IDEAS home Printed from https://ideas.repec.org/a/ami/journl/v19y2020i2p379-404.html
   My bibliography  Save this article

Implementing Benford’s Law in Continuous Monitoring Applications

Author

Listed:
  • Kishore Singh
  • Peter Best

    (Central Queensland University, Australia)

Abstract

Research Question: Do modern ERP systems record sufficient information to allow retrospective monitoring of accounts payable transactions? Can Benford’s Law be applied to these transactions to detect potential fraud in accounting data? Motivation: Modern ERP systems are capable of recording several thousands of transactions daily. This makes it difficult to find a few instances of anomalous activities among legitimate transactions. As organizations continue to become more complex and demand more integrated business processes, automated analytics may provide auditors and fraud examiners some degree of assurance on continuous information simultaneously with, or shortly after disclosure of information. Idea: In this study we develop a proof of concept prototype to monitor invoice transactions and identify those that violate Benford’s law. The prototype exploits audit trails in enterprise systems. Data: Data was obtained from the SAP ERP systems of two large organizations. Organization 1, a government department, provided a one month sample of accounting transaction data. Organization 2, a global manufacturing company, provided a six month sample of their transaction data. Tools: Verification and validation was achieved by obtaining independent reviews from an expert and a panel of auditing practitioners. Their feedback was sought using a survey instrument where they rated various aspects of the prototype software. Findings: A key aim was to demonstrate the feasibility of implementing Benford’s analysis in continuous monitoring applications by exploiting audit trails in ERP systems. The concept was demonstrated by designing prototype software. We found that Benford’s analysis, is a useful tool for identifying suspicious transactions. These transactions may contain possible errors, potential fraud or other irregularities. Contribution: An important contribution of the study is that the entire population of transactions for a specified time period are analyzed. This approach is in contrast with the traditional or manual audit approach which is limited because it reviews only a small percentage of a large population of transactions. The prototype demonstrates the application of technology and data analytics to process transaction data from a SAP ERP system in a near real-time basis. This represents the next step in the evolution of the financial audit from manual to automated methods.

Suggested Citation

  • Kishore Singh & Peter Best, 2020. "Implementing Benford’s Law in Continuous Monitoring Applications," Journal of Accounting and Management Information Systems, Faculty of Accounting and Management Information Systems, The Bucharest University of Economic Studies, vol. 19(2), pages 379-404, June.
    • Handle: RePEc:ami:journl:v:19:y:2020:i:2:p:379-404
    as

    Download full text from publisher

    File URL: http://online-cig.ase.ro/RePEc/ami/articles/19_2_8.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tam Cho, Wendy K. & Gaines, Brian J., 2007. "Breaking the (Benford) Law: Statistical Fraud Detection in Campaign Finance," The American Statistician, American Statistical Association, vol. 61, pages 218-223, August.
    2. Vasarhelyi, Miklos A. & Alles, Michael & Kuenkaikaew, Siripan & Littley, James, 2012. "The acceptance and adoption of continuous auditing by internal auditors: A micro analysis," International Journal of Accounting Information Systems, Elsevier, vol. 13(3), pages 267-281.
    3. Adrian Cojocariu & Alin Munteanu & Ofelia Sofran, 2005. "Verification, Validation and Evaluation of Expert Systems in Order to Develop a Safe Support in the Process of Decision Making," Computational Economics 0510002, University Library of Munich, Germany.
    4. Sinisa Slijepcevic & Branimir Blaskovic, 2014. "Statistical detection of fraud in the reporting of Croatian public companies," Financial Theory and Practice, Institute of Public Finance, vol. 38(1), pages 81-96.
    5. Scott Marchi & James Hamilton, 2006. "Assessing the Accuracy of Self-Reported Data: an Evaluation of the Toxics Release Inventory," Journal of Risk and Uncertainty, Springer, vol. 32(1), pages 57-76, January.
    6. Debreceny, Roger S. & Gray, Glen L., 2010. "Data mining journal entries for fraud detection: An exploratory study," International Journal of Accounting Information Systems, Elsevier, vol. 11(3), pages 157-181.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Amani, Farzaneh A. & Fadlalla, Adam M., 2017. "Data mining applications in accounting: A review of the literature and organizing framework," International Journal of Accounting Information Systems, Elsevier, vol. 24(C), pages 32-58.
    2. Lee, Kang-Bok & Han, Sumin & Jeong, Yeasung, 2020. "COVID-19, flattening the curve, and Benford’s law," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 559(C).
    3. Philip E Hulme & Danish A Ahmed & Phillip J Haubrock & Brooks A Kaiser & Melina Kourantidou & Boris Leroy & Shana M Mcdermott, 2024. "Widespread imprecision in estimates of the economic costs of invasive alien species worldwide," Post-Print hal-04633043, HAL.
    4. Matthew A. Cole & David J. Maddison & Liyun Zhang, 2020. "Testing the emission reduction claims of CDM projects using the Benford’s Law," Climatic Change, Springer, vol. 160(3), pages 407-426, June.
    5. Vadim S. Balashov & Yuxing Yan & Xiaodi Zhu, 2020. "Who Manipulates Data During Pandemics? Evidence from Newcomb-Benford Law," Papers 2007.14841, arXiv.org, revised Jan 2021.
    6. John Morrow, 2014. "Benford's Law, Families of Distributions and a Test Basis," CEP Discussion Papers dp1291, Centre for Economic Performance, LSE.
    7. Montag, Josef, 2017. "Identifying odometer fraud in used car market data," Transport Policy, Elsevier, vol. 60(C), pages 10-23.
    8. Montag, Josef, 2015. "Identifying Odometer Fraud: Evidence from the Used Car Market in the Czech Republic," MPRA Paper 65182, University Library of Munich, Germany.
    9. Fábio Albuquerque & Paula Gomes Dos Santos, 2023. "Recent Trends in Accounting and Information System Research: A Literature Review Using Textual Analysis Tools," FinTech, MDPI, vol. 2(2), pages 1-27, April.
    10. Ronelle Burger & Canh Thien Dang & Trudy Owens, 2017. "Better performing NGOs do report more accurately: Evidence from investigating Ugandan NGO financial accounts," Discussion Papers 2017-10, University of Nottingham, CREDIT.
    11. Roy Cerqueti & Claudio Lupi, 2023. "Severe testing of Benford’s law," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(2), pages 677-694, June.
    12. Wang, Delu & Chen, Fan & Mao, Jinqi & Liu, Nannan & Rong, Fangyu, 2022. "Are the official national data credible? Empirical evidence from statistics quality evaluation of China's coal and its downstream industries," Energy Economics, Elsevier, vol. 114(C).
    13. Fraas, Art & Egorenkov, Alex, 2015. "A Retrospective Study of EPA’s Air Toxics Program under the Revised Section 112 Requirements of the Clean Air Act," RFF Working Paper Series dp-15-23, Resources for the Future.
    14. Auffhammer, Maximilian & Carson, Richard T., 2008. "Forecasting the path of China's CO2 emissions using province-level information," Journal of Environmental Economics and Management, Elsevier, vol. 55(3), pages 229-247, May.
    15. Louie Rivers & Tamara Dempsey & Jade Mitchell & Carole Gibbs, 2015. "Environmental Regulation and Enforcement: Structures, Processes and the Use of Data for Fraud Detection," Journal of Environmental Assessment Policy and Management (JEAPM), World Scientific Publishing Co. Pte. Ltd., vol. 17(04), pages 1-29, December.
    16. Prasad, Acklesh & Green, Peter, 2015. "Governing cloud computing services: Reconsideration of IT governance structures," International Journal of Accounting Information Systems, Elsevier, vol. 19(C), pages 45-58.
    17. Rosa Abrantes-Metz & Sofia Villas-Boas & George Judge, 2011. "Tracking the Libor rate," Applied Economics Letters, Taylor & Francis Journals, vol. 18(10), pages 893-899.
    18. H. Spencer Banzhaf & Randall P. Walsh, 2010. "Segregation and Tiebout Sorting: Investigating the Link between Investments in Public Goods and Neighborhood Tipping," NBER Working Papers 16057, National Bureau of Economic Research, Inc.
    19. Andrea Cardoni & Evgeniia Kiseleva & Francesco De Luca, 2020. "Continuous auditing and data mining for strategic risk control and anticorruption: Creating “fair” value in the digital age," Business Strategy and the Environment, Wiley Blackwell, vol. 29(8), pages 3072-3085, December.
    20. Nilanjana Dutt & Andrew A. King, 2014. "The Judgment of Garbage: End-of-Pipe Treatment and Waste Reduction," Management Science, INFORMS, vol. 60(7), pages 1812-1828, July.

    More about this item

    Keywords

    Benfords Law; fraud; accounts payable; SAP audit trail analysis; internal audit;
    All these keywords.

    JEL classification:

    • M41 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Accounting
    • M42 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Auditing

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ami:journl:v:19:y:2020:i:2:p:379-404. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Cristina Tartavulea (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.