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Development of a fraud risk decision model for prioritizing fraud risk cases in manufacturing firms

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  • Mu, Enrique
  • Carroll, James
Abstract
Among the different risks a manufacturing firm faces, one of the most devastating may be caused by internal fraud. Corporate Fraud Investigation Units receive dozens of reports about possible fraud allegations annually. While all allegations should be addressed, it is not possible to investigate all cases immediately due to resource limitations. However, fraud may cause serious production and financial losses at any stage in the supply chain. Thus, it is necessary to find a method to prioritize the fraud risk of cases for the purpose of allocating resources and to determine how quickly they must be addressed. The Analytic Hierarchy Process (AHP) stands out as the most widely used prioritization methodology due to its intuitive simplicity and mathematical rigor. This study combines current SCM risk frameworks with extant fraud investigation literature and best-practices to develop an AHP ratings model for the prioritization of alleged fraud reports in a corporate setting, more specifically in the context of a large metals and mining manufacturing company.

Suggested Citation

  • Mu, Enrique & Carroll, James, 2016. "Development of a fraud risk decision model for prioritizing fraud risk cases in manufacturing firms," International Journal of Production Economics, Elsevier, vol. 173(C), pages 30-42.
    • Handle: RePEc:eee:proeco:v:173:y:2016:i:c:p:30-42
    DOI: 10.1016/j.ijpe.2015.11.014
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    References listed on IDEAS

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    1. Hales, Douglas N. & Chakravorty, Satya S. & Sridharan, V., 2009. "Testing Benford's Law for improving supply chain decision-making: A field experiment," International Journal of Production Economics, Elsevier, vol. 122(2), pages 606-618, December.
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    4. Fan, Ti-Jun & Chang, Xiang-Yun & Gu, Chun-Hua & Yi, Jian-Jun & Deng, Sheng, 2014. "Benefits of RFID technology for reducing inventory shrinkage," International Journal of Production Economics, Elsevier, vol. 147(PC), pages 659-665.
    5. Tang, Ou & Nurmaya Musa, S., 2011. "Identifying risk issues and research advancements in supply chain risk management," International Journal of Production Economics, Elsevier, vol. 133(1), pages 25-34, September.
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    Cited by:

    1. Yuli L. León & Enrique Mu, 2021. "Organizational Mindfulness Assessment and Its Impact on Rational Decision Making," Mathematics, MDPI, vol. 9(16), pages 1-29, August.
    2. Valerica NESTIAN & Silvia ISTRATE & Maria NEAGU, 2019. "A Company Improvement Analysis using the AHP/ANP Methods and the Modern Technology," Economics and Applied Informatics, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, issue 1, pages 62-77.
    3. Jandhyala, Srividya & Oliveira, Fernando S., 2021. "The role of international anti-corruption regulations in promoting socially responsible practices," Journal of Economic Behavior & Organization, Elsevier, vol. 190(C), pages 15-32.
    4. Anasse Amarouche & Philippe Chapellier & Alain George, 2018. "La gestion des risques dans une chaîne d’approvisionnement [La gestion des risques dans une chaine d'approvisionnement : Le cas de la filière d'approvisionnement en fruits et légumes d'une entrepri," Post-Print hal-02101506, HAL.
    5. Dong, Qingxing & Cooper, Orrin, 2016. "An orders-of-magnitude AHP supply chain risk assessment framework," International Journal of Production Economics, Elsevier, vol. 182(C), pages 144-156.

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