Benford's Law
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Benford's Law: Applications for Forensic Accounting, Auditing, and Fraud Detection
By Mark J. NigriniThe website includes the Nigrini Cycle Excel template and several Access databases with their Benford's Law queries. The tests in the book will focus your attention on the abnormal duplications, biases, and anomalies in your data.
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Benford's Law: Theory, the General Law of Relative Quantities, and Forensic Fraud Detection Applications
By Alex Ely KossovskyIt is almost a certainty that the digital structure of the vote tally in that fraudulent election was quite contrary to the Benford configuration, and thus could have been easily detected forensically. Living in a country without a ...
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Benford's Law: Applications for Forensic Accounting, Auditing, and Fraud Detection
By Mark J. NigriniThe website includes the Nigrini Cycle Excel template and several Access databases with their Benford's Law queries. The tests in the book will focus your attention on the abnormal duplications, biases, and anomalies in your data.
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Benford's Law: Theory and Applications
By Steven J. MillerEmphasizing common challenges and techniques across the disciplines, this accessible book shows how Benford’s law can serve as a productive meeting ground for researchers and practitioners in diverse fields.
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Benford's Law
By Steven J. MillerEmphasizing common challenges and techniques across the disciplines, this accessible book shows how Benford’s law can serve as a productive meeting ground for researchers and practitioners in diverse fields.
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Benford's Law: Theory, the General Law of Relative Quantities, and Forensic Fraud Detection Applications
By Alex Ely KossovskyThis book tells the story of a newly discovered and apparently mysterious digital phenomenon of Benford's Law.
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Benford's Law: Applications for Forensic Accounting, Auditing, and Fraud Detection
By Mark J. NigriniThe website includes the Nigrini Cycle Excel template and several Access databases with their Benford's Law queries. The tests in the book will focus your attention on the abnormal duplications, biases, and anomalies in your data.