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Bolton, RJ and Hand, DJ (2002). Statistical Fraud Detection: a review. Statistical Science 17(3), pp. 235-249.

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Afanasiev, S, Smirnova, A and Kotereva, D (2021). Itsy Bitsy SpiderNet: Fully Connected Residual Network for Fraud Detection. Preprint arXiv:2105.08120 [cs.LG]; last accessed May 24, 2021. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Aggarwal, V and Dharni, K (2020). Deshelling the Shell Companies Using Benford’s Law: An Emerging Market Study. Vikalpa 45(3), pp. 160-169. DOI:10.1177/0256090920979695. View Complete Reference Online information Works that this work references Works that reference this work
Agyemang, EF, Nortey, ENN, Minkah, R and Asah-Asante, K (2023). The unfolding mystery of the numbers: First and second digits based comparative tests and its application to ghana’s elections. Model Assisted Statistics and Applications 18(2), pp. 183-192. DOI:10.3233/MAS-221418. View Complete Reference Online information Works that this work references Works that reference this work
Antunes, AM, Teixeira, D and Sousa, F (2023). Benford’s Law: the fraud detection’s left hand. Proceedings of 18th Iberian Conference on Information Systems and Technologies (CISTI), Aveiro, Portugal, pp. 1-6. DOI:10.23919/CISTI58278.2023.10211738. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Aris, NA, Othman, R, Bukhori, MAM, Arif, SMM and Malek, MAA (2017). Detecting Accounting Anomalies Using Benford’s Law: Evidence from the Malaysian Public Sector. Management & Accounting Review 16(2), pp. 73-100. View Complete Reference Online information Works that this work references Works that reference this work
Ausloos, M, Cerqueti, R and Mir, TA (2017). Data science for assessing possible tax income manipulation: The case of Italy. Chaos, Solitons and Fractals 104, pp. 238–256. DOI:10.1016/j.chaos.2017.08.012. View Complete Reference Online information Works that this work references Works that reference this work
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Barabesi, L, Cerioli, A and Perrotta, D (2021). Forum on Benford’s law and statistical methods for the detection of frauds. Statistical Methods & Applications 30, pp. 767–778. DOI:10.1007/s10260-021-00588-0. View Complete Reference Online information Works that this work references Works that reference this work
Brown, MS (2012). Does the Application of Benford's Law Reliably Identify Fraud on Election Day? . Masters thesis, Georgetown University. View Complete Reference Online information Works that this work references Works that reference this work
Burns, BD (2009). Sensitivity to statistical regularities: People (largely) follow Benford’s law. pp 2872-2877 in: Proceedings of CogSci 2009, Amsterdam, The Netherlands. View Complete Reference Online information Works that this work references Works that reference this work
Cantu, F and Saiegh, SM (2010). A Supervised Machine Learning Procedure to Detect Electoral Fraud Using Digital Analysis. Preprint posted on SSRN; last accessed August 5, 2021. DOI:10.2139/ssrn.1594406. View Complete Reference Online information Works that this work references Works that reference this work
Cerri, J (2018). A fish rots from the head down: how to use the leading digits of ecological data to detect their falsification. Preprint, bioRxiv p. 368951. DOI:10.1101/368951. View Complete Reference Online information Works that this work references Works that reference this work
Chi, D (2020). First Digit Phenomenon in Number Generation Under Uncertainty: Through the Lens of Benford’s Law. Master's thesis, School of Psychology, University of Sydney. View Complete Reference Online information Works that this work references Works that reference this work
Çubukcu, S (2009). Muhasebe Hilelerini Ortaya Çikarmada Benford Modeli'nin İlk İki Basamak Yaklaşimi İle Kullanilmasi [Using Benford Model in First Two Step Approach to Reveal Accounting Cheats]. World of Accounting Science 11(3), pp. 113-142. TUR View Complete Reference Online information Works that this work references Works that reference this work
da Silva, CG and Carreira, PMR (2019). Estimating the Proportion of Misstated Records in an Audit Data set using Benford’s Law. Journal of Accounting, Finance and Auditing Studies 5(2), pp. 146-162. DOI:10.32602/jafas.2019.25. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Dlugosz, S and Müller-Funk, U (2009). The value of the last digit: statistical fraud detection with digit analysis. Advances in Data Analysis and Classification 3, pp. 281-290. DOI:10.1007/s11634-009-0048-5. View Complete Reference Online information Works that this work references Works that reference this work
Fonseca, PMT da (2016). Digit analysis using Benford's Law: A Bayesian approach. Masters Thesis, ISEG - Instituto Superior de Economia e Gestão, Lisbon School of Economics & Management, Portugal. View Complete Reference Online information Works that this work references Works that reference this work
Gauvrit, N, Houillon, J-C and Delahaye, J-P (2017). Generalized Benford’s Law as a Lie Detector. Advances in Cognitive Psychology 13(2), pp. 121-127. DOI:10.5709/acp-0212-x. View Complete Reference Online information Works that this work references Works that reference this work
Gottwalt, F, Waller, A and Liu, W (2016). Natural Laws as a Baseline for Network Anomaly Detection. In: Proceedings of 2016 IEEE Trustcom/BigDataSE/ISPA, pp. 370-377. DOI:10.1109/TrustCom.2016.0086. View Complete Reference Online information Works that this work references Works that reference this work
Graham, SDJ, Hasseldine, J and Paton, D (2009). Statistical fraud detection in a commercial lobster fishery. New Zealand Journal of Marine and Freshwater Research Volume 43, Issue 1, pp. 457-463. DOI:10.1080/00288330909510014. View Complete Reference Online information Works that this work references Works that reference this work
Haferkorn, M (2013). Humans vs. Algorithms – Who Follows Newcomb-Benford’s Law Better with Their Order Volume?. In: Rabhi F.A., Gomber P. (eds), Enterprise Applications and Services in the Finance Industry: Lecture Notes in Business Information Processing Volume 135, pp. 61-70 . ISSN/ISBN:9783642362187. DOI:10.1007/978-3-642-36219-4_4. View Complete Reference Online information Works that this work references Works that reference this work
Hein, J, Zobrist, R, Konrad, C and Schuepfer, G (2012). Scientific fraud in 20 falsified anesthesia papers : detection using financial auditing methods. Der Anaesthesist 61(6), pp. 543-9. DOI:10.1007/s00101-012-2029-x. View Complete Reference Online information Works that this work references Works that reference this work
Huang, SM, Yen, DC, Yang, LW and Hua, JS (2008). An investigation of Zipf's Law for fraud detection. Decision Support Systems 46(1), pp. 70-83. DOI:10.1016/j.dss.2008.05.003. View Complete Reference Online information Works that this work references Works that reference this work
Hüllemann, S , Schüpfer, G and Mauch, J (2017). Application of Benford's law: a valuable tool for detecting scientific papers with fabricated data?. Der Anaesthesist vol. 66(10), pp. 795--802 . DOI:10.1007/s00101-017-0333-1. View Complete Reference Online information Works that this work references Works that reference this work
Kennedy, AP and Yam, SCP (2020). On the authenticity of COVID-19 case figures. PLoS ONE 15(12): e0243123. DOI:10.1371/journal.pone.0243123. View Complete Reference Online information Works that this work references Works that reference this work
Korauš, A, Gombár, M, Kelemen, P and Backa, S (2019). Using Quantitative Methods to Identify Security and Unusual Business Operations. Entrepreneurship And Sustainability Issues 6(3), pp.1101-1112. DOI:10.9770/jesi.2019.6.3(3). View Complete Reference Online information Works that this work references No Bibliography works reference this work
Kundt, TC (2014). Applying "Benford's law" to the Crosswise Model: Findings from an online survey on tax evasion . Helmut-Schnidt-University, Department of Economics, Working Paper, 148/2014. View Complete Reference Online information Works that this work references Works that reference this work
Lu, F (2007). Uncovering Fraud in Direct Marketing Data with a Fraud Auditing Case Builder. Lecture Notes in Computer Science 4702, pp. 540-547. ISSN/ISBN:978-3-540-74975-2. DOI:10.1007/978-3-540-74976-9_56. View Complete Reference Online information Works that this work references Works that reference this work
Lu, F and Boritz, JE (2005). Detecting Fraud in Health Insurance Data: Learning to Model Incomplete Benford’s Law Distributions. Machine Learning: ECML 2005 (Proceedings). Lecture Notes in Artificial Intelligence 3270, pp. 633-640. ISSN/ISBN:0302-9743. View Complete Reference Online information Works that this work references Works that reference this work
Lu, F, Boritz, JE and Covvey, D (2006). Adaptive Fraud Detection Using Benford’s Law. Advances in Artificial Intelligence Lecture Notes in Computer Science Volume 4013, pp. 347-358. ISSN/ISBN:978-3-540-34628-9. DOI:10.1007/11766247_30. View Complete Reference Online information Works that this work references Works that reference this work
Macías, ALO and Ogua, ST (2018). Encontrando datos anómalos en la tributación. Aplicación de la Ley de Benford en el Impuesto a la Renta en Ecuador [Finding anomalous data in taxation. Application of the Benford Law on Income Tax in Ecuador]. SaberEs 10(2), pp. 173-188. SPA View Complete Reference Online information Works that this work references Works that reference this work
Miller, SJ (ed.) (2015). Benford's Law: Theory and Applications. Princeton University Press: Princeton and Oxford. ISSN/ISBN:978-0-691-14761-1. View Complete Reference Online information Works that this work references Works that reference this work
Morzy, M, Kajdanowicz, T and Szymański, BK (2016). Benford’s Distribution in Complex Networks. Scientific Reports 6:34917. DOI:1038/srep34917. View Complete Reference Online information Works that this work references Works that reference this work
Mucko, P and Adamczyk, A (2023). Does the bankrupt cheat? Impact of accounting manipulations on the effectiveness of a bankruptcy prediction. PLoS ONE 18(1), e0280384. ISSN/ISBN:1932-6203. DOI:10.1371/journal.pone.0280384. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Nigrini, MJ (2011). Forensic Analytics: Methods and Techniques for Forensic Accounting Investigations. John Wiley & Sons: Hoboken, New Jersey; (2nd edition published in 2020, isbn 978-1-119-58576-3). ISSN/ISBN:978-0-470-89046-2. View Complete Reference Online information Works that this work references Works that reference this work
O'Keefe, J and Yom, C (2017). Offsite Detection of Insider Abuse and Bank Fraud among U.S. Failed Banks 1989-2015. Available at SSRN: https://ssrn.com/abstract=3013174. DOI:10.2139/ssrn.3013174. View Complete Reference Online information Works that this work references Works that reference this work
Otey, ME (2006). Approaches to Abnormality Detection with Constraints. PhD thesis, The Ohio State University, USA. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Scholes, CA (2023). Utilising forensic tools to assist in chemical engineering capstone assessment grading. Education for Chemical Engineers 45, pp. 61-67 . DOI:10.1016/j.ece.2023.08.001. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Schüpfer, G, Hein, J, Casutt, M, Steiner, L and Konrad, C (2012). Vom Finanz- sum Wissenschaftsbetrug [From financial to scientific fraud : methods to detect discrepancies in the medical literature]. Der Anaesthesist 61(6):537-42. ISSN/ISBN:0003-2417. DOI:10.1007/s00101-012-2028-y. GER View Complete Reference Online information Works that this work references Works that reference this work
Shahana, T, Lavanya, V and Bhat, AR (2023). State of the art in financial statement fraud detection: A systematic review. Technological Forecasting and Social Change 192, p. 122527 . DOI:10.1016/j.techfore.2023.122527. View Complete Reference Online information Works that this work references Works that reference this work
Suh, I and Headrick, TC (2010). A comparative analysis of the bootstrap versus traditional statistical procedures applied to digital analysis based on Benford's Law. Journal of Forensic and Investigative Accounting 2(2), 2010, 144-175. View Complete Reference Online information Works that this work references Works that reference this work
Tsagbey, S, de Carvalho, M and Page, GL (2017). All Data are Wrong, but Some are Useful? Advocating the Need for Data Auditing . The American Statistician, 71, pp. 231--235. DOI:10.1080/00031305.2017.1311282. View Complete Reference Online information Works that this work references Works that reference this work
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