Analiza profesionalnih prevara na osnovu SWARA i MARCOS metode
DOI:
https://doi.org/10.46793/Rev25109.037LKljučne reči:
profesionalne prevare, industrija, odeljenja, SWARA, MARCOSApstrakt
U poslednje vreme, profesionalne prevare postale su izraženije, što njihovo istraživanje čini veoma izazovnim. Ova studija istražuje profesionalne prevare u različitim industrijama i visokorizičnim odeljenjima koristeći metode SWARA i MARCOS. Istraživanje je pokazalo da, prema rezultatima metode SWARA-MARCOS, pet sektora s najvećim brojem profesionalnih prevara uključuje građevinarstvo, verske, humanitarne ili socijalne službe, državnu i javnu administraciju, zdravstvenu zaštitu i obrazovanje. Profesionalne prevare su najzastupljenije u sektoru građevinarstva, dok ih u informacionoj industriji ima najmanje. U maloprodaji je stopa profesionalnih prevara relativno niska. Ključno pitanje je kako suzbiti ili minimizirati profesionalne prevare u različitim sektorima. To se može postići efikasnijom kontrolom naplata, krađe gotovine, isplata gotovine na ruke, neovlašćenih izmena čekova i plaćanja, korupcije, lažnih nadoknada troškova, prevara u finansijskim izveštajima, bezgotovinskih plaćanja, zloupotreba pri obračunu plata, registrovanih plaćanja i prisvajanja novca (skimming). Spoljne revizije, interne revizije, državne revizije, kao i forenzičko računovodstvo i revizija, igraju značajnu ulogu u sprečavanju prevara. Pored toga, digitalizacija celokupnog poslovanja ima pozitivan efekat na suzbijanje profesionalnih prevara. Neophodno je razvijati etičke vrednosti među svim učesnicima u lancu vrednosti. Što se tiče profesionalnih prevara u visokorizičnim odeljenjima, prema rezultatima metode SWARA-MARCOS, najveći nivo profesionalnih prevara prisutan je u računovodstvu, zatim u finansijama. Ostala pogođena odeljenja uključuju izvršno/starije rukovodstvo, administrativnu podršku, nabavku, operacije, korisničku službu i prodaju. Profesionalne prevare su najmanje zastupljene u sektoru prodaje. Efikasna kontrola računovodstvenih procesa i finansijskih aktivnosti može značajno smanjiti profesionalne prevare. Spoljna revizija, interna revizija, forenzičko računovodstvo i revizija imaju ključnu ulogu u tom procesu. Takođe, uticaj digitalizacije računovodstvenih i finansijskih operacija je značajan. Pored toga, održavanje visokih etičkih standarda u računovodstvu i finansijama od suštinskog je značaja.
Reference
Adnan, N. S., Halmi, S. A. H., Nasir, N. E. M., & Ahmad, S. (2024). Forensic Accounting: Exploration of
Trends and Theme via Bibliometric Analysis. Advances in Social Sciences Research Journal, 11(9.2), 13–28.
https://doi.org/10.14738/assrj.119.2.17405
Aggrey, E., Baffoe, I. K., Adomako, F., Gideon, Y. B., & Amoah, B. D. (2024). The Role of Artificial Intelligence in Banking and Fraud Prevention: A Cross-Sectional Study in Ghana. Asian Journal of Research in
Computer Science, 17(8), 116–124. https://doi.org/10.9734/ajrcos/2024/v17i8494
Arman, Hj. Ahmad, Ridzuan Masri, Chang Mui Zeh, Mohd. Farid Shamsudin, Rizal Ula Ananta Fauzi.
(2020). The Impact of Digitalization on Occupational Fraud Opportunity in Telecommunication Industry:
A Strategic Review. PalArch’s Journal of Archaeology of Egypt / Egyptology, 17(9), 1308 - 1326. Retrieved
from https://archives.palarch.nl/index.php/jae/article/view/3755
Bader, A. A., Abu Hajar, Y. A., Weshah, S. R. S., & Almasri, B. K. (2024). Predicting Risk of and Motives
behind Fraud in Financial Statements of Jordanian Industrial Firms Using Hexagon Theory. Journal of
Risk and Financial Management, 17(3), 120. https://doi.org/10.3390/jrfm17030120
Beemamol, M. (2023). Occupational Fraud in the Highly Regulated Banking Industry: The Case of India.
In A. Rafay (Ed.), Concepts and Cases of Illicit Finance (pp. 175-203). IGI Global Scientific Publishing.
https://doi.org/10.4018/978-1-6684-8587-3.ch010
Bonrath, Annika; Eulerich, Marc (2024). Internal auditing’s role in preventing and detecting fraud: An
empirical analysis. International Journal of Auditing, ISSN 1099-1123, Vol. 28, Iss. 4, pp. 615-631, https://
doi.org/10.1111/ijau.12342
Demir, G., Chatterjee, P., Kadry, S., Abdelhadi, A., & Pamučar, D. (2024). Measurement of Alternatives
and Ranking according to Compromise Solution (MARCOS) Method: A Comprehensive Bibliometric
Analysis. Decision Making: Applications in Management and Engineering, 7(2), 313–336. https://doi.
org/10.31181/dmame7220241137
Đalić, I., Stević, Ž., Erceg, Ž., Macura, P., & Terzić, S. (2020). Selection of a distribution channel using
the integrated FUCOM-MARCOS model. International Review, 3-4, 80-96. https://doi.org/10.5937/
intrev2003080Q
Ezeji, C. L. (2024). Artificial Intelligence for detecting and preventing procurement fraud. International
Journal of Business Ecosystem & Strategy (2687-2293), 6(1), 63–73. https://doi.org/10.36096/ijbes.v6i1.477
Garba, A. (2024). Impact of forensic accounting on fraud detection in Nigerian deposit money banks. Journal of Advance Research in Business, Management and Accounting (ISSN: 2456-3544), 10(2), 16-30. https://
doi.org/10.61841/pm035a38
Guellim, N., Yami, N., Freihat, A.F. et al.(2024). Evaluating the perceived value of forensic accounting: a
systematic review method. Discov Sustain, 5, 351. https://doi.org/10.1007/s43621-024-00431-y
Ismail, M. M., & Haq, M. A. (2024). Enhancing Enterprise Financial Fraud Detection Using Machine Learning. Engineering. Technology & Applied Science Research, 14(4), 14854–14861. https://doi.org/10.48084/
etasr.7437
Junaidi, Hendrian, & Syahputra, B. E. (2024). Fraud detection in public sector institutions: an empirical
study in Indonesia. Cogent Business & Management, 11(1). https://doi.org/10.1080/23311975.2024.2404479
Keršulienė, V., Zavadskas, E. K., & Turskis, Z. (2010). Selection of rational dispute resolution method by
applying new step‐wise weight assessment ratio analysis (SWARA). Journal of Business Economics and
Management, 11(2), 243-258. https://doi.org/10.3846/jbem.2010.12
Kirkos, Efstathios and Boskou, Georgia and Chatzipetrou, Evrikleia and Tiakas, Eleftherios and Spathis,
Charalampos, Exploring the Boundaries of Financial Statement Fraud Detection with Large Language
Models (May 27, 2024). Available at SSRN: https://ssrn.com/abstract=4842962 or http://dx.doi.org/10.2139/
ssrn.4842962
Kovač, M., Tadić, S., Krstić, M. & Bouarima, M.B. (2021). Novel Spherical Fuzzy MARCOS Method for
Assessment of Drone-Based City Logistics Concepts. WILEY Hindawi Complexity Volume 2021, Article
ID 2374955, 17 pages. https://doi.org/10.1155/2021/2374955
Maulidi, A. & Ansell, J. (2020. Tackling practical issues in fraud control: A practice-based study. Journal
of Financial Crime, vol. N/A, pp. 1-28. https://doi.org/10.1108/JFC-07-2020-0150
Mehdipour, F., Babenkov,E., Hewage, U.H.W.A & Aharari, A. (2023). Banking Fraud Identification and
Prevention. 2023 27th International Conference on Circuits, Systems, Communications and Computers
(CSCC), Rhodes (Rodos) Island, Greece, 2023, pp. 1-6, doi: 10.1109/CSCC58962.2023.00019.
Miškić S, Stević, Ž, Tanackov, I. (2021). A novel integrated SWARA-MARCOS model for inventory classification. IJIEPR., 32 (4), 1-17. URL: http://ijiepr.iust.ac.ir/article-1-1243-en.html
Nedeljković, M, Puška, A, Doljanica, S, Virijević Jovanović, S, Brzaković, P, Stević, Ž, et al. (2021). Evaluation
of rapeseed varieties using novel integrated fuzzy PIPRECIA – Fuzzy MABAC model. PLoS ONE, 16(2):
e0246857. https://doi.org/10.1371/journal.pone.0246857
Nuswantara, D.A., Maulidi, A. & Pujiono (2017).The efficacy of control environment as fraud deterrence
in local government. Management & Marketing. Challenges for the Knowledge Society, 12(42), 591-613.
DOI: 10.1515/mmcks-2017-0035.
Ponnusamy, S., & Rajkumar, P. (2024). Enhancing Fraud Detection Systems through Advanced Data Engineering Techniques. Asian Journal of Research in Computer Science, 17(11), 46–64. https://doi.org/10.9734/
ajrcos/2024/v17i11518
Puška, A. , Stević, Ž., Stojanović, I. (2021). Selection of Sustainable Suppliers Using the Fuzzy MARCOS
Method. Current Chinese Science, 1(2), 218-229. https://dx.doi.org/10.2174/2210298101999201109214028
Rachma Archanti, Amelia, Rohman, Abdul (2024). Addressing The Factors Causing Financial Statement
Fraud: A Systematic Literature Review And Bibliometric Analysis. Journal Eduvest, 4 (6), 5487-5499
Said, J., Asri, S., Rafidi, M., Obaid, R.R., & Alam, M.M. (2018). Integrating Religiosity into Fraud Triangle
Theory: Empirical Findings From Enforcement Officer. Global Journal al Thaqafah, Special Issue: 131- 143.
(online) http://www.gjat.my/gjat2018si/SI2018-09.pdf
Śmiałek-Liszczyńska, P. (2023). On Reducing Occupational Fraud Risk in SMEs: Recommendations. Krakow
Review of Economics and Management Zeszyty Naukowe Uniwersytetu Ekonomicznego W Krakowie, 1(999),
-90. https://doi.org/10.15678/ZNUEK.2023.0999.0105
Stanković, M., Stević, Ž., Das, D.K., Subotić, M. & Pamučar, D. (2020). New Fazzy MARCOS Method for
Road Traffic Risk Analysis. Mathematics, MDPI,8, 457, 181-198.
Stanujkic, D., Karabasevic, D., Zavadskas, E.K.(2015). A framework for the Selection of a packaging design
based on the SWARA method. Inz. Ekon.-Eng. Econ., 26, 181–187.
Stanujkić, D., Karabašević, D., Popović, G., Stanimirović, P.S., Saračević, M., Smarandache, F., Katsikis,
V.N., Ulutas, A. (2021). A New Grey Approach for Using SWARA and PIPRECIA Methods in a Group Decision-Making Environment. Mathematics, 9, 1554. https:// doi.org/10.3390/math9131554
Stević, Ž., Pamučar, D., Puška, A. and Chatterjee, P. (2020a). Sustainable supplier selection in healthcare
industries using a new MCDM method: Measurement of alternatives and ranking according to COmpromise solution (MARCOS). Computers & Industrial Engineering, 140, 106231. https://doi.org/10.1016/j.
cie.2019.106231.
Stević, Ž., Brković, N. A. (2020). Novel Integrated FUCOM-MARCOS Model for Evaluation of Human
Resources in a Transport Company. Logistics, 4, 4. https://doi.org/10.3390/logistics4010004
Syamkumar, K., Sridevi, J., Ashraff, N., & Kavitha, K. S. (2024). Causes and effects and prevention of insurance
fraud: A systematic literature review. Seybold Report Journal, 19(06), 106-122. DOI: 10.5110/77. 1610
Taher, M., Guermah, H. and Nassar, M. (2020). MCDM method for Financial Fraud Detection: A review. In
Proceedings of the 4th International Conference on Big Data and Internet of Things (BDIoT ‘19). Association
for Computing Machinery, New York, NY, USA, Article 11, 1–8. https://doi.org/10.1145/3372938.3372949
Thar, K.W. and Wai, T.T. (2024). A Predictive Analytics Framework for Fraud Detection Using Efficient
Resampling Based on Hybrid Ensemble Machine Learning. 5th International Conference on Advanced
Information Technologies (ICAIT), Yangon, Myanmar, 2024, pp. 1-6, doi: 10.1109/ICAIT65209.2024.10754927.
Trung, Do Duc. (2021). Application of EDAS, MARCOS, TOPSIS, MOORA, and PIV Methods for Multi-Criteria Decision Making in Milling Process. Strojnícky časopis - Journal of Mechanical Engineering, 71(2),
-84. https://doi.org/10.2478/scjme-2021-0019
Umanhonlen. O.F. (2024). Determinants of Occupational Fraud amongst Small and Medium Scale
Enterprises (SMEs) in Edo State, Nigeria. International Journal of Small Business and Entrepreneurship
Research, 12(5), 1-70.
Zhang, R., Cheng, Y., Wang, L., Sang, N., & Xu, J. (2023). Efficient Bank Fraud Detection with Machine
Learning. Journal of Computational Methods in Engineering Applications, 3(1), 1–10. https://doi.org/10.62836/
jcmea.v3i1.030102