Decoding “Digital Sin”: Forex Tech and the New Frontiers of Currency Instability

Authors

  • Sandeep Kumar Pandey Research Scholar, Department of International Business, Pondicherry University Author
  • Prof. P. Sridharan Professor & Head of Department, Department of International Business, Pondicherry University Author

DOI:

https://doi.org/10.31305/ijmds.v15n10.009

Keywords:

Forex Tech, Currency Instability, Digital Sin, Gamification, Behavioural Finance, Retail Forex Trading, Fintech Regulation

Abstract

The rapid diffusion of mobile and platform-based foreign exchange trading technologies collectively termed “Forex Tech” has transformed currency speculation from a domain reserved for banks and institutional dealers into an activity accessible to millions of retail participants at the tap of a screen. While this democratisation is often celebrated as a marker of financial inclusion and innovation, it has also introduced a set of behavioural and systemic risks that remain conceptually under-theorised. This paper develops the notion of “digital sin” a technologically mediated pattern of compulsive, overconfident, and loss-chasing speculative behaviour that is deliberately or incidentally cultivated by the design architecture of Forex Tech platforms. Drawing on behavioural finance, the sin-stock and problem-gambling literatures, and fintech scholarship, the paper proposes a conceptual framework, the “Digital Sin Cycle,” that links platform design features to individual behavioural triggers, aggregated retail herding, and short-run currency market instability, with a reinforcing feedback loop back to platform design. Six theoretical propositions are advanced to guide future empirical inquiry. The paper concludes with implications for platform design ethics, financial consumer protection, and macroprudential and conduct regulation in an era of increasingly gamified, algorithmically mediated currency markets.

References

[1] Aramonte, S., Huang, W., & Schrimpf, A. (n.d.). DeFi risks and the decentralisation illusion.

[2] Arner, D. W., Barberis, J. N., & Buckley, R. P. (2015). The Evolution of Fintech: A New Post-Crisis Paradigm? SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2676553 DOI: https://doi.org/10.2139/ssrn.2676553

[3] Arthur, J. N., Williams, R. J., & Delfabbro, P. H. (2016). The conceptual and empirical relationship between gambling, investing, and speculation. Journal of Behavioral Addictions, 5(4), 580–591. https://doi.org/10.1556/2006.5.2016.084. DOI: https://doi.org/10.1556/2006.5.2016.084

[4] Barber, B. M., & Odean, T. (1998). The Common Stock Investment Performance of Individual Investors. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.94140 DOI: https://doi.org/10.2139/ssrn.94140

[5] Barberis, N., & Huang, M. (2008). Stocks as Lotteries: The Implications of Probability Weighting for Security Prices. American Economic Review, 98(5), 2066–2100. https://doi.org/10.1257/aer.98.5.2066 DOI: https://doi.org/10.1257/aer.98.5.2066

[6] Chaboud, A. P., Chiquoine, B., Hjalmarsson, E., & Vega, C. (2014). Rise of the Machines: Algorithmic Trading in the Foreign Exchange Market. The Journal of Finance, 69(5), 2045–2084. https://doi.org/10.1111/jofi.12186 DOI: https://doi.org/10.1111/jofi.12186

[7] Fabozzi, F. J., Ma, K. C., & Oliphant, B. J. (2008). FRANK J. FABOZZI, K.C. MA, AND BECKY J. OLIPHANT.

[8] Fogg, B. (2009). A behavior model for persuasive design. Proceedings of the 4th International Conference on Persuasive Technology, 1–7. https://doi.org/10.1145/1541948.1541999 DOI: https://doi.org/10.1145/1541948.1541999

[9] Gale, D. (1996). What have we learned from social learning? European Economic Review, 40(3–5), 617–628. https://doi.org/10.1016/0014-2921(95)00074-7 DOI: https://doi.org/10.1016/0014-2921(95)00074-7

[10] Gomber, P., Koch, J.-A., & Siering, M. (2017). Digital Finance and FinTech: Current research and future research directions. Journal of Business Economics, 87(5), 537–580. https://doi.org/10.1007/s11573-017-0852-x DOI: https://doi.org/10.1007/s11573-017-0852-x

[11] Hong, H., & Kacperczyk, M. (n.d.). The price of sin The effects of social norms on markets.

[12] Hsu, H. (2013). Machine gambling in the ‘zone’: Natasha Dow Schüll’s Addiction by Design. Social Studies of Science, 43(6), 952–956. https://doi.org/10.1177/0306312713507496 DOI: https://doi.org/10.1177/0306312713507496

[13] Krugman, P. (1979). A Model of Balance-of-Payments Crises. Journal of Money, Credit and Banking, 11(3), 311. https://doi.org/10.2307/1991793 DOI: https://doi.org/10.2307/1991793

[14] Kumar, A. (2009). Who Gambles in the Stock Market? The Journal of Finance, 64(4), 1889–1933. https://doi.org/10.1111/j.1540-6261.2009.01483.x DOI: https://doi.org/10.1111/j.1540-6261.2009.01483.x

[15] Pelster, M., & Hofmann, A. (2018). About the fear of reputational loss: Social trading and the disposition effect. Journal of Banking & Finance, 94, 75–88. https://doi.org/10.1016/j.jbankfin.2018.07.003 DOI: https://doi.org/10.1016/j.jbankfin.2018.07.003

[16] Statman, M. (2002). Lottery Players/Stock Traders. Financial Analysts Journal, 58(1), 14–21. https://doi.org/10.2469/faj.v58.n1.2506 DOI: https://doi.org/10.2469/faj.v58.n1.2506

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Published

2026-10-15

How to Cite

Pandey, S. K., & Sridharan, P. (2026). Decoding “Digital Sin”: Forex Tech and the New Frontiers of Currency Instability. International Journal of Management and Development Studies, 15(10), 88-97. https://doi.org/10.31305/ijmds.v15n10.009

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