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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.

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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 No Bibliography works reference this work
Bannier, C, Ewelt-Knauer, C, Lips, J and Winker, P (2020). Benford’s Law and Its Application to Detecting Financial Fraud and Manipulation. Ch. 18 in: Corruption and Fraud in Financial Markets: Malpractice, Misconduct and Manipulation, C. Alexander and D. Cumming (Eds.), John Wiley & Sons: Chichester, U.K., pp. 473-504. ISSN/ISBN:978-1-119-42177-1. View Complete Reference No online information available Works that this work references No Bibliography works reference this work
Bannier, C, Ewelt-Knauer, C, Winker, P and Lips, J (2019). Benford’s law and its application to detecting financial fraud and manipulation. Chapter 5 of Econometric Modelling of Energy & Financial Markets, Doctoral Thesis of Johannes Lips, Justus-Liebig University Gießen . View Complete Reference Online information Works that this work references No Bibliography works reference this work
Costa, JI (2012). Desenvolvimento de metodologias contabilométricas aplicadas a auditoria contábil digital: uma proposta de análise da lei de Newcomb-Benford para os Tribunais de Contas. Thesis, Universidade Federal de Pernambuco, Recife, Brasil. POR View Complete Reference Online information Works that this work references Works that reference this work
Jasak, Z (2010). Benfordov zakon i reinforcement učenje (Benford's Law and reinforcment learning) . MSc Thesis, University of Tuzla, Bosnia. SRP View Complete Reference Online information Works that this work references Works that reference this work
Kappal, S (2019). Deplyoing [sic] Advance Data Analytics Techniques with Conversational Analytics Outputs for Fraud Detection . International Journal of Mathematical Sciences and Computing (IJMSC) 5(1), pp. 42-52 . DOI:10.5815/ijmsc.2019.01.04. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Li, F, Han, S, Zhang, H, Ding, J, Zhang, J and Wu, J (2019). Application of Benford’s law in Data Analysis. Journal of Physics: Conference Series 1168, pp. 032133. DOI:10.1088/1742-6596/1168/3/032133. 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
Winter, C, Schneider, M and Yannikos, Y (2011). Detecting Fraud Using Modified Benford Analysis. Advances in Digital Forensics VII, 7th IFIP WG 11.9 International Conference on Digital Forensics, Orlando, FL, USA, January 31 – February 2, 2011, Revised Selected Papers. Gilbert Peterson and Sujeet Shenoi (Editors). IFIP Advances in Information and Co. ISSN/ISBN:1868-4238. DOI:10.1007/978-3-642-24212-0_10. View Complete Reference Online information Works that this work references Works that reference this work
Winter, C, Schneider, M and Yannikos, Y (2012). Model-Based Digit Analysis for Fraud Detection overcomes Limitations of Benford Analysis. Availability, Reliability and Security (ARES 2012), Seventh International Conference, August 20–24, 2012, Prague, Czech Republic. IEEE CS volume E4775, pages 255–261. IEEE Computer Society. ISSN/ISBN:978-1-4673-2244-7 . DOI:10.1109/ARES.2012.37. View Complete Reference Online information Works that this work references Works that reference this work