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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.

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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
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 Works that reference this work
Arslan, U, Calıyurt, KT and Kahyaoglu, SB (2024). Financial statement anomaly detection based on Benford law and Beneish model: Case of a public sector hospital. The EDP Audit, Control, and Security Newsletter 69(1), pp.69-87. DOI:10.1080/07366981.2024.2312018. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Azevedo, CdS, Gonçalves, RF, Gava, VL and Spinola, MdM (2021). A Benford’s Law based methodology for fraud detection in social welfare programs: Bolsa Familia analysis. Physica A 567, p. 125626. DOI:10.1016/j.physa.2020.125626. View Complete Reference Online information Works that this work references Works that reference this work
Branets, S (2019). Detecting money laundering with Benford’s law and machine learning . Masters Thesis, University of Tartu. 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
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. DOI:10.1371/journal.pone.0268965. View Complete Reference Online information Works that this work references No Bibliography works reference this work
da Cruz Filho, EC, Nunes, DMS and Santana, CM (2021). LEI DE BENFORD: uma análise de sua aplicabilidade na detecção de fraudes nas prestações de contas de senadores da República. Revista Brasileira de Ciências Policiais 12(6). DOI:10.31412%2Frbcp.v12i6.830. POR View Complete Reference Online information Works that this work references No Bibliography works reference this work
de Jong, J, de Bruijne, J and De Ridder, J (2020). Benford’s law in the Gaia universe. Preprint arXiv:2008.12271 [astro-ph.GA]; last accessed August 8, 2022. Published Astron. & Astrophys. 642, A205. View Complete Reference Online information Works that this work references Works that reference this work
Kauko, K (2019). Benford's law and Chinese banks' non-performing loans. Bank of Finland, Institute for Economies in Transition (BOFIT), Discussion Papers No. 25/2019. View Complete Reference Online information Works that this work references Works that reference this work
Kauko, K (2024). How to detect what drives deviations from Benford’s law? An application to bank deposit data. Empirical Economics 67, pp. 1045–1061. DOI:10.1007/s00181-024-02576-1. View Complete Reference Online information Works that this work references Works that reference this work
Le, L and Mantelaers, E (2024). Benford’s Law and Beyond: A framework for auditors. Maandblad voor Accountancy en Bedrijfseconomie 98(7), pp. 427–438. DOI:10.5117/mab.98.134061. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Long, MA, Stretesky, PB, Berry, KJ, Johnston, JE and Lynch, MJ (2023). Applying Benford's Law for Assessing the Validity of Social Science Data. Cambridge University Press. ISSN/ISBN:1009463683, 97810094. View Complete Reference Online information Works that this work references Works that reference this work
Luty, P, Petković, M and Vavrek, R (2022). Applying Benfordʼs Law on assessing the reliability of financial information in European companies from the rental and leasing sector before and after the adoption of IFRS 16. The Theoretical Journal of Accounting 46(4), pp. 51-68. ISSN/ISBN:1641-4381 . View Complete Reference Online information Works that this work references No Bibliography works reference this work
Moreau, VH (2021). Inconsistencies in Countries COVID-19 Data Revealed by Benford’s Law’. Model Assisted Statistics and Applications 16(1), pp. 73-79. DOI:10.3233/MAS-210517. View Complete Reference Online information Works that this work references Works that reference this work
Nguyen, LHT, Nguyen, TT, Le, TVN and Mai, ND (2023). Applying Benford’s Law to examine earnings management: Evidence from emerging ASEAN-5 countries. Journal of Financial Reporting and Accounting (in press). DOI:10.1108/JFRA-10-2022-0390. View Complete Reference Online information Works that this work references Works that reference this work
Pinto, SO and Sobreiro, VA (2022). Literature review: Anomaly detection approaches on digital business financial systems. Digital Business 2(2), pp. 100038. ISSN/ISBN:2666-9544. DOI:10.1016/j.digbus.2022.100038. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Ramos, PCR (2021). Lei de Benford: uma Integração no Trabalho de Auditoria [Benford Law - An Integration in the Audit Work]. Masters thesis, Universidade de Brasília, Brasília. POR View Complete Reference Online information Works that this work references Works that reference this work
Sushkov, VM, Leonov, PY, Nadezhina, OS and Blagova, IY (2023). Integrating Data Mining Techniques for Fraud Detection in Financial Control Processes. International Journal of Technology 14(8), pp. 1675-1684. DOI:10.14716/ijtech.v14i8.6830. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Toosi, FG (2024). The Relationship Between the Distribution of Neural Network Weights and Model Accuracy: A Benford’s Law Perspective. Proceedings of International Congress on Information and Communication Technology, pp. 509–528. DOI:10.1007/978-981-97-3305-7_41. View Complete Reference Online information Works that this work references Works that reference this work
Toosi, FG (2025). Benford’s Law in Basic RNN and Long Short-Term Memory (LSTM) and their Associations. Preprint, Authorea. DOI:10.22541/au.173901291.14107238/v1. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Wang, D, Chen, F, Mao, J, Liu, N and Rong, F (2022). Are the official national data credible? Empirical evidence from statistics quality evaluation of China's coal and its downstream industries . Energy Economics, p. 106310. DOI:10.1016/j.eneco.2022.106310. View Complete Reference Online information Works that this work references Works that reference this work
Xu, X and Zeng, Z (2020). Did Alibaba Fake the Tmall “Double Eleven” Data? Evidence from Benford’s Law. In: Xu J., Duca G., Ahmed S., García Márquez F., Hajiyev A. (eds) Proceedings of the Fourteenth International Conference on Management Science and Engineering Management. ICMSEM 2020. Advances in Intelligent Systems and Computing, vol 1190. Springer, Cham. DOI:10.1007/978-3-030-49829-0_39. View Complete Reference Online information Works that this work references No Bibliography works reference this work