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Charoenwong, B and Reddy, P (2022). Using forensic analytics and machine learning to detect bribe payments in regime-switching environments: Evidence from the India demonetization. PLoS ONE 17(6): e0268965.

This work cites the following items of the Benford Online Bibliography:

Badal-Valero, E, Alvarez-Jareño, JA and Pavía, JM (2018). Combining Benford's Law and machine learning to detect money laundering. An actual Spanish court case. Forensic Science International 282, pp. 24-34. DOI:10.1016/j.forsciint.2017.11.008. View Complete Reference Online information Works that this work references Works that reference this work
Debreceny, RS and Gray, GL (2010). Data mining journal entries for fraud detection: An exploratory study. International Journal of Accounting Information Systems, Vol. 11, No. 3, pp. 157–181. DOI:10.1016/j.accinf.2010.08.001. View Complete Reference Online information Works that this work references Works that reference this work
Deleanu, IS (2017). Do Countries Consistently Engage in Misinforming the International Community about Their Efforts to Combat Money Laundering? Evidence Using Benford's Law. PLoS One 12(1), p. e0169632. DOI:10.1371/journal.pone.0169632. View Complete Reference Online information Works that this work references Works that reference this work
Durtschi, C, Hillison, W and Pacini, C (2004). The effective use of Benford’s law to assist in detecting fraud in accounting data. Journal of Forensic Accounting 1524-5586/Vol. V, pp. 17-34. View Complete Reference Online information Works that this work references Works that reference this work
Fairweather, WR (2017). Sensitivity and Specificity in the Application of Benford’s Law to Explore for Potential Fraud. Journal of Forensic & Investigative Accounting 9(3), Special Issue, pp. 953-961. View Complete Reference Online information Works that this work references Works that reference this work
Krakar, Z and Žgela, M (2009). Application of Benford's Law in information systems auditing. Journal of Information and Organizational Sciences, 33(1), pp. 39-51. View Complete Reference No online information available Works that this work references Works that reference this work
Kurien, KL and Chikkamannur, AA (2019). Benford’s Law and Deep Learning Autoencoders: An approach for Fraud Detection of Credit card Transactions in Social Media. Proceedings of 4th International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT-2019), pp. 1030-1035. DOI:10.1109/RTEICT46194.2019.9016804. View Complete Reference Online information Works that this work references Works that reference this work
Larsen, JE (2017). Benford’s Law and Earnings Management Detection: The Case of REITs. Journal of Forensic & Investigative Accounting 9(2), pp. 779-790. 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
Mir, TA (2012). The leading digit distribution of the worldwide illicit financial flows. arXiv:1201.3432. DOI:10.1007/s11135-014-0147-z. View Complete Reference Online information Works that this work references Works that reference this work
Nigrini, MJ (1999). I’ve got your number. Journal of Accountancy 187(5), pp. 79-83. View Complete Reference Online information Works that this work references Works that reference this work
Nigrini, MJ and Miller, SJ (2009). Data Diagnostics Using Second-Order Tests of Benford's Law. Auditing: A Journal of Practice & Theory 28(2), pp. 305-324. DOI:10.2308/aud.2009.28.2.305 . View Complete Reference Online information Works that this work references Works that reference this work
Pericchi, LR and Torres, DA (2011). Quick anomaly detection by the Newcomb-Benford law, with applications to electoral processes data from the USA, Puerto Rico and Venezuela. Statistical Science 26(4), pp. 502-16. DOI:10.1214/09-STS296. View Complete Reference Online information Works that this work references Works that reference this work
Saville, A (2006). Using Benford's law to detect data error and fraud: an examination of companies listed on the Johannesburg Stock Exchange. South African Journal of Economic and Management Sciences 9(3), 341-354. ISSN/ISBN:1015-8812. View Complete Reference Online information Works that this work references Works that reference this work
Seow, P-S, Pan, G and Suwardy, T (2016). Data Mining Journal Entries for Fraud Detection: A Replication of Debreceny and Gray's (2010) Techniques. Journal of Forensic and Investigative Accounting 8(3), pp. 501-514. View Complete Reference Online information Works that this work references Works that reference this work