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Reseach Article

Road Construction Fraud Detection System using Fuzzy Logic

by Emmanuel O. Atomatofa, Eli Adama Jiya, Johnson Akpa
International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 12 - Number 40
Year of Publication: 2023
Authors: Emmanuel O. Atomatofa, Eli Adama Jiya, Johnson Akpa
10.5120/ijais2023451937

Emmanuel O. Atomatofa, Eli Adama Jiya, Johnson Akpa . Road Construction Fraud Detection System using Fuzzy Logic. International Journal of Applied Information Systems. 12, 40 ( February 2023), 1-7. DOI=10.5120/ijais2023451937

@article{ 10.5120/ijais2023451937,
author = { Emmanuel O. Atomatofa, Eli Adama Jiya, Johnson Akpa },
title = { Road Construction Fraud Detection System using Fuzzy Logic },
journal = { International Journal of Applied Information Systems },
issue_date = { February 2023 },
volume = { 12 },
number = { 40 },
month = { February },
year = { 2023 },
issn = { 2249-0868 },
pages = { 1-7 },
numpages = {9},
url = { https://www.ijais.org/archives/volume12/number40/1132-2023451937/ },
doi = { 10.5120/ijais2023451937 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T19:11:35.527065+05:30
%A Emmanuel O. Atomatofa
%A Eli Adama Jiya
%A Johnson Akpa
%T Road Construction Fraud Detection System using Fuzzy Logic
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 12
%N 40
%P 1-7
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

All over the world, fraud poses a serious threat to both the developing and developed economies, this is generally due to the large amount of resource it illegal take away from the state and advanced nature of technology which aids the scheme. Nigeria is not an exception in fraud and financial crime-related cases, however, road construction and infrastructural development related frauds are rarely checked. Through these frauds, large state resources is diverted. Using World Bank benchmark for road construction in Africa, this paper designed a Road Construction Fraud Detection System Using Fuzzy Logic. Contract cost, environment factors, and other contract details were compared against the standard benchmark of contract sum in such areas. Also fuzzy rules were used to determine whether a contract is fraudulent or not. This work would show that contract inflations and fraud in road construction can be detected and minimized with a good fraud detection system.

References
  1. Osegi, E. N., & Jumbo, E. F. (2021). Comparative analysis of credit card fraud detection in Simulated Annealing trained Artificial Neural Network and Hierarchical Temporal Memory. Machine Learning with Applications, 6(October 2020), 1–10. https://doi.org/10.1016/j.mlwa.2021.100080
  2. Adekunle, O., & Olusa, A. (2021). The Impact of Fraudulent Practices on Infrastructural Development in Nigeria. Journal of Economics and Sustainable Development, 12(6), 81–89. https://doi.org/10.7176/JESD/12-6-08
  3. Rodrigo, A (2000), World Bank Reports, Roads Works Costs per Km., http://www.worldbank.org/transport/roads/c&m_docs/kmcosts.pdf on 25/2/2016
  4. Mayowaak A.(2014): Nigerian Roads, most Expensive in Africa: it’s Between N400m ToN1bn Per Kilometre. Retrieved from https://www.nairaland.com/1898876/nigerian-roadsmost-expensive-africa-n400m accessed on 04/10/2022.
  5. Leondes, C. T. (Ed.). (1998). Fuzzy Logic and Expert system applications. In Fuzzy Logic and Expert system applications (p. 437). ACADEMIC PRESS.
  6. Chen, G., & Pham, T. T. (2001). Introduction to fuzzy sets, fuzzy logic, and fuzzy control systems. CRC Press LLC.
  7. Gang, F. (2010). Anaysis and Synthesis of Fuzzy Control System: A model-based approach. CRC Press.
  8. Saeed, S. K., & Hagras, H. (2019). A Fraud-Detection Fuzzy Logic Based System for the Sudanese Financial Sector. SUST Journal of Engineering and Computer Science (JECS), 20(1), 17–30.
  9. azooqi, T., & Khurana, P. (2016). Credit Card Fraud Detection Using Fuzzy Logic and Neural Network. Proceedings of the 19th Communications & Networking Symposium, 1–5.
  10. Marah, H. M., Elrajubi, O. M., & Abouda, A. A. (2015). Fraud Detection in International Calls Using Fuzzy Logic. International Conference on Computer Vision and Image Analysis Applications, 1–6.
  11. Khan, M. Z., Pathan, J. D., Haider, A., & Ahmed, E. (2014). Credit Card Fraud Detection System Using Hidden Markov Model and K-Clustering. 3(2), 5458–5461.
  12. Costa, b. C., alberto, b. L. A., portela, a. M., maduro, w., eler, o., & horizonte, b. (2013). F raud d etection in e lectric p ower d istribution n etworks u sing an ANN -based k nowledge -d iscovery p rocess. 4(6), 17–23.
  13. Modi, H., Lakhani, S., Patel, N., & Patel, V. (2013). Fraud Detection in Credit Card System Using Web Mining. International Journal of Innovative Research in Computer and Communication Engineering, 1(2), 175–179.
  14. Manuel, A., Serrano, R., Paulo, J., Lustosa, C., Cardonha, C. H., Fernandes, A. A., Timóteo, R., Júnior, D. S., & Paulo, S. (2012). Neural Network Predictor for Fraud Detection : A Study Case for the Federal Patrimony Department. 1, 61–66. https://doi.org/DOI: 10.5769/C2012010 or http://dx.doi.org/10.5769/C2012010
  15. Ngai, E. W. T., Hu, Y., Wong, Y. H., Chen, Y., & Sun, X. (2011). The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decision Support Systems, 50(3), 559–569. https://doi.org/10.1016/j.dss.2010.08.006
  16. Murynets, I., Zabarankin, M., Jover, R. P., & Panagia, A. (2014). Analysis and detection of SIMbox fraud in mobility networks. In INFOCOM, 2014 Proceedings IEEE (pp. 1519–1526). https://doi.org/10.1109/INFOCOM.2014.6848087
  17. Guo, T. G. T., & Li, G.-Y. L. G.-Y. (2008). Neural data mining for credit card fraud detection. 2008 International Conference on Machine Learning and Cybernetics, 7, 1–4. https://doi.org/10.1109/ICMLC.2008.4621035
  18. Farvaresh, H., & Sepehri, M. M. (2011). A data mining framework for detecting subscription fraud in telecommunication. Engineering Applications of Artificial Intelligence, 24(1), 182–194. https://doi.org/10.1016/j.engappai.2010.05.009
Index Terms

Computer Science
Information Sciences

Keywords

Fraud detection Road construction fraud Fuzzy Logic