Evaluation and detection of fraud in the supply chain process based on the integrated approach of ANN-Big data

Subject Areas :

الناز علیخانی زنجانی ¹ , فرید عسگری ² , امیر نجفی ³ , بابک حاجی کریمی ⁴

1 - 1 دانشجوی دکتری مدیریت صنعتی_ مالی، واحد ابهر، دانشگاه آزاد اسلامی، ابهر، ایران.
2 - 2نویسنده مسئول، استادیار گروه اقتصاد مالی، واحد ابهر، دانشگاه آزاد اسلامی، ابهر، ایران. آدرس پست الکترونیکی: [e-mail]
3 - دانشیار گروه مدیریت، واحد زنجان، دانشگاه آزاد اسلامی، زنجان، ایران.
4 - استادیار گروه مدیریت، واحد زنجان، دانشگاه آزاد اسلامی، زنجان، ایران.

Received: 2022-06-30 Accepted : 2022-08-27 Published : 2022-10-22

Keywords: Big data, Keywords: ANN, Supply Chain and Evaluation and detection of fraud,

Abstract :

Abstract In recent decades, competition to provide superior value to customers has shifted from competition between companies to competition between supply chains. Appropriate design of the supply chain with regard to the economic, social and environmental dimensions at the strategic, tactical and operational levels guarantees the survival and sustainable development of enterprises active in each part of the supply chain. One of the important issues in the supply chain is the existence of fraud and the risks associated with it throughout the chain. Also, according to the amount of scientific and experimental research conducted in recent years, the problem of detecting and evaluating fraud using computational methods alone It is an important topic for research. The most important challenges of fraud detection and evaluation are the unavailability of real data sets, the existence of asymmetric data sets, the large size of data sets, the dynamic behavior of fraudsters and the dispersion of fraudulent events, which are the result of these two Uncertainty and ambiguity in decision making. Therefore, the aim of this research is to provide a model to detect and evaluate fraud in the supply chain process based on the ANN-Big data integrated approach. In terms of its purpose, this research is a developmental and applied research. First, due to the creation of a comprehensive model, taking into account the various sources of decision-making regarding the legitimacy or fraud of electronic card transactions, and the requirements of big data analysis, it tries to develop theoretical knowledge in this field, this research can be considered Considering the nature of a mathematical analytical research, on the basis of this research, in terms of approach, it is a quantitative research. The inter-industry standard process of data mining or CRISP DM (Chapman et al. 2000) has been used as the analysis methodology. Clustering models and K-means algorithms, Kohonen neural network, anomaly detection based on clustering, hidden Markov model, parallel processing method of reduction mapping and fuzzy Dempster cipher fusion model are used. The analysis was done using MATLAB and R software. The proposed model has been able to show superior performance compared to other models in terms of output and execution time.

References:

منابع
- Alshawabkeh, M., Jang, B & Kaeli, D. (2010). Accelerating the Local Outlier Factor Algorithm on a GPU for Intrusion Detection Systems .ACM, 104-110 .
-Awasthi, A & ,.Satyaveer , S. C. (2011). Using AHP and Dempster–Shafer theory for evaluating sustainable transport solutions. Environmental Modelling & Software. 787-796 .
-Bai, M., Wang, X., Xin, J & Wang, G. (2016). An efficient algorithm for distributed density-based outlier detection on big data. Neurocomputing, 19-28 .
-Bhattacharyya, S., Jha, S., Tharakunnel, K & Westland, J. C. (2011). Data mining for credit card fraud: A comparative study. Decision Support Systems, 602-613.
-Bhusari, V & ,.Patil, S. (2011). Study of Hidden Markov Model in Credit Card Fraudulent Detection. International Journal of Computer Applications.
-Bitterer, A. (2011). Hype Cycle for Business Intelligence. Gartner Inc.
-Bolton, R & ,.Hand, D. (2011). Unsupervised Profiling Methods for Fraud Detection.Credit Scoring and Credit Control.
-Boyd, D & ,.Crawford, K. (2011). Six provocations for Big Data. Symposium on the Dynamics of the Internet and Society, (1-17). London: Oxford Internet Institute.
-Brown, B., Chui, M & Manyika, J. (2011). Are you ready for the era of big data? McKinsey quarterly.
-Chaudhary , K., Yadav, J & Mallick, B. (2012). A review of Fraud Detection Techniques: Credit Card. International Journal of Computer Applications.
-Chen, H., Chiang, R. H & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS Quarterly, 1165-1188.
-Chen, R., Chen, T & Lin, C. (2006). A new binary support vector system for increasing detection rate of credit card fraud. International Journal of Pattern Recognition and Artificial Intelligence, 227-239.
-Correa Bahnsen, A., Aouada, D., Stojanovic, A & Ottersten, B. (2016). Feature engineering strategies for credit card fraud detection. Expert Systems with Applications, 134-142.
-Correa Bahnsen, A., Stojanovic, A., Aouada, D & Ottersten, B. (2013). Cost sensitive credit card fraud detection using bayes minimum risk. In Proceedings of the 2013 12th international conference on machine learning and applications, 333-338.
2 / .... .. ......... ار ز یا بی و کش ف تقل ب د ر فر ا ین د زن ج یر ه تا م ی ن مبت ن ی بر ر و یکرد تل ف ی ق ی .. . 18
-Correa Bahnsen, A., Stojanovic, A., Aouada, D & Ottersten, B. (2014). Improving credit card fraud detection with calibrated probabilities. In Proceedings of the fourteenth siam international conference on data mining, 677-685.
-Dal Pozzolo, A., Caelen, O., Le Borgne, Y. A., Waterschoot, S & Bontempi, G. (2014). Learned lessons in credit card fraud detection from a practitioner perspective. Expert Systems with Applications, 4915-4928.
-Deng, Y., Chan, T. S., Wu, Y & Wang, D. (2011). A new linguistic MCDM method based on multiple-criterion data fusion. Expert Systems with Applications, 6985-6993.
-Doan, A., Ramakrishnan, R & Halevy, A. Y. (2011). Crowd Crowdsourcing. Communication of the ACM, 86-96.
-Duman, E & ,.Ozcelik, M. H. (2011). Detecting credit card fraud by genetic algorithm and scatter search. Expert Systems with Applications, 13057-13063.
-Duru Aral, K., Güvenir, H. A., Sabuncuoglu, I & Akar, A. R. (2012). A prescription fraud detection model. Computer methods and programs in biomedicine, 37-46.
-Dymova, L., Sevastianov, P & Bart, P. (2011). A new approach to the rule-base evidential reasoning: Stock trading expert system application. Expert systems with Applications, 5564-5576.
-Epaillard, E & ,.Bouguila, N. (2016). Proportional data modeling with hidden Markov models based on generalized Dirichlet and Beta-Liouville mixtures applied to anomaly detection in public areas. Pattern Recognition, 125-136.
-European payment cards fraud report Payments Card. (2010). European payment cards fraud report Payments, Cards and Mobiles. LLP & Author.
-González, P. C & Velásquez, J. D. (2013). Characterization and detection of tax payers with false invoices using data mining techniques., Expert systemps with applications, 1427-1436.
-Graaff, A. J & Engelbrecht, A. P. (2011). The Artificial Immune System for Fraud Detection in the Telecommunications Environment.
-Gravina, R., Alinia, P., Ghasemzadeh, H & Fortino, G. (2017). Multi-sensor fusion in body sensor networks: State-of-the-art and research challenges. Information Fusion ,68-80.
-Hadavandi, E., Shahrabi, J & Hayashi, Y. (2015). SPMoE: a novel subspace-projected mixture of experts’ model for multi-target regression problems. Soft Computing, 2047-2065.
فصلنامه مطالعات کمی در مدیریت...................................................................... / 219
-Hafezi, R., Shahrabi, J & Hadavandi, E. (2015). A bat-neural network multi-agent system (BNNMAS) for stock price prediction: Case study of DAX stock price. Applied Soft Computing, 196-210.
-Halvaiee, N. S & Akbari, M. K. (2014). A novel model for credit card fraud detection using Artificial Immune Systems. Applied soft computing, 40-49.
-He, W., Williard, N., Osterman, M & Pecht, M. (2011). Prognostics of lithium-ion batteries based on Dempster–Shafer theory and the Bayesian Monte Carlo method. Journal of Power Sources, 10314-10321.
-Herodotou, H., Lim, H., Luo, G., Borisov, N., Dong, L., Cetin, F. B. (2011). Starfish: A Selftuning System for Big Data Analysis. Biennial Conference on Innovative Data Systems Research, 261-272.
-Ho, G. T., Ip, W. H., Lee, C. K & Mou, W. L. (2012). Customer grouping for better resource allocation using GA based clustering technique. Expert Systems with Applications, 1979-1987.
-Huang, J., Zhu, Q., Yang, L & Feng, J. (2016). A non-parameter outlier detection algorithm based on Natural Neighbor. Knowledge-Based Systems, 71-77.
-Huang, S. Y., Tsaih, R. H & Yu, F. (2014). Topological pattern discovery and feature extraction for fraudulent financial reporting. Expert systems with applications, 4360-4372.
-Jha, S., Guillen, M & Westland, J.C. (2012). Employing transaction aggregation strategy to detect credit card fraud. Expert Systems with Applications, 12650-12657.
-Kesselheim, A. S., Studdert, D. M & Mello, M. M. (2010). Whistle-blowers ’experiences in fraud litigation against pharmaceutical companies. New England Journal of Medicine, 1832-1839.
-Khaleghi, B., Khamis, A., Okar, F & Razavi, S. N. (2013). Multisensor data fusion: A review of the state-of-the-art. Information Fusion, 28-44.
-Khatibi, V & ,.Montazer, G. A. (2010). A fuzzy-evidential hybrid inference engine for coronary heart disease risk assessment. Expert Systems with Applications, 8536-8542.
-Khreich, W., Granger, E., Miri, A & Sabourin, R. (2010). On the memory complexity of the forward–backward algorithm. Pattern Recognition Letters, 91-99.
-Kotu, V & ,.Deshpande, B. (2015). Predictive analytics and data mining; Concepts and practice with RapidMiner. San Francisco: Morgan Kaufmann.
-Kumar, R & ,.Raj, S. (2012). Design and analisys of credit card fraud detection based on HMM. International Journal of Engineering and Innovative Technology, 332-335.
2 / .... .. ......... ار ز یا بی و کش ف تقل ب د ر فر ا ین د زن ج یر ه تا م ی ن مبت ن ی بر ر و یکرد تل ف ی ق ی .. . 20
-Kundu, A., Panigrahi, S., Sural, S & Majumdar, A. K. (2009). Credit card fraud detection: A fusion approach using Dempster-Shafer theory and Bayesian learning. Special Issue on Information Fusion in Computer Security, 354-363.
-Loshin, D. (2013). Big data analytucs; from strategic planning to enterprise integration with tools, technigues, NoSQL, and graph. Morgan Kaufmann.
-Lusch, R. F., Liu, F & Chen, Y. (2010). The Phase Transition of Markets and Organizations: The New Intelligence and Entrepreneurial Frontier. IEEE Intelligent Systems, 71-75.
-Manovich, L. (2011). Trending: The Promises and the Challenges of Big Social Data. Debates in the Digital Humanities. Minneapolis: University of Minnesota Press.
-Mansouri, T., Nabavi, A., Ravasan, A. Z & Ahangarbahan, H. (2015). A practical model for ensemble estimation of QoS and QoE in VoIP services via fuzzy inference systems and fuzzy evidence theory. Telecommunication Systems, 1-13.
-Mansouri, T., Zare Ravasan, A & Gholamian, M. R. (2014). A Novel Hybrid Algorithm Based on K-Means and Evolutionary Computations for Real Time Clustering. International Journal of Data Warehousing and Mining, 1-14.
-Metan, G., Sabuncuoglu, I & Pierreval, H. (2010). Real time selection of scheduling rules and knowledge extraction via dynamically controlled data mining. International Journal of Production Research, 6909-6938.
-Minegishi, T & ,.Niimi, A. (2011). Proposal of Credit Card Fraudulent Use Detection by Online-type Decision Tree Construction and Verification of Generality. International Journal for Information Security Research, 229-235.
-Mishra, J. S., Panda, S & Mishra, A. K. (2013). A Novel Approach for Credit Card Fraud Detection Targeting the Indian Market. International Journal of Computer Science Issues, 172-179.
-Nasiri, N & ,.Minayi, B. (2011). Data mining methods for credit card fraud detection first International conference on E-Citizen & Cellphone, Tehran, 28-29.
-Ngai, E. W., 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, 559-569.
-Olszewski, D. (2014). Fraud detection using self-organizing map visualizing the user profiles. Knowledge-Based Systems, 324-334.
-Oracle. (2012). Oracle: Big Data for the Enterprise. Oracle white paper. Oracle. (2013). Big Data Analytics; Advanced Analytics in Oracle Database.www.oracle.com.
فصلنامه مطالعات کمی در مدیریت...................................................................... / 221
-Patil, D. D., Wadhai, V. M & Gokhale, J. A. (2010). Evaluation of Decision Tree Pruning Algorithms for Complexity and Classification Accuracy. International Journal of Computer Applications, 23-30.
-Pejic-Bach, M. (2010). Profiling intelligent systems applications in fraud detection and prevention: survey of research articles. Proceedings of International Conference on Intelligent Systems: Modelling and Simulation, 80-85.
-Popescu, D. E., Lonea, M & Zmaranda, D. (2010). Some aspects about vagueness &imprecision in computer network fault-tree analysis. International Journal of Computer Commun Control, 558-566.
-Powers, D. M. (2011). Evaluation: From Precision, Recall and F-Measure to ROC Informedness, Markedness & Correlation. Journal of Machine Learning Technologies, 37-63.
-Qibei, L & ,.Chunhua, J. (2011). Research on Credit Card Fraud Detection Model Based on Class Weighted Support Vector Machine. Journal of Convergence Information Technology, 62-68.
-Raj, B. E & Portia, A. (2011). Analysis on Credit Card Fraud Detection Methods. International Conference on Computer, Communication and Electrical Technology.
-RamaKalyani, K & ,.UmaDevi, D. (2012). Fraud Detection of Credit Card Payment System by Genetic Algorithm. International Journal of Scientific & Engineering Research.
-Sahin, Y., Bulkan, S & Duman, E. (2013). A cost-sensitive decision tree approach for fraud detection. Expert Systems with Applications, 5916-5923.
-Sanjeev, J., Montserrat, G & Westland, J. C. (2012). Employing transaction aggreagation strategy to detect credit card fraud. Expert system with application, 12650-12657.
-Sawant, N & ,.Shah, H. (2013). Big data application, architecture Q&A. Apress.
-Seyedhossein, L & ,.hashemi, M. R. (2010). Mining Information from Credit Card Time Series for Timelier Fraud Detection. IEEE-5th International Symposium on Telecommunications.
-Sherly, K. K. (2012). A comparative assessment of supervised data mining techniques for fraud prevention. International Journal of Science and Technology, 1-6.
-Singh, S. P., Shukla, S. S., Rakesh, N & Tyagi, V. (2011). Problem Reduction in Online Payment System Using Hybrid Model. International Journal of Managing Information Technology.
-Tabassian, M., Ghaderi, R & Ebrahimpour, R. (2012). Combination of multiple diverse classifiers using belief functions for handling data with imperfect labels. Expert Systems with Applications, 1698-1707.
2 / .... .. ......... ار ز یا بی و کش ف تقل ب د ر فر ا ین د زن ج یر ه تا م ی ن مبت ن ی بر ر و یکرد تل ف ی ق ی .. . 22
-Tankard, C. (2012). Big data security. Network Security, 5-8.
-Tripathi, K. K & Pavaskar, M. A. (2012). Survey on Credit Card Fraud Detection Methods. International Journal of Emerging Technology and Advanced Engineering, 721-726.
-Tripathi, K. K & Ragha, L. (2013). Hybrid Approach for Credit Card Fraud Detection.
International Journal of Soft Computing and Engineering, 8-11.
-Weng, X & ,.Shen, J. (2008). Detecting outlier samples in multivariate time series dataset. Knowledge-Based Systems, 807-812.
-Weston, D. J., Hand, D. J., Adams, N. M., Whitrow, C & Juszczak, P. (2008). Plastic card fraud detection using peer group analysis. Advances in Data Analysis and Classification, 45-62.
-Witten, I. H., Frank, E & Hall, M. A. (2011). Data mining; Practical machine learning; Tools and techniques. San Francisco: Morgan Kaufmann.
-Wong, N., Ray, P., Stephens, G & Lewis, L. (2012). Artificial immune systems for the detection of credit card fraud. Info Systems.
-Wu, C. H., Tzeng, G. H., Goo, Y. J & Fang, W. C. (2007). A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy. Expert Systems with Applications, 397-408.
-Yang, J., Huang, H. Z., He, L. P., Zhu, S. P & Wen, D. (2011). Risk evaluation in failure mode and effects analysis of aircraft turbine rotor blades using using Dempster–
-Shafer evidence theory under uncertainty .Engineering Failure Analysis, 2084-2092.
-Yu, W. F & Wang, N. (2009). Research on Credit Card Fraud Detection Model Based on Distance Sum. International Joint Conference on Artificial Intelligence.
-Zareapoor, M & ,.Shamsolmoali, P. (2015). Application of credit card fraud detection: based on bagging ensemble classifier. Procedia computer science, 679-685.
-Zareapoor, M., Seeja, K. R & Alam, M. A. (2012). Analysis of Credit Card Fraud Detection Techniques: based on Certain Design Criteria. International Journal of Computer Applications, 35-42.

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