Detecting Corporate Crime

BUS465 Detecting Corporate Crime

Høst 2024

  • Topics

    This course provides an overview of white collar crime and methods of crime detection. The course is composed of several interconnected parts.

    Part 1 will give an overview on types of white collar crime. It will introduce students to how criminals take the decision to commit a crime, taking into account both the probability of detection and the punishment they could get. White collar crime is the most prevalent type of crime, as it hinges on gray areas of law and criminal responsibility. It is committed both by individuals and organizations. Examples of unethical and criminal behavior will be highlighted through case studies.

    Part 2 will focus on detection. Whistleblowers face the ethical dilemma of either keeping silent or reporting the crime and facing backlash within the industry. Beyond whistleblowers, traces of crimes can be spotted in publicly available data. Through the use of statistical software like R, the course will focus on several case studies of detection: e.g. number manipulation in the LIBOR cartel, insider trading to pin down illegal arms smuggling, tax evasion through cum-ex stock trading. This part of the course will focus on applications. We will consider the development of out-of-the-box methods for detection of cheating behavior.

    The final part of the course will introduce students to how to conduct an investigation, based on a tip. PwC will provide a real-world case where students have to uncover evidence for a crime. The output from this exercise will be a fact-finding report, which will be evaluated as one of the exam items in the course. Following the report, students will be divided in groups and will undergo training in interview techniques.

    The following topics are covered in the course:

    1. White Collar Crime and Becker model of Crime. Legal Framework. Embezzlement.
    2. Introduction into R.
    3. Case studies of unethical and illegal behavior within the organization.
    4. Whistleblowing and rocking the boat. The ethical problems of reporting on illegal or unethical behavior.
    5. Principles of crime detection for insider trading, tax evasion and cartels.
    6. How to use event studies. Insider trading and R application.
    7. Tax evasion and money laundering. Financial secrecy and tax havens. R application on cum-ex trading.
    8. Non-random behavior and detecting market cartels. R application on LIBOR cartel.
    9. Gaps in reported numbers. R application on misreporting of import/exports and the smuggling of art.
    10. Forensics Investigation and Reporting
    11. Interview Techniques

  • Learning outcome

    Upon course completion the students can:


    • Explain the link between unethical behavior and crime.
    • Distinguish different types of white-collar crime.
    • Explain how a criminal makes a decision to commit a crime.
    • Recognize the ethical dilemma faced by whistleblowers.
    • Explain how different detection strategies work.
    • Understand how to implement detection strategies.
    • Understand the Norwegian and international legal framework on corporate/financial crime.


    • Analyze crime risks within the organization, government and market.
    • Recognize when and how to blow the whistle safely on crime.
    • Apply known crime-detection strategies using statistical software.
    • Conduct a forensic investigation in the workplace.
    • Conduct interviews in the process of building a case.

    General Competence

    • Understand how cultural norms influence rule-breaking in an organization and in specific economic sectors and overall.
    • Debate an effective way to deter crime in the organization.
    • Discuss relevant ethical issues of criminal behavior in the organization.
    • Understand the uses and drawbacks of detection strategies.
    • Discern reliable information for building a case in the process of investigation.

  • Teaching

    The lectures will involve the discussion of cases in person. Theoretical material and R tutorials will be available in pre-recorded videos. Some lectures will be given by guest lecturers.

    Class participation. Students are expected to come to class prepared to have a conversation about the case discussed.

  • Recommended prerequisites

    Previous knowledge of microeconomics, statistics, finance, accounting will be useful.

    Experience in working with R or any other statistical software.

  • Credit reduction due to overlap


  • Compulsory Activity

    Participation in the interview training in week 12 (18.03-20.03).

  • Assessment

    Assignment in groups of 3-4 students (70%). From week 11 to 14 the students can work on the assignment and submit it for feedback. Feedback will be provided in week 15. The final submission deadline for the assignment is in week 16. In practice, students are expected to work on the assignment until week 16.

    6 hours individual home exam (30%).

    Assignment and home exam must be written in English.

    All elements have to be completed in the same semester.

  • Grading Scale

    A - F

  • Computer tools

    Statistical packages such as R, Stata or other

  • Literature


    Dyck, A., Morse, A., and Zingales, L. 2010 "Who Blows the Whistle of Corporate Fraud?" Journal of Finance

    DellaVigna, S., & La Ferrara, E. (2010). Detecting illegal arms trade. American Economic Journal: Economic Policy, 2(4), 26-57

    Fisman, R. and S.-J. Wei, 2004, "Tax Rates and Tax Evasion: Evidence from "Missing" Imports in China" Journal of Political Economy

    Fisman, R. and Wei, S.J., 2009. The smuggling of art, and the art of smuggling: Uncovering the illicit trade in cultural property and antiques. American Economic Journal: Applied Economics

    Abrantes-Metz, R.M., Villas-Boas, S.B. and Judge, G., 2011. Tracking the Libor rate. Applied Economics Letters

    Teichmann, F.M.J., 2017. Twelve methods of money laundering. Journal of Money Laundering Control, 20(2), pp.130-137.



Spring. Will be offered Spring 2024.

Course responsible

Professor Evelina Gavrilova-Zoutman, Department of Management and Business Science