PREDICTION OF FRAUD IN DRINKING WATER CONSUMPTION THROUGH THE USE OF DATA MINING

Abstract

The fraudulent behavior in drinking water consumption is a major problem faced by water treatment companies due to generates significant economic losses. Characterizing fraudulent drinking water consumption is a complex task, based mainly on experience, and which presents the challenge of the constant incorporation of new clients and the variation in monthly consumption. In this research, data mining techniques are used to characterize and predict fraud in the consumption of drinking water. For this, historical information on consumption was used. The techniques applied showed high predictive performance and its application will allow focusing efficiently resources oriented to avoid this type of fraud.

Keywords: data mining, machine learning, drinking water, fraud detection.

References

[1]Centro de Investigación Periodística., «Producción y facturación de agua potable,» 30 Julio 2020. [Online]. Available: https://ciperchile.cl/wp-content/uploads/gestion-siis-2014-pag 88.pdf. [Último acceso: 30 Julio 2020].

[2]Bureau Veritas S.A., «https://www.bureauveritas.cl/es,» [Online]. Available: https://www.bureauveritas.cl/es/bureau-veritas-lider-mundial-en-ensayos-inspeccion-y-certificacion. [Último acceso: 1 Junio 2020].

[3]Essbio S.A., «www.essbio.cl,» [Online].

[4]I. Monedero, F. Biscarri, J. Guerrero, M. Peña, M. Roldán and C. León, «Detection of water meter under-registration using statistical algorithms,» Journal of Water Resources Planning and Management, vol. 142, nº 1, p. 04015036, 2016.

[5]I. Monedero, F. Biscarri, C. León, J. Guerrero, J. Biscarri and R. Millán, «Detection of frauds and other non-technical losses in a power utility using Pearson coefficient, Bayesian networks and decision trees,» International Journal of Electrical Power & Energy Systems, vol. 34, nº 1, pp. 90-98, 2012.

[6]S. Wang, «A comprehensive survey of data mining-based accounting-fraud detection research,» de 2010 International Conference on Intelligent Computation Technology and Automation, New York, 2010.

[7]J. Bierstaker, R. Brody and C. Pacini, «Accountants' perceptions regarding fraud detection and prevention methods,» Managerial Auditing Journal, vol. 21, nº 5, pp. 520-535, 2006.

[8]C. Phua, V. Lee, K. Smith and R. Gayler, «A comprehensive survey of data mining-based fraud detection research,» arXiv preprint arXiv:1009.6119, 2010.

[9]S. Kotsiantis, I. Zaharakis and P. Pintelas, «Machine learning: a review of classification and combining techniques,» Artificial Intelligence Review, vol. 26, nº 3, pp. 159-190, 2006.

[10]J. Han, J. Pei and M. Kamber, Data Mining: Concepts and Techniques, Elsevier, 2011.