Detecting fraud beyond cooked books: Forensic economics, psychology and accounting toolkit

Analysis of existing archival data

The simplest tool is a direct observation of dishonesty in existing data. Firms do not always use their opportunities to analyze data they already own to reveal whether their employees, contractors or clients cheat. However, some must do so, such as the Dutch company Candyman. It delivers a box of sweets to its clients – firms. Each employee of the client can take the candy and pay by putting money in a box of coins. The box is open so that people can get change. Marco Haan and Peter Kooreman

Observational studies

Another simple tool is made when an organization or an individual decides to create data about potentially fraudulent behavior by recording or observing, be it illegal parking or bribery. Another possibility is to compare data drawn from two independent sources and examine if frauds occur, e.g. utilizing the independent measurement of GPS proves that taxi drivers overestimate the distance traveled. Sometimes it may be necessary to obtain new data verifying a potential fraud in already existing

Randomized field experiments, audit studies, and lab-in-the-field experiments

Randomized experiments (A/B testing) reveal how a group exposed to a specific treatment (be it an intensity of supervision, a form of operational management, working conditions etc.) behaves compared to the control group with the treatment absent. For instance, Daniel Nagin and colleagues conducted a double-blind field experiment to observe the effect of experimentally-induced variations in monitoring on employee opportunism. Many employees acted dishonestly; nevertheless, a substantial

Lexicographical Text Analysis and Analysis of Speech and Voice

Text analysis can reveal patterns which correlate with fraud. For example, deceptive language uses more activation language, words, imagery, pleasantness, group references, and less lexical diversity than non-deceptive language. Fraudulent financial disclosures thus appear credible while communicating less in actual content. Gerard Hoberg and Craig Lewis based on firms’ disclosures to the U. S. Securities and Exchange Commission have found that fraudulent verbal disclosure contains fewer

Integrity testing (overt integrity tests and personality-oriented tests)

Integrity testing is a large class of structured interviews, questionnaires and other personality tests which measure the moral attitudes of employees or managers, their theft attitudes, perceived ease of fraud, endorsement of common rationalizations for fraud or dishonesty, etc. The most employed tests, The Reid Report, Stanton Survey, and PSI Honesty Scale, are identified as overt measures of integrity. Alternatively, the questioner tries to (directly or indirectly) gain admissions of theft

Conclusion

Cheating, dishonest and deceptive behavior bear indisputable costs. Corporate scandals not only lower or destroy a firm’s value for its shareholders and undermine (otherwise) a productive and ethical company culture, but also weaken trust in the market or government. Similarly, stealing and cheating customers do not merely weaken moral norms, but also decrease the motivation of employees.

Cheating is, by definition concealed, so the striving for its detection and elimination is an ongoing

Conflict of interest

The author declares that he has no conflict of interest.

Funding

This study was funded by Czech Grant Agency (grant number 18-13766S).

Acknowledgments

I am very grateful to Douglas Shields Dix for editing the English version of the manuscript.

Selected bibliography

Introductory section is based on these studies: Bazerman, M. H., & Gino, F. (2012). Behavioral Ethics: Toward a Deeper Understanding of Moral Judgment and Dishonesty. Annual Review of Law and Social Science, 8(1), 85–104. doi: 10.1146/annurev-lawsocsci-102811-173815; Eden, D. (2017). Field Experiments in Organizations. Annual Review of Organizational Psychology and Organizational Behavior, 4(1), 91–122. doi: 10.1146/annurev-orgpsych-041015-062400; Greve, H. R., Palmer, D., & Pozner, J. E.