrity in village financial system reporting. Asia Pacific Fraud Journal, 7(1), 53. https://doi.org/10.21532/apfjournal.v7i1.252

Othman, I. (2021). Financial statement fraud: challenges and technology deployment in fraud detection. International Journal of Accounting and Financial Reporting, 11(4), 1. https://doi.org/10.5296/ijafr.v11i4.19067

Park, M., Golden, K., Vizcaino-Vickers, S., Jidong, D., & Raj, S. (2021). Sociocultural values, attitudes and risk factors associated with adolescent cyberbullying in east asia: a systematic review. Cyberpsychology Journal of Psychosocial Research on Cyberspace, 15(1). https://doi.org/10.5817/cp2021-1-5

Roškot, M., Wanasika, I., & Kroupova, Z. (2020). Cybercrime in europe: surprising results of an expensive lapse. Journal of Business Strategy, 42(2), 91-98. https://doi.org/10.1108/jbs-12-2019-0235

Sahebjamnia, N., Torabi, S., & Mansouri, S. (2015). Integrated business continuity and disaster recovery planning: towards organizational resilience. European Journal of Operational Research, 242(1), 261-273. https://doi.org/10.1016/j.ejor.2014.09.055

Sanusi, K. and Dickason-Koekemoer, Z. (2022). Cryptocurrency returns, cybercrime and stock market volatility: gas and regime switching approaches. International Journal of Economics and Financial Issues, 12(6), 52-64. https://doi.org/10.32479/ijefi.13555

Sarno, D., McPherson, R., & Neider, M. (2022). Is the key to phishing training persistence?: developing a novel persistent intervention.. Journal of Experimental Psychology Applied, 28(1), 85-99. https://doi.org/10.1037/xap0000410

Sasikala, G., Mohan, L., Sathyasri, B., Supraja, C., Mahalakshmi, V., Mole, S., … & Dejene, M. (2022). An innovative sensing machine learning technique to detect credit card frauds in wireless communications. Wireless Communications and Mobile Computing, 2022, 1-12. https://doi.org/10.1155/2022/2439205

Shah, M., Jones, P., & Choudrie, J. (2019). Cybercrimes prevention: promising organisational practices. Information Technology and People, 32(5), 1125-1129. https://doi.org/10.1108/itp-10-2019-564

Shah, V. (2022). How efficient is machine learning in detecting financial fraud using mobile transaction metadata?. Journal of Student Research, 11(3). https://doi.org/10.47611/jsrhs.v11i3.2865

Smith, K., Jones, A., Johnson, L., & Smith, L. (2019). Examination of cybercrime and its effects on corporate stock value. Journal of Information Communication and Ethics in Society, 17(1), 42-60. https://doi.org/10.1108/jices-02-2018-0010

Sun, N. (2022). How do organizations seek cyber assurance? investigations on the adoption of the common criteria and beyond.. https://doi.org/10.48550/arxiv.2203.01526

Tan, S., Ng, K., Khan, S., & Tan, O. (2022). Data-centric analysis to combat cybercrime in malaysia., 61-73. https://doi.org/10.2991/978-2-494069-59-6_6

Viaene, S., Derrig, R., Baesens, B., & Dedene, G. (2002). A comparison of state‐of‐the‐art classification techniques for expert automobile insurance claim fraud detection. Journal of Risk & Insurance, 69(3), 373-421. https://doi.org/10.1111/1539-6975.00023

Vlasselaer, V., Bravo, C., Caelen, O., Eliassi-Rad, T., Akoglu, L., Snoeck, M., … & Baesens, B. (2015). Apate: a novel approach for automated credit card transaction fraud detection using network-based extensions. Decision Support Systems, 75, 38-48. https://doi.org/10.1016/j.dss.2015.04.013

Weijer, S., Leukfeldt, R., & Bernasco, W. (2018). Determinants of reporting cybercrime: a comparison between identity theft, consumer fraud, and hacking. European Journal of Criminology, 16(4), 486-508. https://doi.org/10.1177/1477370818773610

Wilson, M., Cross, C., Holt, T., & Powell, A. (2022). Police preparedness to respond to cybercrime in australia: an analysis of individual and organizational capabilities. Journal of Criminology, 55(4), 468-494. https://doi.org/10.1177/26338076221123080

Yarovenko, H., Kuzmenko, O., & Stumpo, M. (2020). Strategy for determining country ranking by level of cybersecurity. Financial Markets Institutions and Risks, 4(3), 124-137. https://doi.org/10.21272/fmir.4(3).124-137.2020

Zgureanu, A. (2022). The role of rpo and rto in disaster recovery planning.. https://doi.org/10.53486/9789975155663.26

Zhang, Z., Zhou, X., Zhang, X., Wang, L., & Wang, P. (2018). A model based on convolutional neural network for online transaction fraud detection. Security and Communication Networks, 2018, 1-9. https://doi.org/10.1155/2018/5680264

Zhou, H., Sun, G., Sha, F., Wang, L., Hu, J., & Gao, Y. (2021). Internet financial fraud detection based on a distributed big data approach with node2vec. Ieee Access, 9, 43378-43386. https://doi.org/10.1109/access.2021.3062467