Abstract
The National Institute of Metrology, Quality and Technology of Brazil (Inmetro), established by government decree 49/2010, has changed the requirements for vehicle safety inspections on both light and heavy vehicles that are converted to run on natural gas. In addition, according to government decree Inmetro 49/2010, the General Coordination for Accreditation of Inmetro is responsible for accrediting Brazilian vehicle safety inspection bodies. In recent years, there have been news reports, complaints, and denouncement about fraud cases in these accredited inspections, which increases the risk of accidents and environmental damage. In this paper, we propose a procedure to detect suspected fraud by an accredited vehicle safety inspection body. This tool combines clustering, digital analysis, and descriptive statistics to consider indicators of anomalous behavior and dataset object attributes in the clustering process. This mixed clustering structure links objects with similar anomaly scores together. We used descriptive statistics to identify which groups of observations were more likely to be fraudulent than others. In experiments, the proposed procedure identified unusual patterns successfully.
Similar content being viewed by others
References
Souza RP, Carmo LFRC, Boccardo DR, Pirmez L, Machado RC (2013) Redes de Kohonen para detecção de fraudes em inspeções na área de transporte de produtos perigosos. Anais do VII Congresso Brasileiro de Metrologia. http://metrologia.org.br/site/site/estaticos/arquivos/anais/Metrologia2013.zip. Accessed 01 Mar 2015
Souza RP, Carmo LFRC, Pirmez L (2014) Detecção de Dados Suspeitos de Fraude em Organismos de Inspeção Acreditados. XIV Simpósio Brasileiro em Segurança da Informação e de Sistemas Computacionais. http://www.sbseg2014.dcc.ufmg.br/files/anais.pdf. Accessed 06 Nov 2014
Downey AB (2014) Think stats: probability and statistics for programmers. Green Tea Press, Massachusetts. ISBN-10: 1449307116
Wenzel T, Brett CS, Robert S (2001) Some issues in the statistical analysis of vehicle emissions. J Transp Stat 3:1–14
Zhang Y, Bishop GA, Stedman DH (1994) Automobile emissions are statistically gamma distributed. Environ Sci Technol 28:1370–1374
Devore J (2012) Probability and statistics for engineering and the sciences. Cengage Learning, Boston. ISBN-10: 0-538-73352-7
Nigrini MJ (2012) Benford’s law applications for forensic accounting, auditing, and fraud detection. Wiley, New Jersey. ISBN: 978-1-118-15285-0
Formann AK (2010) The Newcomb-Benford law in its relation to some common distributions. PLoS One 5:e10541
Chang J, Wen-Hsi C (2014) Analysis of fraudulent behavior strategies in online auctions for detecting latent fraudsters. Electron Commer Res Appl 13:79–97
Wu X, Carlsson M (2011) Detecting data fabrication in clinical trials from cluster analysis perspective. Pharm Stat 10:257–264
Zhu S, Yan W, Yun W (2011) Health care fraud detection using nonnegative matrix factorization. In: 6th international conference on computer science and education. doi:10.1109/ICCSE.2011.6028688
Bredl S, Peter W, Kerstin K (2012) A statistical approach to detect interviewer falsification of survey data. Surv Methodol 38:1–10
ISO/IEC 17000 (2004) Conformity assessment-vocabulary and general principles. International Organization for Standardization/International Electrotechnical Commission, Geneva
Guo H, Qingyu Z, Yao S, Dahui W (2007) On-road remote sensing measurements and fuel-based motor vehicle emission inventory in Hangzhou, China. Atmos Environ 41:3095–3107
Behdad M, Barone L, Bennamoun M, French T (2012) Nature-inspired techniques in the context of fraud detection. IEEE Trans Syst Man Cybern Part C 42:1273–1290
Lakshmi BN, Raghunandhan GH (2011) A conceptual overview of data mining. In: National conference on innovations in emerging technology. doi:10.1109/NCOIET.2011.5738828
Smith CA (2001) Detecting anomalies in your data using Benford’s Law. Midwest SAS user group 2001 proceedings. www2.sas.com/proceedings/sugi27/p249-27.pdf. Accessed 01 Dec 2015
Murray G, Christopher W (2013) Fraud in clinical trials: detecting it and preventing it. Qual Control Appl Stat 58:233–234
Lan H, Eibe F, Hall MA (2011) Data mining: practical machine learning tools and techniques. Morgan Kaufmann, San Francisco. ISBN: 978-0-12-374856-0
Pal NR, James CB (1995) On cluster validity for the fuzzy c-means model. IEEE Trans Fuzzy Syst 3:370–379
He H, Yonghong T (2012) A two-stage genetic algorithm for automatic clustering. Neurocomputing 81:49–59
Chandola V, Arindam B, Vipin K (2009) Anomaly detection: a survey. ACM Comput Surv 15:1–58
Bin O (2003) A logit analysis of vehicle emissions using inspection and maintenance testing data. Transp Res Part D-Transp Environ 8:215–227
Buyse M, George SL, Evans S, Geller NL, Ranstam J, Scherrer B, Lesaffre E, Murray G, Edler L, Hutton J, Colton T, Lachenbruch P, Verma BL (1999) The role of biostatistics in the prevention, detection and treatment of fraud in clinical trials. Stat Med 18:3435–3451
Aggarwal CC (2013) Outlier analysis. Springer, New York. ISBN: 978-1-4614-6395-5
Lantz B (2013) Machine learning with R. Packt Publishing Ltd, Birmingham. ISBN: 978-1-78216-214-8
Acknowledgments
The authors would like to thank the National Institute of Metrology, Quality and Technology of Brazil for its support.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Rights and permissions
About this article
Cite this article
de Souza, R.P., Carmo, L.F.R.C. & Pirmez, L. A procedure to detect suspected patterns of fraudulent behavior in vehicle emissions tests performed by an accredited inspection body. Accred Qual Assur 21, 323–333 (2016). https://doi.org/10.1007/s00769-016-1231-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00769-016-1231-y