The Postmodern Ponzi Scheme: Empirical Analysis of High-Yield Investment Programs

  • Tyler Moore
  • Jie Han
  • Richard Clayton
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7397)


A High Yield Investment Program (HYIP) is an online Ponzi scheme, a financial fraud that pays outrageous levels of interest using money from new investors. We call this fraud ‘postmodern’ in that sophisticated investors understand the fraud, but hope to profit by joining early. These investors support ‘aggregators’ – reputation websites that track the status of HYIPs. We examine 9 months of aggregator data and show that there is no evidence of collusion between different aggregators. We use their data to measure the time until HYIPs collapse, finding – perhaps unsurprisingly – that longer lifetimes are associated with lower interest payments and longer mandatory investment terms. We look at the role of digital currencies in supporting HYIPs, finding that a handful of systems dominate. Finally, we estimate that this type of criminality is turning over at least $6 million/month and set out ways in which it might be disrupted.


Interest Rate Survival Function Investment Program Investment Term Email Spam 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Simpson, E.H.: Measurement of diversity. Nature 163, 688 (1949)zbMATHCrossRefGoogle Scholar
  2. 2.
    Kaplan, E., Meier, P.: Nonparametric estimation from incomplete observations. Journal of the American Statistical Association 53, 457–481 (1958)MathSciNetzbMATHCrossRefGoogle Scholar
  3. 3.
    Lorenzini, M.: Strictpay scam: Thoughts before strictpay shutdown. HYIP News (June 2010),
  4. 4.
    Zetter, K.: Bullion and bandits: The improbable rise and fall of e-gold. Wired (June 2009),
  5. 5.
    Kanich, C., Weaver, N., McCoy, D., Halvorson, T., Kreibich, C., Levchenko, K., Paxson, V., Voelker, G.M., Savage, S.: Show me the money: Characterizing spam-advertised revenue. In: Proceedings of USENIX Security 2011, San Francisco, CA (August 2011)Google Scholar
  6. 6.
    Kanich, C., Kreibich, C., Levchenko, K., Enright, B., Voelker, G., Paxson, V., Savage, S.: Spamalytics: An empirical analysis of spam marketing conversion. In: Conference on Computer and Communications Security (CCS), Alexandria, VA (October 2008)Google Scholar
  7. 7.
    Leontiadis, N., Moore, T., Christin, N.: Measuring and analyzing search-redirection attacks in the illicit online prescription drug trade. In: Proceedings of USENIX Security 2011, San Francisco, CA (August 2011)Google Scholar
  8. 8.
    Commission, C.F.E.: Press Release PR6074-11: CFTC charges Jeffery A. Lowrance and his company, First Capital Savings and Loan, with operating a million dollar foreign currency Ponzi scheme (July 2011),
  9. 9.
    Moore, T., Clayton, R., Anderson, R.: The economics of online crime. Journal of Economic Perspectives 23(3), 3–20 (2009)CrossRefGoogle Scholar
  10. 10.
    Cova, M., Leita, C., Thonnard, O., Keromytis, A.D., Dacier, M.: An Analysis of Rogue AV Campaigns. In: Jha, S., Sommer, R., Kreibich, C. (eds.) RAID 2010. LNCS, vol. 6307, pp. 442–463. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  11. 11.
    Stone-Gross, B., Abman, R., Kemmerer, R.A., Kruegel, C., Steigerwald, D.G., Vigna, G.: The underground economy of fake antivirus software. In: 10th Workshop on the Economics of Information Security, Fairfax, VA (June 2011)Google Scholar
  12. 12.
    Christin, N., Yanagihara, S., Kamataki, K.: Dissecting one click frauds. In: ACM Conference on Computer and Communications Security (CCS), Chicago, IL, pp. 15–26 (October 2010)Google Scholar
  13. 13.
    Stajano, F., Wilson, P.: Understanding scam victims: seven principles for systems security. Commun. ACM 54, 70–75 (2011)CrossRefGoogle Scholar
  14. 14.
    Birch, D.G.W., McEvoy, N.A.: Electronic Cash - Technology will Denationalise Money. In: Hirschfeld, R. (ed.) FC 1997. LNCS, vol. 1318, pp. 95–108. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  15. 15.
    Chaum, D.: Achieving electronic privacy. Scientific American, 96–101 (August 1992)Google Scholar
  16. 16.
    Wayner, P.C.: Money Laundering: Past, Present and Future. In: Hirschfeld, R. (ed.) FC 1997. LNCS, vol. 1318, pp. 301–306. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  17. 17.
    Anderson, R.: Closing the phishing hole: Fraud, risk and nonbanks. In: Federal Reserve Bank of Kansas City – Payment System Research Conferences (2007)Google Scholar
  18. 18.
    Moore, T., Clayton, R.: Evaluating the Wisdom of Crowds in Assessing Phishing Websites. In: Tsudik, G. (ed.) FC 2008. LNCS, vol. 5143, pp. 16–30. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  19. 19.
    Chia, P.H., Knapskog, S.J.: Re-evaluating the Wisdom of Crowds in Assessing Web Security. In: Danezis, G. (ed.) FC 2011. LNCS, vol. 7035, pp. 299–314. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  20. 20.
    Levchenko, K., Chachra, N., Enright, B., Felegyhazi, M., Grier, C., Halvorson, T., Kanich, C., Kreibich, C., Liu, H., McCoy, D., Pitsillidis, A., Weaver, N., Paxson, V., Voelker, G., Savage, S.: Click trajectories: End-to-end analysis of the spam value chain. In: IEEE Symposium on Security and Privacy, Oakland, CA, pp. 431–446 (May 2011)Google Scholar
  21. 21.
    Clayton, R.: How much did shutting down McColo help? In: Sixth Conference on Email and Antispam, CEAS (July 2009)Google Scholar
  22. 22.
    Liu, H., Levchenko, K., Felegyhazi, M., Kreibich, C., Maier, G., Voelker, G.M., Savage, S.: On the effects of registrar-level intervention. In: USENIX Workshop on Large-scale Exploits and Emergent Threats (LEET), Boston, MA (March 2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Tyler Moore
    • 1
  • Jie Han
    • 1
  • Richard Clayton
    • 2
  1. 1.Computer Science DepartmentWellesley CollegeUSA
  2. 2.Computer LaboratoryUniversity of CambridgeUK

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