Artificial Intelligence and data analytics in impact investing: Emerging opportunities for social enterprises

Authors

  • Pawan Kumar Singh Research Scholar, School of Management & Commerce, Vikrant University, Gwalior, Madhya Pradesh Author
  • Prof. (Dr.) Rahul Kushwah Supervisor & Dean (School of Management & Commerce), Vikrant University, Gwalior, Madhya Pradesh Author

DOI:

https://doi.org/10.29070/71ynw502

Keywords:

Artificial Intelligence, Data Analytics, Impact Investing, Social Enterprises, ESG, Sustainable Finance, FinTech, Big Data, Machine Learning, Responsible Investment

Abstract

The rapid advancement of Artificial Intelligence (AI) and Data Analytics has transformed the global financial ecosystem, particularly in the field of impact investing and social entrepreneurship. Impact investing refers to investments made with the intention of generating measurable social and environmental benefits alongside financial returns. Social enterprises, which aim to solve societal challenges through innovative business models, increasingly rely upon technology-driven financial systems to expand their operations and improve impact measurement. Artificial Intelligence and data-driven analytics provide sophisticated tools for assessing investment risks, predicting social outcomes, identifying underserved markets, and optimizing sustainable financial decision-making.

This study critically examines the emerging role of AI and data analytics in impact investing and explores how these technologies create new opportunities for social enterprises. The article evaluates the integration of machine learning, predictive analytics, big data, blockchain-based transparency systems, and automated impact assessment models within the impact investment ecosystem. It further analyses legal, ethical, and governance challenges associated with algorithmic bias, data privacy, transparency, accountability, and digital inequality.

The study adopts a doctrinal and analytical methodology based on secondary sources including academic literature, international reports, policy documents, and financial technology studies. It concludes that AI and data analytics have the potential to significantly enhance transparency, scalability, and effectiveness in impact investing. However, ethical regulation, responsible AI governance, and inclusive digital infrastructure are necessary to ensure sustainable and equitable growth of social enterprises globally.

Downloads

Download data is not yet available.

References

1. Agrawal, A., Gans, J., & Goldfarb, A. (2019). The economics of artificial intelligence. University of Chicago Press.

2. Barman, A. (2022). Artificial intelligence and sustainable finance: Emerging legal dimensions. Journal of Financial Regulation and Compliance, 30(4), 512–528.

3. Brynjolfsson, E., & McAfee, A. (2017). Machine, platform, crowd: Harnessing our digital future. W.W. Norton & Company.

4. European Commission. (2021). Proposal for a regulation laying down harmonized rules on artificial intelligence. Brussels.

5. Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerization? Technological Forecasting and Social Change, 114, 254–280.

6. Global Impact Investing Network. (2023). Annual impact investor survey. GIIN Publications.

7. Gupta, R., & Sharma, N. (2021). Big data analytics in sustainable finance. International Journal of Financial Studies, 9(3), 1–18.

8. Hildebrandt, M. (2020). Law for computer scientists and other folk. Oxford University Press.

9. Kaplan, A., & Haenlein, M. (2019). Siri, Siri in my hand: Who’s the fairest in the land? Business Horizons, 62(1), 15–25.

10. Kshetri, N. (2021). Blockchain and sustainable development goals. IT Professional, 23(4), 11–16.

11. Lee, K. F. (2018). AI superpowers: China, Silicon Valley, and the new world order. Houghton Mifflin Harcourt.

12. Marr, B. (2021). Data strategy: How to profit from a world of big data, analytics and artificial intelligence. Kogan Page.

13. Millstone, E. (2020). Ethical governance of AI in finance. Journal of Business Ethics, 165(4), 745–759.

14. OECD. (2019). OECD principles on artificial intelligence. OECD Publishing.

15. O’Neil, C. (2016). Weapons of math destruction. Crown Publishing.

16. Porter, M. E., & Kramer, M. R. (2019). Creating shared value. Harvard Business Review, 89(1–2), 62–77.

17. Raworth, K. (2018). Doughnut economics: Seven ways to think like a 21st-century economist. Chelsea Green Publishing.

18. Schwab, K. (2017). The fourth industrial revolution. Crown Business.

19. Sharma, P., & Jain, V. (2023). AI-enabled ESG investing and regulatory challenges. Indian Journal of Corporate Law, 15(2), 95–118.

20. Singh, R. (2022). Digital finance and social entrepreneurship in India. Journal of Social Innovation and Entrepreneurship, 8(1), 44–61.

21. Smith, N. C., & Reficco, E. (2020). Social enterprises and sustainable finance. California Management Review, 62(3), 59–79.

22. United Nations. (2022). Sustainable development goals report 2022. United Nations Publications.

23. World Economic Forum. (2023). AI governance alliance report. WEF Publications.

24. Yunus, M. (2017). A world of three zeros. Public Affairs.

25. Zuboff, S. (2019). The age of surveillance capitalism. Public Affairs.

Downloads

Published

2026-04-01

Issue

Vol. 23 No. 2 (2026): Journal of Advances and Scholarly Researches in Allied Education (JASRAE)

Section

Articles

How to Cite

[1]
“Artificial Intelligence and data analytics in impact investing: Emerging opportunities for social enterprises”, JASRAE, vol. 23, no. 2, pp. 334–344, Apr. 2026, doi: 10.29070/71ynw502.