Fraudsters target the elderly: Behavioural evidence from randomised controlled scam-baiting experiments

Author:

Robinson Jemima,Edwards MatthewORCID

Abstract

AbstractEmail-based fraud is a lucrative market for cybercriminals to scam a wide range of potential victims. Yet there is a sometimes conflicted literature on who these victims are, complicated by low and possibly confounded reporting rates. We make use of an experimental automated scam-baiting platform to test hypotheses about the characteristics online fraudsters find more attractive, gathering behavioural evidence directly from the fraudsters themselves (n = 296). In our comparison of four instrumented ‘personalities’ designed based on traits highlighted in the literature and in a small public perception survey, we find that a script adopting the personality of an elderly woman attracts significantly more engagement from scammers than our control measure. We discuss our approach and the possible interpretations and implications of our findings.

Publisher

Springer Science and Business Media LLC

Subject

Law,Strategy and Management,Safety Research

Reference48 articles.

1. Al, Big. 2022. ScamSurvivors.com, My ethical baiting 101 file. https://www.scamsurvivors.com/forum/viewtopic.php?f=80 &t=96040. Accessed 03 May 2023.

2. Alves, Linda M., and Steve R. Wilson. 2008. The effects of loneliness on telemarketing fraud vulnerability among older adults. Journal of Elder Abuse & Neglect 20 (1): 63–85.

3. Australian Competition and Consumer Commission. 2016. Scam statistics. https://www.scamwatch.gov.au/scam-statistics?scamid=all. Accessed 30 March 2023.

4. Authority, National Fraud Strategic. 2008. Strategic. The national fraud strategy: A new approach to combating fraud.

5. Bajaj, Piyush, and Matthew, Edwards. 2023. Automatic scam-baiting using ChatGPT. In Proceedings of the 7th International Workshop on Applications of AI, Cyber Security and Economics Data Analytics (ACE-2023). IEEE.

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