Testing the Mixing Property of the Newcomb-Benford Profile: Implications for the Audit Context

Lusk, Edward J.; Halperin, Michael

doi:10.5539/ijef.v7n6p42

Cited by 2 publications

(4 citation statements)

References 12 publications

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“…1) According to the research results of Hill (1995a,b, 1996& 1998, Wallace (2002 and Lusk & Halperin (2015b), if one combines small related datasets from large Conforming datasets the act of combining such small related datasets appears to move the Benford testing-results towards Conformity from the ijef.ccsenet.org…”

Section: Protocol Indications For Conducting Studies Where Large Datamentioning

confidence: 99%

“…10, No. 2;2018 Non-Conformity that was tacitly created due to the small sample size datasets. 2) Another protocol suggested by this research report is to first run the large dataset from which one desires to form a sub-set for the creation of audit evidence.…”

Section: Protocol Indications For Conducting Studies Where Large Datamentioning

confidence: 99%

“…10, No. 2;2018 Sample size expectation order of H1. The Bernoulli test results show that all six directional movements, shaded in Table 1, are in the predicted direction and this produces a p-value of (1/2) 6 or 1.6% relative to the Null of no effect, providing a consistent inferential non-parametric confirmation of the result discussed for H1.…”

Section: Testing H2mentioning

confidence: 99%

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Empirical Information on the Small Size Effect Bias Relative to the False Positive Rejection Error for Benford Test-Screening

Bao¹,

Lee²,

Heilig³

et al. 2018

IJEF

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Due to the theoretical work of Hill Benford digital profile testing is now a staple in screening data for forensic investigations and audit examinations. Prior empirical literature indicates that Benford testing when applied to a large Benford Conforming Dataset often produces a bias called the FPE Screening Signal [FPESS] that misleads investigators into believing that the dataset is Non-Conforming in nature. Interestingly, the same FPESS can also be observed when investigators partition large datasets into smaller datasets to address a variety of auditing questions. In this study, we fill the empirical gap in the literature by investigating the sensitivity of the FPESS to partitioned datasets. We randomly selected 16 balance-sheet datasets from: China Stock Market Financial Statements Database™, that tested to be Benford Conforming noted as RBCD. We then explore how partitioning these datasets affects the FPESS by repeated randomly sampling: first 10% of the RBCD and then selecting 250 observations from the RBCD. This created two partitioned groups of 160 datasets each. The Statistical profile observed was: For the RBCD there were no indications of Non-Conformity; for the 10%-Sample there were no overall indications that Extended Procedures would be warranted; and for the 250-Sample there were a number of indications that the dataset was Non-Conforming. This demonstrated clearly that small datasets are indeed likely to create the FPESS. We offer a discussion of these results with implications for audits in the Big-Data context where the audit In-charge would find it necessary to partition the datasets of the client.

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Section: Protocol Indications For Conducting Studies Where Large Datamentioning

confidence: 99%

Section: Protocol Indications For Conducting Studies Where Large Datamentioning

confidence: 99%

Section: Testing H2mentioning

confidence: 99%

See 1 more Smart Citation

Empirical Information on the Small Size Effect Bias Relative to the False Positive Rejection Error for Benford Test-Screening

Bao¹,

Lee²,

Heilig³

et al. 2018

IJEF

Self Cite

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“…Finally, Hill (1995aHill ( , 1995bHill ( , 1996Hill ( , & 1998 is usually given credit for proving that digital profiles from large datasets which are produced from unconstrained and mixed mathematical manipulations can be expected to track along with the profile sketched out by EQ1. Also recently, Lusk & Halperin (2015b) report on the testing of the Hill mixing concepts. This had the obvious implication for DSS-screening in the execution of the audit-to wit, if the client's first digit profile for an account under audit diverges from the first digit profile offered by EQ1 then, this dataset may warrant further EPT.…”

Section: Newcomb and Benfordmentioning

confidence: 99%

A Robust Newcomb-Benford Account Screening Profiler: An Audit Decision Support System

Heilig¹,

Lusk²

2017

IJFR

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The best practices execution of the audit is conditioned by the facility with which Decision Support Systems [DSS] can be created using simple Excel™ programming tools and functionalities. Such DSS can aid in the exclusive binary triage of the many of the client's accounts each of which typically has tens of thousands of items into: {Accounts that may warrant Extended Procedures Testing [EPT]} or {Accounts that may not warrant EPT}. We use the Newcomb-Benford first-digit-profile as a triage platform to screen client accounts into the above mentioned exclusive sets.

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Testing the Mixing Property of the Newcomb-Benford Profile: Implications for the Audit Context

Cited by 2 publications

References 12 publications

Empirical Information on the Small Size Effect Bias Relative to the False Positive Rejection Error for Benford Test-Screening

Empirical Information on the Small Size Effect Bias Relative to the False Positive Rejection Error for Benford Test-Screening

A Robust Newcomb-Benford Account Screening Profiler: An Audit Decision Support System

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