1. bookVolume 53 (2020): Issue 2 (May 2020)
Journal Details
First Published
17 Oct 2008
Publication timeframe
4 times per year
access type Open Access

Fraud Prevention in the Leasing Industry Using the Kohonen Self-Organising Maps

Published Online: 08 Jun 2020
Page range: 128 - 145
Received: 11 Jul 2019
Accepted: 08 Apr 2020
Journal Details
First Published
17 Oct 2008
Publication timeframe
4 times per year

Background and Purpose: Data mining techniques are intensely used in various industries for the purpose of fraud prevention and detection. Research that focuses on the leasing industry is scarce, although frauds in the field of leasing occur rather often. First, we identify clusters of business clients in one leasing company by using the method of self-organising maps based on leasing contract attributes. Second, we compare clusters based on the presence of fraudulent clients, in order to develop fraudsters’ profiles.

Methodology: For detecting characteristics of fraudulent clients, we use a client database containing leasing contract attributes of one Croatian leasing company. In order to develop profiles of fraudulent clients, we utilise a clustering procedure with the Kohonen Self-Organizing Maps supported by Viscovery SOMine software.

Results: Five clusters were identified and labelled according to the modal values of attributes describing the leasing object and the industry in which the client operates: (i) New cars / Trade; (ii) Used trucks or tugboats / Other services; (iii) New machinery / Construction; (iv) New motors / Trade; and (v) New machinery and tractors / Agriculture.

Conclusion: Self-organising maps have proved to be a useful methodology for developing profiles of fraudulent clients in leasing companies. Companies can use our results and make additional efforts in monitoring clients from the identified industries, buying specific leasing objects. In addition, companies can apply our methodology to their own databases, in order to develop fraudster profiles for their specific purposes, and implement fraud alert mechanisms in their client database.


Abbas, O.A. (2008). Comparison between Data Clustering Algorithms. The International Arab Journal of Information Technology, 5(3), 320-325.Search in Google Scholar

Alavi, M., & Leidner, D.E. (2001). Knowledge management and knowledge management systems: Conceptual foundations and research issues. MIS Quarterly, 25(1), 107-136. http://doi.org/10.2307/325096110.2307/3250961Search in Google Scholar

Almendra, V. D., & Enachescu, D. (2013). Using Self-Organizing Maps for Fraud Prediction at Online Auction Sites. 2013 15th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, 281-288. http://doi.org/10.1109/synasc.2013.4410.1109/SYNASC.2013.44Search in Google Scholar

Bação, F., Lobo, V., & Painho, M. (2005). Self-organizing Maps as Substitutes for K-Means Clustering. Lecture Notes in Computer Science Computational Science – ICCS 2005, 3516, 476-483. http://doi.org/10.1007/11428862_6510.1007/11428862_65Search in Google Scholar

Balasupramanian, N., Ephrem, B. G., & Al-Barwani, I. S. (2017). User pattern based online fraud detection and prevention using big data analytics and self organizing maps. 2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), 691-694. http://doi.org/10.1109/icicict1.2017.834264710.1109/ICICICT1.2017.8342647Search in Google Scholar

Bănărescu, A. (2015). Detecting and Preventing Fraud with Data Analytics. Procedia Economics and Finance, 32, 1827-1836. http://doi.org/10.1016/S2212-5671(15)01485-910.1016/S2212-5671(15)01485-9Search in Google Scholar

Basel Committee on Banking Supervision. (2002). Operational Risk Data Collection Exercise. Retrieved March 28, 2019, from http://www.bis.org/bcbs/qis/oprdata.pdfSearch in Google Scholar

Boobyer, C. (2003). Leasing and Asset Finance: The Comprehensive Guide for Practitioners. London: Euromoney Books.Search in Google Scholar

Brockett, P. L., Xia, X., & Derrig, R. A. (1998). Using Kohonen’s self-organizing feature map to uncover automobile bodily injury claims fraud. Journal of Risk and Insurance, 65(2), 245-274.10.2307/253535Search in Google Scholar

Caldeira, A. M., Gassenferth, W., Machado, M. A., & Santos, D. J. (2015). Auditing Vehicles Claims Using Neural Networks. Procedia Computer Science, 55, 62-71. http://doi.org/10.1016/j.procs.2015.07.00810.1016/j.procs.2015.07.008Search in Google Scholar

Carcillo, F., Le Borgne, Y.-A., Caelen, O., Kessaci, Y., Oblé, F., & Bontempi, G. (2019). Combining unsupervised and supervised learning in credit card fraud detection. Information Sciences, In Press.http://doi.org/10.1016/j.ins.2019.05.04210.1016/j.ins.2019.05.042Search in Google Scholar

Carneiro, N., Figueira, G., & Costa, M. (2017). A data mining based system for credit-card fraud detection in e-tail. Decision Support Systems, 95, 91-101. http://doi.org/10.1016/j.dss.2017.01.00210.1016/j.dss.2017.01.002Search in Google Scholar

Chen, Y.-KJ., Liou, W.-C., Chen, Y.-M., & Wu, J.-H. (2019). Fraud detection for financial statements of business groups. International Journal of Accounting Information Systems, 32, 1-23., ISSN 1467-0895, https://doi.org/10.1016/j.accinf.2018. in Google Scholar

Chouiekh, A., & Haj, E. H. (2018). ConvNets for Fraud Detection analysis. Procedia Computer Science, 127, 133-138. http://doi.org/10.1016/j.procs.2018.01.10710.1016/j.procs.2018.01.107Search in Google Scholar

Dorronsoro, J., Ginel, F., Sánchez, C., & Cruz, C. (1997). Neural fraud detection in credit card operations. IEEE Transactions on Neural Networks, 8(4), 827-834. http://doi.org/10.1109/72.59587910.1109/72.595879Search in Google Scholar

Eshghi, A., & Kargari, M. (2019). Introducing a new method for the fusion of fraud evidence in banking transactions with regards to uncertainty. Expert Systems with Applications, 121, 382–392. http://doi.org/10.1016/j.eswa.2018.11.03910.1016/j.eswa.2018.11.039Search in Google Scholar

European Commission. (2011). EU Accounting Rule 8 Leases. Retrieved April 5, 2019, from https://ec.europa.eu/info/sites/info/files/about_the_european_commission/eu_budget/eu-accounting-rule-8-leases_2011_en.pdfSearch in Google Scholar

Fiore, U., Santis, A. D., Perla, F., Zanetti, P., & Palmieri, F. (2019). Using generative adversarial networks for improving classification effectiveness in credit card fraud detection. Information Sciences, 479, 448–455. http://doi.org/10.1016/j.ins.2017.12.03010.1016/j.ins.2017.12.030Search in Google Scholar

Flath, D. (1980). The economics of short-term leasing. Economic inquiry, 18(2), 247-259.10.1111/j.1465-7295.1980.tb00573.xSearch in Google Scholar

Folorunso, O., & Ogunde, A. (2005). Data mining as a technique for knowledge management in business process redesign. Information Management & Computer Security, 13(4), 274-280. http://doi.org/10.1108/0968522051061440710.1108/09685220510614407Search in Google Scholar

Hainaut, D. (2019). A self-organizing predictive map for non-life insurance. European Actuarial Journal, 9(1), 173-207.10.1007/s13385-018-0189-zSearch in Google Scholar

Holmbom, A. H., Eklund, T., & Back, B. (2011). Customer portfolio analysis using the SOM. International Journal of Business Information Systems, 8(4), 396-412. http://doi.org/10.1504/ijbis.2011.04239710.1504/IJBIS.2011.042397Search in Google Scholar

Horvat, I., Pejić Bach, M., & Merkač Skok, M. (2014). Decision tree approach to discovering fraud in leasing agreements. Business Systems Research Journal, 5(2), 61-71. http://doi.org/10.2478/bsrj-2014-001010.2478/bsrj-2014-0010Search in Google Scholar

Jian, L., Ruicheng, Y., & Rongrong, G. (2016). Self-organizing map method for fraudulent financial data detection. In 2016 3rd International Conference on Information Science and Control Engineering (ICISCE) (pp. 607-610). http://doi.org/10.1109/ICISCE.2016.13510.1109/ICISCE.2016.135Search in Google Scholar

Leite, R. A., Gschwandtner, T., Miksch, S., Gstrein, E., & Kuntner, J. (2018). Visual analytics for event detection: Focusing on fraud. Visual Informatics, 2(4), 198-212. http://doi.org/10.1016/j.visinf.2018.11.00110.1016/j.visinf.2018.11.001Search in Google Scholar

Lucas, Y., Portier, P.-E., Laporte, L., He-Guelton, L., Caelen, O., Granitzer, M., & Calabretto, S. (2020). Towards automated feature engineering for credit card fraud detection using multi-perspective HMMs. Future Generation Computer Systems, 102, 393–402. http://doi.org/10.1016/j.future.2019.08.029 202010.1016/j.future.2019.08.029Search in Google Scholar

Merkevicius, E., Garšva, G., & Simutis, R. (2004). Forecasting of credit classes with the self-organizing maps. Information Technology and Control, 33(4), 61-66. Retrieved March 25, 2019, from http://itc.ktu.lt/index.php/ITC/article/view/11956Search in Google Scholar

Moradi, M., Salehi, M., Ghorgani, M. E., & Yazdi, H. S. (2013). Financial distress prediction of Iranian companies using data mining techniques. Organizacija, 46(1), 20-27. http://dx.doi.org/10.2478/orga-2013-000310.2478/orga-2013-0003Search in Google Scholar

Nami, S., & Shajari, M. (2018). Cost-sensitive payment card fraud detection based on dynamic random forest and k -nearest neighbors. Expert Systems with Applications, 110, 381-392. http://doi.org/10.1016/j.eswa.2018.06.01110.1016/j.eswa.2018.06.011Search in Google Scholar

Ngai, E., Hu, Y., Wong, Y., 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. http://dx.doi.org/10.1016/j.dss.2010.08.00610.1016/j.dss.2010.08.006Search in Google Scholar

Nian, K., Zhang, H., Tayal, A., Coleman, T., & Li, Y. (2016). Auto insurance fraud detection using unsupervised spectral ranking for anomaly. The Journal of Finance and Data Science, 2(1), 58-75. http://doi.org/10.1016/j.jfds.2016.03.00110.1016/j.jfds.2016.03.001Search in Google Scholar

Olszewski, D. (2014). Fraud detection using self-organizing map visualizing the user profiles. Knowledge-Based Systems, 70, 324-334. http://doi.org/10.1016/j.knosys.2014.07.00810.1016/j.knosys.2014.07.008Search in Google Scholar

Osei-Bryson, K. M. (2010). Towards supporting expert evaluation of clustering results using a data mining process model. Information Sciences, 180(3), 414-431.10.1016/j.ins.2009.09.019Search in Google Scholar

Patel, R., & Singh, D. (2013). Credit Card Fraud Detection & Prevention of Fraud Using Genetic Algorithm. International Journal of Soft Computing, 6. Retrieved March 25, 2019, from http://www.ijsce.org/attachments/File/v2i6/F1189112612.pdfSearch in Google Scholar

Patil, S., Nemade, V., & Soni, P. K. (2018). Predictive Modelling For Credit Card Fraud Detection Using Data Analytics. Procedia Computer Science, 132, 385-395. http://doi.org/10.1016/j.procs.2018.05.19910.1016/j.procs.2018.05.199Search in Google Scholar

Pejić Bach, M., Juković, S., Dumičić, K., & Šarlija, N. (2014). Business Client Segmentation in Banking Using Self-Organizing Maps. South East European Journal of Economics and Business, 8(2), 32-41. http://doi.org/10.2478/jeb-2013-000710.2478/jeb-2013-0007Search in Google Scholar

Pejić Bach, M., Vlahović, N, & Pivar, J. (2018). Self-organizing maps for fraud profiling in leasing. 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, 2018, 1203-1208. http://doi.org/10.23919/MIPRO.2018.840021810.23919/MIPRO.2018.8400218Search in Google Scholar

Quah, J. T., & Sriganesh, M. (2008). Real-time credit card fraud detection using computational intelligence. Expert Systems with Applications, 35(4), 1721-1732. http://doi.org/10.1016/j.eswa.2007.08.09310.1016/j.eswa.2007.08.093Search in Google Scholar

Rousseeuw, P., Perrotta, D., Riani, M., & Hubert, M. (2019). Robust Monitoring of Time Series with Application to Fraud Detection. Econometrics and Statistics, 9, 108-121. http://doi.org/10.1016/j.ecosta.2018.05.00110.1016/j.ecosta.2018.05.001Search in Google Scholar

Ryman-Tubb, N. F., Krause, P., & Garn, W. (2018). How Artificial Intelligence and machine learning research impacts payment card fraud detection: A survey and industry benchmark. Engineering Applications of Artificial Intelligence, 76, 130-157. http://doi.org/10.1016/j.engappai.2018.07.00810.1016/j.engappai.2018.07.008Search in Google Scholar

Sadgali, I., Sael, N., & Benabbou, F. (2019). Performance of machine learning techniques in the detection of financial frauds. Procedia Computer Science, 148, 45-54. http://doi.org/10.1016/j.procs.2019.01.00710.1016/j.procs.2019.01.007Search in Google Scholar

Singleton, T. W., & Singleton, A. J. (2007). Why don’t we detect more fraud? Journal of Corporate Accounting & Finance, 18(4), 7-10.10.1002/jcaf.20302Search in Google Scholar

Šubelj, L., Furlan, Š, & Bajec, M. (2011). An expert system for detecting automobile insurance fraud using social network analysis. Expert Systems with Applications, 38(1), 1039-1052. http://doi.org/10.1016/j.eswa.2010.07.14310.1016/j.eswa.2010.07.143Search in Google Scholar

Subudhi, S., & Panigrahi, S. (2017). Use of optimized Fuzzy C-Means clustering and supervised classifiers for automobile insurance fraud detection. Journal of King Saud University - Computer and Information Sciences, In press. http://doi.org/10.1016/j.jksuci.2017.09.01010.1016/j.jksuci.2017.09.010Search in Google Scholar

Tu, B., He, D., Shang, Y., Zhou, C., & Li, W. (2019). Deep feature representation for anti-fraud system. Journal of Visual Communication and Image Representation, 59, 253–256. http://doi.org/10.1016/j.jvcir.2019.01.03110.1016/j.jvcir.2019.01.031Search in Google Scholar

Uribe, C., & Isaza, C. (2012). Expert knowledge-guided feature selection for data-based industrial process monitoring. Revista Facultad De Ingeniería Universidad De Antioquia, 65, 112-125. Retrieved March 25, 2019, from http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0120-62302012000400009Search in Google Scholar

Urueña López, A., Mateo, F., Navío-Marco, J., Martínez-Martínez, J. M., Gómez-Sanchís, J., Vila-Francés, J., & Serrano-López, A. J. (2019). Analysis of computer user behavior, security incidents and fraud using Self-Organizing Maps. Computers & Security, 83, 38-51. http://doi.org/10.1016/j.cose.2019.01.00910.1016/j.cose.2019.01.009Search in Google Scholar

Van Hulle, M.M. (2012). Self-organizing Maps. In Rozenberg G., Bäck T., & Kok J.N. (Eds.) Handbook of Natural Computing. Berlin: Springer, Heidelberg.Search in Google Scholar

Van Laerhoven K. (2001) Combining the Self-Organizing Map and K-Means Clustering for On-Line Classification of Sensor Data. In: Dorffner G., Bischof H., Hornik K. (eds) Artificial Neural Networks — ICANN 2001. ICANN 2001. Lecture Notes in Computer Science, 2130, 464–469, Springer, Berlin, Heidelberg.10.1007/3-540-44668-0_65Search in Google Scholar

Viaene, S., Dedene, G., & Derrig, R. (2005). Auto claim fraud detection using Bayesian learning neural networks. Expert Systems with Applications, 29(3), 653-666. http://doi.org/10.1016/j.eswa.2005.04.03010.1016/j.eswa.2005.04.030Search in Google Scholar

Viscovery. (2019). The Ward cluster algorithm of Viscovery SOMine. Retrieved April 5, 2019, from https://www.viscovery.net/download/public/The-SOM-Ward-cluster-algorithm.pdfSearch in Google Scholar

Wang, D., Cheng, B. & Chen, J. (2019). Credit card fraud detection strategies with consumer incentives. Omega, 88, 179-195. http://doi.org/10.1016/j.omega.2018.07.00110.1016/j.omega.2018.07.001Search in Google Scholar

Wang, H., & Wang, S. (2008). A knowledge management approach to data mining process for business intelligence. Industrial Management & Data Systems, 108(5), 622-634.10.1108/02635570810876750Search in Google Scholar

Wang, Q., Xu, W., Huang, X., & Yang, K. (2019). Enhancing intraday stock price manipulation detection by leveraging recurrent neural networks with ensemble learning. Neurocomputing, 347, 46–58. http://doi.org/10.1016/j.neucom.2019.03.00610.1016/j.neucom.2019.03.006Search in Google Scholar

Wang, Y., & Xu, W. (2018). Leveraging deep learning with LDA-based text analytics to detect automobile insurance fraud. Decision Support Systems, 105, 87–95. http://doi.org/10.1016/j.dss.2017.11.00110.1016/j.dss.2017.11.001Search in Google Scholar

Wehrens, R., & Buydens, L. (2007). Self- and Super-organizing Maps in R: The kohonen Package. Journal of Statistical Software, 21(5). Retrieved March 25, 2019, from https://www.jstatsoft.org/article/view/v021i05Search in Google Scholar

West, J., & Bhattacharya, M. (2016). Some Experimental Issues in Financial Fraud Mining. Procedia Computer Science, 80, 1734-1744. http://doi.org/10.1016/j.procs.2016.05.51510.1016/j.procs.2016.05.515Search in Google Scholar

Yan, C., Li, M., Liu, W., & Qi, M. (2020). Improved adaptive genetic algorithm for the vehicle Insurance Fraud Identification Model based on a BP Neural Network. Theoretical Computer Science, 817, 12–23. http://doi.org/10.1016/j.tcs.2019.06.02510.1016/j.tcs.2019.06.025Search in Google Scholar

Yan, C., Li, Y., Liu, W., Li, M., Chen, J., & Wang, L. (2019). An artificial bee colony-based kernel ridge regression for automobile insurance fraud identification. In press: Neurocomputing. http://doi.org/10.1016/j.neucom.2017.12.07210.1016/j.neucom.2017.12.072Search in Google Scholar

Zakaryazad, A., & Duman, E. (2016). A profit-driven Artificial Neural Network (ANN) with applications to fraud detection and direct marketing. Neurocomputing, 175, 121-131. http://doi.org/10.1016/j.neucom.2015.10.04210.1016/j.neucom.2015.10.042Search in Google Scholar

Zareapoor, M., & Shamsolmoali, P. (2015). Application of Credit Card Fraud Detection: Based on Bagging Ensemble Classifier. Procedia Computer Science, 48, 679-685. http://doi.org/10.1016/j.procs.2015.04.20110.1016/j.procs.2015.04.201Search in Google Scholar

Zaslavsky, V., & Strizhak, A. (2006). Credit Card Fraud Detection Using Self-Organizing Maps. Information & Security: An International Journal, 18, 48-63. http://doi.org/10.11610/isij.180310.11610/isij.1803Search in Google Scholar

Recommended articles from Trend MD

Plan your remote conference with Sciendo