BUS465 Corporate Crime: Detection and Prevention
This course provides an overview of white collar crime, methods of crime detection and policies of crime prevention. The course is composed of three 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 and how they organize with others criminals. Criminals take 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, where unethical behavior does not have a set punishment.
Part 2 will give an overview on the types of unethical criminal behavior that can happen within and by an organization. On one hand, case studies such as Olympos and Siemens will highlight how crime is a way to continue the existence of the firm, at the cost of its integrity. On the other hand, whistleblowers who report white collar crimes face the ethical dilemma of either keeping silent or reporting the crime and facing backlash within the industry.
Part 3 will focus on developing techniques in order to detect common crime types with data tools. Some crimes can be spotted through the analysis of data, e.g. market cartels (such as the LIBOR cartel), insider trading (suspicious stock price movements) and tax evasion (misreporting and third party reporting). 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.
Upon completing the course 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·
- Know how to implement detection strategies
- 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
- 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
Previous knowledge of microeconomics, statistics, finance, accounting will be useful.
The assessment in this course will not be changed in the spring semester of 2020.
1 assignment (groups of 3-4 students) with re-submission after feedback which account for 30% of the grade and one (individual) home examination, 6 hours, counts 70 % 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.
Statistical package such as R, Stata or other
Dyck, A., Morse, A., and Zingales, L. 2010 "Who Blows the Whistle of Corporate Fraud?" Journal of Finance
Fisman, R. and E. Miguel, 2007, "Corruption, Norms, and Legal Enforcement: Evidence from UN Diplomatic Parking Tickets" Journal of Political Economy
Chalfin, A. and McCrary, J., 2017. Criminal deterrence: A review of the literature. Journal of Economic Literature
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.
- ECTS Credits
- Teaching language
Spring. First time Spring 2020.
Associate Professor Evelina Gavrilova-Zoutman, Department of Management and Business Science