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Gepp, A, Kumar, K and Bhattacharya, S (2023). Taking the hunch out of the crunch: A framework to improve variable selection in models to detect financial statement fraud. Accounting & Finance 2023, pp.1–20..

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

Alawadhi, A, Karpoff, J, Koski, JL and Martin, GD (2023). The prevalence and price distorting effects of undetected financial misrepresentation: Empirical evidence. Preprint (first posted at SSRN in 2020). DOI:10.2139/ssrn.3532053. View Complete Reference Online information Works that this work references Works that reference this work
Amershi, AH and Feroz, EH (2000). The occurrence of Fibanocci numbers in time series of financial accounting ratios: Anomalies or indicators of firm survival, bankruptcy and fraud? An exploratory study. Managerial Finance 26(11), pp. 5-20. DOI:10.1108/03074350010766954. View Complete Reference Online information Works that this work references Works that reference this work
Amiram, D, Bozanic, Z and Rouen, E (2015). Financial statement errors: evidence from the distributional properties of financial statement numbers. Review of Accounting Studies 20(4), pp. 1540–1593. DOI:10.1007/s11142-015-9333-z. View Complete Reference Online information Works that this work references Works that reference this work
Bhattacharya, S, Xu, D and Kumar, K (2010). An ANN-based auditor decision support system using Benford's Law. Decision support systems, 50 (3), pp. 576-584. View Complete Reference Online information Works that this work references Works that reference this work
Busta, B and Weinberg, R (1998). Using Benford’s law and neural networks as a review procedure. Managerial Auditing Journal 13(6), pp. 356-366. DOI:10.1108/02686909810222375. View Complete Reference Online information Works that this work references Works that reference this work
Chakrabarthy, B, Moulton, PC, Pugachev, L and Wang, X (2019). Catch Me If You Can: Improving the Scope and Accuracy of Fraud Prediction. E-print available at SSRN: https://ssrn.com/abstract=3352667, March 13, 2019. DOI:10.2139/ssrn.3352667. View Complete Reference Online information Works that this work references Works that reference this work
Cho, WKT and Gaines, BJ (2007). Breaking the (Benford) law: Statistical fraud detection in campaign finance. American Statistician 61(3), pp. 218-223. ISSN/ISBN:0003-1305. DOI:10.1198/000313007X223496. View Complete Reference Online information Works that this work references Works that reference this work
Goodman, WM (2016). The promises and pitfalls of Benford's law. Significance 13(3) pp. 38-41. DOI:10.1111/j.1740-9713.2016.00919.x. View Complete Reference Online information Works that this work references Works that reference this work
Nigrini, MJ (2017). Audit Sampling Using Benford's Law: A Review of the Literature With Some New Perspectives. Journal of Emerging Technologies in Accounting Vol. 14, No. 2, pp. 29–46. DOI:10.2308/jeta-51783. View Complete Reference Online information Works that this work references Works that reference this work
Rad, M, Amiri, A, Ranjbar, MH and Salari, H (2021). Predictability of financial statements fraud-risk using Benford’s Law. Cogent Economics & Finance 9(1), p. 1889756 . DOI:10.1080/23322039.2021.1889756. 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
Stambaugh, C, Tipgos, MA, Carpenter, F and Smith, M (2012). Using Benford Analysis to Detect Fraud. Internal Auditing 27(3), pp. 24-29. View Complete Reference Online information Works that this work references Works that reference this work