Albrecht, WS and Albrecht, CC (2002). Root out financial deception. Journal of Accountancy 193(4), pp. 3034. ISSN/ISBN:00218448.





Bhattacharya, P, Chatterjee, A and Chakrabarti, BK (2005). A common mode of origin of the power law distributions in models of market and earthquake. Physica A Statistical Mechanics and its Applications 381, pp. 377382. DOI:10.1016/j.physa.2007.02.096.





Bhattacharya, S, Kumar, K and Smarandache, F (2005). Conditional probability of actually detecting a financial fraud – a neutrosophic extension to Benford’s law. International Journal of Applied Mathematics 17(1), pp. 714.





Bourke, N and van Peursem, K (2004). Detecting fraudulent financial reporting: teaching the 'watchdog' new tricks. Working paper 79, University of Waikato. ISSN/ISBN:11737182.





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 15245586/Vol. V, pp. 1734.





Harrington, JE (2005). Detecting Cartels. Economics Working Paper 526, Johns Hopkins University; also in: Paolo Buccirossi (Ed.): Handbook of antitrust economics. Cambridge, Mass: MIT Press, pp. 213258 (2008).





Hill, TP (1995). A Statistical Derivation of the SignificantDigit Law. Statistical Science 10(4), pp. 354363. ISSN/ISBN:08834237.





Li, Z, Cong, L and Wang, H (2004). Discussion on Benford’s law and its application. posted on arXiv:math/0408057, Aug 4, 2004.





Lindsay, DH, Foote, PS, Campbell, A and Reilly, DP (2004). Detecting fraud in the data using automatic intervention detection. Fraud Magazine. A Publication of the Association of Certified Fraud Examiners, January/February 2004.





Murphy, J, Baxter, R, Eyerman, J, Cunningham, D and Kennet, J (2004). A system for detecting interviewer falsification. RTI International at 59th Annual AAPOR Conference, Phoenix, Arizona.





Newcomb, S (1881). Note on the frequency of use of the different digits in natural numbers. American Journal of Mathematics 4(1), pp. 3940. ISSN/ISBN:00029327. DOI:10.2307/2369148.





Petucci, SD (2005). Benford’s Law: Can It Be Used to Detect Irregularities in First Party Automobile Insurance Claims?. Journal of Economic Crime Management 3(1), pp. 135.





Posch, PN and Kreiner, WA (2005). A general approach to digital analysis exemplified by stock market indices. Online unpublished manuscript; link broken; copy available upon request.





Roukema, BF (2009). Benford's Law Anomalies in the 2009 Iranian presidential election. preprint arXiv:0906.2789.





Schäfer, C, Schräpler, JP, Müller, KR and Wagner GG (2004). Automatic Identification of Faked and Fraudulent Interviews in Surveys by Two Different Methods. Discussion paper 441, DIW Berlin (German Institute for Economic Research). ISSN/ISBN:16194535.





Smith, CA (2002). Detecting Anomalies in Your Data Using Benford’s Law. Paper 249 (Statistics and Data Analysis) in: Proceedings of SUGI 27, Orlando, USA, April 1417.





Smith, SW (1997). Explaining Benford's Law. Chapter 34 in: The Scientist and Engineer's Guide to Digital Signal Processing. California Technical Publishing: San Diego, CA. Republished in softcover by Newnes, 2002. ISSN/ISBN:0966017633.





Swanson, D, Cho, MJ and Eltinge, J (2003). Detecting possibly fraudulent or errorprone survey data using Benford’s law. pp 41724177 in: 2003 Joint Statistical Meetings  Section on Survey Research Methods, Proceedings of the American Statistical Association.





Taylor, RN, McEntegart, DJ and Stillman, EC (2002). Statistical techniques to detect fraud and other data irregularities in clinical questionnaire data. Drug Information Journal 36, 115125. DOI:10.1177/009286150203600115.




