AI-DRIVEN FINTECH AND FINANCIAL INCLUSION: OPPORTUNITIES AND CHALLENGES
DOI:
https://doi.org/10.69980/gchmn975Keywords:
artificial intelligence, FinTech, financial inclusion, algorithmic bias, data privacy, responsible AI, digital divideAbstract
The goal of financial inclusion which provides affordable and responsible financial services to all people remains an important global development target because 1.4 billion adults worldwide do not have access to banking services (Demirgüç-Kunt et al., 2022). The conventional FinTech developments have increased digital payment systems but artificial intelligence (AI) delivers a revolutionary advancement through its ability to provide different credit evaluation methods and bespoke customer support and talking banking services and automated danger assessment systems. The research presents a thorough examination of artificial intelligence-based FinTech systems which create both advantages and difficulties for achieving financial access through their application. The research shows that artificial intelligence technology can help organizations in low- and middle-income countries reduce operational expenses while solving problems related to information access and reaching people who have been excluded from financial services. The study identifies major threats which include algorithmic bias that maintains existing discriminatory patterns and hidden processes of "black box" systems that reduce accountability and security breaches of personal information and system deficiencies and the situation where financial inclusion leads to exploitative pricing practices. The responsible AI framework which we present for implementation in Kenya India Mexico and Nigeria is based on four key principles which include fairness and explainability and privacy and human oversight. The paper concludes with actionable policy recommendations for central banks and FinTech firms and international development organizations which aim to use artificial intelligence while preventing the creation of digital and algorithmic disparities.
References
1.Aker, J. C. (2017). Using digital technology for financial inclusion in developing countries. In D. Lee & D. J. Lee (Eds.), The Palgrave handbook of economics and development (pp. 345–372). Palgrave Macmillan.
2.Barocas, S., & Selbst, A. D. (2016). Big data's disparate impact. California Law Review, 104(3), 671–732. https://doi.org/10.15779/Z38BG31
3.Benartzi, S., & Thaler, R. H. (2013). Behavioral economics and the retirement savings crisis. Science, 339(6119), 410–412. https://doi.org/10.1126/science.1230756
4.Berg, T., Burg, V., Gombović, A., & Puri, M. (2020). On the rise of finTechs: Credit scoring using digital footprints. The Review of Financial Studies, 33(7), 2845–2897. https://doi.org/10.1093/rfs/hhz099
5.Björkegren, D., & Grissen, D. (2020). The potential of digital credit to expand financial inclusion. Journal of Economic Perspectives, 34(1), 34–54. https://doi.org/10.1257/jep.34.1.34
6.Branch. (2022). Annual impact report: AI for financial inclusion. Branch International. https://branch.com/impact
7.Buchak, G., Matvos, G., Piskorski, T., & Seru, A. (2021). Beyond the balance sheet: The rise of FinTech and the transformation of consumer credit. Journal of Financial Economics, 141(3), 920–946. https://doi.org/10.1016/j.jfineco.2021.04.011
8.Demirgüç-Kunt, A., Klapper, L., Singer, D., & Ansar, S. (2022). *The Global Findex Database 2021: Financial inclusion, digital payments, and resilience in the age of COVID-19*. World Bank. https://doi.org/10.1596/978-1-4648-1897-4
9.GSMA. (2022). The state of mobile internet connectivity 2022. GSMA Intelligence. https://www.gsma.com/r/somic/
10.Hardt, M., Price, E., & Srebro, N. (2016). Equality of opportunity in supervised learning. Proceedings of the 30th International Conference on Neural Information Processing Systems, 3315–3323.
11.Jack, W., & Suri, T. (2011). Mobile money: The economics of M-PESA. National Bureau of Economic Research Working Paper No. 16721. https://doi.org/10.3386/w16721
12.Karlan, D., Ratan, A. L., & Zinman, J. (2014). Savings by and for the poor: A research review and agenda. Review of Income and Wealth, 60(1), 36–78. https://doi.org/10.1111/roiw.12101
13.Klapper, L., El-Zoghbi, M., & Hess, J. (2016). Achieving the sustainable development goals: The role of financial inclusion. CGAP. https://www.cgap.org/sites/default/files/Working-Paper-Achieving-Sustainable-Development-Goals-Apr-2016.pdf
14.López, F., & Rodríguez, C. (2021). Algorithmic redlining in Mexican FinTech: Evidence from indigenous communities. Latin American Journal of Economics, 58(2), 145–172.
15.NITI Aayog. (2021). AI for all: Leveraging artificial intelligence for inclusive growth in India. Government of India. https://www.niti.gov.in/sites/default/files/2021-02/AI-For-All.pdf
16.Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. New York University Press.
17.Ogunlesi, T. (2021, March 15). Data breach at Nigerian lender Carbon exposes 1 million customers. TechCrunch. https://techcrunch.com/2021/03/15/carbon-data-breach-nigeria/
18.O'Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown.
19.Pula. (2023). Parametric insurance impact report 2022. Pula Advisors. https://pula.io/reports/
20.Reserve Bank of India. (2022). Report of the Working Group on Digital Lending. https://rbidocs.rbi.org.in/rdocs/PublicationReport/Pdfs/DLWG1082022.pdf
21.Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson.
22.Schicks, J. (2013). The definition and causes of over-indebtedness: A literature review. CGAP Focus Note, 88, 1–12.
23.Suri, T., & Jack, W. (2016). The long-run poverty and gender impacts of mobile money. Science, 354(6317), 1288–1292. https://doi.org/10.1126/science.aah5309
24.Tala. (2023). 2023 impact report: Expanding credit access with AI. Tala. https://tala.com/impact
25.Wachter, S., Mittelstadt, B., & Floridi, L. (2017). Why a right to explanation of automated decision-making does not exist in the General Data Protection Regulation. International Data Privacy Law, 7(2), 76–99. https://doi.org/10.1093/idpl/ipx005
26.Wachter, S., Mittelstadt, B., & Russell, C. (2018). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. Harvard Journal of Law & Technology, 31(2), 841–887.
27.World Bank. (2022). Financial inclusion and sustainable development goals. World Bank Group. https://www.worldbank.org/en/topic/financialinclusion
28.Zenka. (2022). Behavioral nudges and savings behavior: A randomized controlled trial. Zenka Digital. https://zenka.co.ke/research
29.Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. PublicAffairs.
