IDEAS home Printed from https://ideas.repec.org/a/inm/orisre/v30y2019i2p466-485.html
   My bibliography  Save this article

Audit Policies Under the Sentinel Effect: Deterrence-Driven Algorithms

Author

Listed:
  • Lina Bouayad

    (Department of Information Systems and Business Analytics, College of Business, Florida International University, Miami, Florida 33199;)

  • Balaji Padmanabhan

    (Department of Information Systems & Decision Sciences, Muma College of Business, University of South Florida, Tampa, Florida 33620;)

  • Kaushal Chari

    (Department of Information Systems & Decision Sciences, Muma College of Business, University of South Florida, Tampa, Florida 33620; Lubar School of Business, University of Wisconsin–Milwaukee, Milwaukee, Wisconsin 53202)

Abstract

Fraud, waste, and abuse are significant problems in major industries such as healthcare, particularly when third-party payers such as Medicare are involved. Auditors looking for fraudulent activities use scoring models to select practitioners or claims that are likely to be fraudulent. In addition to the direct benefits of the audit effect , which evokes a response by auditing fraudulent individuals, the sentinel effect provides second-order benefits. Yet current auditing algorithms do not take the sentinel effect into account. In this paper, we present an algorithm that supports a deterrence-driven audit approach in the presence of audit and sentinel effects. Our results indicate that a significant reduction in healthcare excess costs can be achieved, while maintaining fairness, when auditing policies take sentinel effects into account.The online appendix is available at https://doi.org/10.1287/isre.2019.0841 .

Suggested Citation

  • Lina Bouayad & Balaji Padmanabhan & Kaushal Chari, 2019. "Audit Policies Under the Sentinel Effect: Deterrence-Driven Algorithms," Information Systems Research, INFORMS, vol. 30(2), pages 466-485, June.
    • Handle: RePEc:inm:orisre:v:30:y:2019:i:2:p:466-485
    DOI: 10.1287/isre.2019.0841
    as

    Download full text from publisher

    File URL: https://doi.org/10.1287/isre.2019.0841
    Download Restriction: no
    File URL: https://libkey.io/10.1287/isre.2019.0841?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Eide, Erling & Rubin, Paul H. & Shepherd, Joanna M., 2006. "Economics of Crime," Foundations and Trends(R) in Microeconomics, now publishers, vol. 2(3), pages 205-279, December.
    2. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    3. Georges Dionne & Florence Giuliano & Pierre Picard, 2009. "Optimal Auditing with Scoring: Theory and Application to Insurance Fraud," Management Science, INFORMS, vol. 55(1), pages 58-70, January.
    4. Wu, Fang & Huberman, Bernardo A. & Adamic, Lada A. & Tyler, Joshua R., 2004. "Information flow in social groups," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 337(1), pages 327-335.
    5. G. Dionne & F. Giuliano & P. Picard, 2002. "Optimal auditing for insurance fraud," THEMA Working Papers 2002-32, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    6. H. Peyton Young, 2009. "Innovation Diffusion in Heterogeneous Populations: Contagion, Social Influence, and Social Learning," American Economic Review, American Economic Association, vol. 99(5), pages 1899-1924, December.
    7. Véronique Van Vlasselaer & Tina Eliassi-Rad & Leman Akoglu & Monique Snoeck & Bart Baesens, 2017. "GOTCHA! Network-Based Fraud Detection for Social Security Fraud," Management Science, INFORMS, vol. 63(9), pages 3090-3110, September.
    8. John D'Arcy & Anat Hovav & Dennis Galletta, 2009. "User Awareness of Security Countermeasures and Its Impact on Information Systems Misuse: A Deterrence Approach," Information Systems Research, INFORMS, vol. 20(1), pages 79-98, March.
    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. Farbmacher, Helmut & Löw, Leander & Spindler, Martin, 2022. "An explainable attention network for fraud detection in claims management," Journal of Econometrics, Elsevier, vol. 228(2), pages 244-258.
    2. Höppner, Sebastiaan & Baesens, Bart & Verbeke, Wouter & Verdonck, Tim, 2022. "Instance-dependent cost-sensitive learning for detecting transfer fraud," European Journal of Operational Research, Elsevier, vol. 297(1), pages 291-300.
    3. Galeotti, Marcello & Rabitti, Giovanni & Vannucci, Emanuele, 2020. "An evolutionary approach to fraud management," European Journal of Operational Research, Elsevier, vol. 284(3), pages 1167-1177.
    4. Sergio Currarini & Carmen Marchiori & Alessandro Tavoni, 2016. "Network Economics and the Environment: Insights and Perspectives," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 65(1), pages 159-189, September.
    5. Sgrignoli, P. & Agliari, E. & Burioni, R. & Schianchi, A., 2015. "Instability and network effects in innovative markets," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 108(C), pages 260-271.
    6. Edouard Civel & Marc Baudry, 2018. "The Fate of Inventions. What can we learn from Bayesian learning in strategic options model of adoption ?," EconomiX Working Papers 2018-47, University of Paris Nanterre, EconomiX.
    7. Jean-Marc Bourgeon & Pierre Picard, 2014. "Fraudulent Claims and Nitpicky Insurers," American Economic Review, American Economic Association, vol. 104(9), pages 2900-2917, September.
    8. Mercure, Jean-François, 2018. "Fashion, fads and the popularity of choices: Micro-foundations for diffusion consumer theory," Structural Change and Economic Dynamics, Elsevier, vol. 46(C), pages 194-207.
    9. John Bone & Dominic Spengler, 2014. "Does Reporting Decrease Corruption?," Journal of Interdisciplinary Economics, , vol. 26(1-2), pages 161-186, January.
    10. Dionne, Georges & Wang, Kili, 2011. "Does opportunistic fraud in automobile theft insurance fluctuate with the business cycle?," Working Papers 11-4, HEC Montreal, Canada Research Chair in Risk Management.
    11. Appelgren, Leif, 2020. "A survey of models for determining optimal audit strategies," Advances in accounting, Elsevier, vol. 48(C).
    12. John E. Murray, 2011. "Asymmetric Information and Countermeasures in Early Twentieth‐Century American Short‐Term Disability Microinsurance," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 78(1), pages 117-138, March.
    13. Jing Ai & Patrick L. Brockett & Linda L. Golden & Montserrat Guillén, 2013. "A Robust Unsupervised Method for Fraud Rate Estimation," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 80(1), pages 121-143, March.
    14. Nicola Gennaioli & Rafael La Porta & Florencio Lopez-de-Silanes & Andrei Shleifer, 2022. "Trust and Insurance Contracts," The Review of Financial Studies, Society for Financial Studies, vol. 35(12), pages 5287-5333.
    15. Michael Ludkovski & Virginia R. Young, 2010. "Ex Post Moral Hazard and Bayesian Learning in Insurance," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 77(4), pages 829-856, December.
    16. Cantono, Simona, 2012. "Unveiling diffusion dynamics: an autocatalytic percolation model of environmental innovation diffusion and the optimal dynamic path of adoption subsidies," Department of Economics and Statistics Cognetti de Martiis LEI & BRICK - Laboratory of Economics of Innovation "Franco Momigliano", Bureau of Research in Innovation, Complexity and Knowledge, Collegio 201222, University of Turin.
    17. Abe Dunn & Joshua D. Gottlieb & Adam Shapiro & Daniel J. Sonnenstuhl & Pietro Tebaldi, 2021. "A Denial a Day Keeps the Doctor Away," NBER Working Papers 29010, National Bureau of Economic Research, Inc.
    18. Chamal Gomes & Zhuo Jin & Hailiang Yang, 2021. "Insurance fraud detection with unsupervised deep learning," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 88(3), pages 591-624, September.
    19. Pierre Picard, 2012. "Economic Analysis of Insurance Fraud," Working Papers hal-00725561, HAL.
    20. Giovanni Pegoretti & Francesco Rentocchini & Giuseppe Vittucci Marzetti, 2012. "An agent-based model of innovation diffusion: network structure and coexistence under different information regimes," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 7(2), pages 145-165, October.

    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:inm:orisre:v:30:y:2019:i:2:p:466-485. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.