Detecting Corporate Crime

BUS465 Detecting Corporate Crime

Autumn 2021

  • Topics

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

  • Learning outcome

    Upon completing the course students can

    Knowledge

    • 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·
    • Know how to implement detection strategies 

    Skills

    • 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

    General Competence

    • Understand how cultural norms influence rule-breaking in specific economic sectors and overall
    • Debate an effective way to deter crime in the organization
    • Have an insight into relevant ethical issues of criminal behavior in the organization
    • Understand the uses and drawbacks of detection strategies

  • Teaching

    Lectures.

    It will be possible to follow the course digitally.

  • 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

    None.

  • Requirements for course approval

    None.

  • Assessment

    One assignment (groups of 3-4 students) with re-submission after feedback, which accounts for 70% of the grade. The resubmission should be accompanied with individual reflection notes.

    One home examination, 6 hours, counts 30 % of the grade. Assignments must be written in English. The students will work with the assignment between week 5 and week 14.

    All elements have to be taken in the same semester.

  • Grading Scale

    A-F

  • Computer tools

    Statistical packages such as R, Stata or other

  • Literature

    The following topics are covered in the course:

    1. White Collar Crime and Becker model of Crime. 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. R application on predicting crime through machine learning.

    Papers:

    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

    Dube, A., Kaplan, E. and Naidu, S., 2011. Coups, corporations, and classified information. The Quarterly Journal of Economics

    Hsieh, C.T. and Moretti, E., 2006. Did Iraq cheat the United Nations? Underpricing, bribes, and the oil for food program. The Quarterly Journal of Economics

    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

    Jacob, Brian A., and Steven D. Levitt. 2003 "Rotten apples: An investigation of the prevalence and predictors of teacher cheating." Quarterly Journal of 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.

Overview

ECTS Credits
7.5
Teaching language
English
Semester

Spring. Offered Spring 2021.

Course responsible

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