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Horton, J, Kumar, DK and Wood, A (2020). Detecting academic fraud using Benford law: The case of Professor James Hunton. Research Policy 49(8), 104084 .

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Balashov, VS, Yan, Y and Zhu, X (2020). Who Manipulates Data During Pandemics? Evidence from Newcomb-Benford Law. Preprint arXiv:2007.14841 [econ.GN]; last accessed March 10, 2021. View Complete Reference Online information Works that this work references Works that reference this work
Balashov, VS, Yan, Y and Zhu, X (2021). Using the Newcomb–Benford law to study the association between a country’s COVID-19 reporting accuracy and its development. Scientific Reports 11, pp. 22914. DOI:10.1038/s41598-021-02367-z. View Complete Reference Online information Works that this work references Works that reference this work
Dutta, A, Choudhury, MR and De, AK (2022). A Unified Approach to Fraudulent Detection. International Journal of Applied Engineering Research 17(2), pp. 110-124. ISSN/ISBN:0973-4562. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Erfani, A, Zhang, K and Cui, Q (2021). TAB Bid Irregularity: Data-Driven Model and Its Application. Journal of Management in Engineering 37(5), p. 04021055. DOI:10.1061/(ASCE)ME.1943-5479.0000958. View Complete Reference No online information available Works that this work references No Bibliography works reference this work
Filho, DF, Silva, L and Medeiros, H (2022). “Won’t get fooled again”: statistical fault detection in COVID-19 Latin American data. Globalization and Health 18, pp.105. DOI:10.1186/s12992-022-00899-1. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Hanci, F (2022). Application of Benford’s law in agricultural production statistics. Journal of the National Science Foundation of Sri Lanka 50 (2), pp. 387-393. DOI:10.4038/jnsfsr.v50i2.10429. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Szabo, JK, Forti, LR and Callaghan, CT (2023). Large biodiversity datasets conform to Benford's law: Implications for assessing sampling heterogeneity. Biological Conservation 280, pp. 109982. DOI:10.1016/j.biocon.2023.109982. 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 No Bibliography works reference this work