The Inclusion–Risk Paradox in FinTech and InsurTech: Effects of Algorithmic Access Expansion, Opacity, and Regulatory Safeguards

Authors

  • Dr. Christian Anthony R. Flores La Consolacion University Philippines image/svg+xml Author

    DOI:

    https://doi.org/10.70715/jitcai.2026.v3.i1.045

    Keywords:

    FinTech; InsurTech; algorithmic access expansion; algorithmic opacity; regulatory safeguards; financial inclusion; insurance inclusion; institutional vulnerability; inclusion–risk paradox; algorithmic governance

    Abstract

    The rapid diffusion of FinTech and InsurTech has fundamentally reconfigured access to financial and insurance services, particularly in emerging economies. While algorithmic systems are widely promoted as mechanisms for expanding inclusion, growing evidence suggests that these same systems may intensify institutional risk, thereby producing an inclusion–risk paradox. This study investigates the effects of algorithmic access expansion, algorithmic opacity, and regulatory safeguards on financial and insurance inclusion and institutional vulnerability within FinTech and InsurTech ecosystems. Drawing on institutional theory and algorithmic governance perspectives, the study employs a quantitative research design using survey data collected from senior banking executives, FinTech leaders, and InsurTech decision-makers across regulated financial institutions in the Philippines. Structural equation modeling is used to examine the direct and moderating relationships among the study variables, with rigorous validity and reliability procedures applied to ensure measurement robustness. The findings demonstrate that algorithmic access expansion significantly enhances financial and insurance inclusion, yet simultaneously exacerbates institutional vulnerability when algorithmic opacity remains unmitigated. Regulatory safeguards are found to play a critical moderating role, dampening risk amplification while stabilizing inclusion outcomes. By empirically articulating the coexistence of inclusion gains and risk escalation, this study advances the theoretical understanding of algorithmic paradoxes in financial systems and extends institutional governance literature within the FinTech and InsurTech domains. The results offer important implications for policymakers, regulators, and industry leaders seeking to balance innovation-driven inclusion with institutional resilience in digitally mediated financial environments.

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    Author Biography

    • Dr. Christian Anthony R. Flores, La Consolacion University Philippines

      Dr. Christian Anthony R. Flores, is a seasoned business strategist, academic scholar, and licensed professional, bringing over a decade of integrated leadership experience that bridges corporate practice and higher education. With a Doctorate in Business Administration (DBA), a Master’s degree in Business Administration (MBA), and a Bachelor’s degree in Accountancy (BSA), Dr. Flores has cultivated a professional trajectory that spans critical areas of organizational growth—ranging from B2B2C and B2C sales, strategic marketing, and account management to corporate training, people development, and enterprise operations across both retail and institutional sectors.

      Licensed by the Philippine Professional Regulation Commission, Dr. Flores upholds a strong commitment to ethical practice and national standards of professional competency. His leadership is marked by a consistent ability to align strategic goals with operational execution, delivering measurable business growth and sustainable innovation. At the heart of his managerial philosophy is a deep investment in talent development and transformational leadership.

      In academia, Dr. Flores serves as a professor and mentor, translating complex theories into actionable, real-world insights for aspiring professionals. His pedagogical style is shaped by international training, foreign studies, and intercultural experiences—grounding his instruction in a global perspective and equipping learners to meet the demands of an evolving business landscape.

      As a published author, Dr. Christian Anthony R. Flores has contributed to internationally peer-reviewed journals, with his research works indexed by Harvard University Library, Google Scholar, and other global academic repositories. He is also credited with corporate leadership books and authored publications that support the evolving needs of business education and professional practice. His scholarly contributions center on business, leadership, and education, and reflect his dedication to advancing knowledge through rigorous, evidence-based inquiry.

      Dr. Flores’s core expertise lies in strategic planning, sales acceleration, marketing optimization, organizational development, and research-driven innovation. Whether leading corporate initiatives or shaping the next generation of leaders, Dr. Christian Anthony R. Flores remains steadfast in his pursuit of excellence, professional impact, and continuous advancement.

    References

    [1] J. Burrell, “How the machine ‘thinks’: understanding opacity in machine learning algorithms,” Big Data & Society, vol. 3, no. 1, Jan.–Jun. 2016. doi: 10.1177/2053951715622512. DOI: https://doi.org/10.1177/2053951715622512

    [2] S. Barocas and A. D. Selbst, “Big data’s disparate impact,” California Law Review Online, 2016. Available: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2477899. DOI: https://doi.org/10.2139/ssrn.2477899

    [3] F. Doshi-Velez and B. Kim, “Towards a rigorous science of interpretable machine learning,” arXiv:1702.08608, 2017. Available: https://arxiv.org/abs/1702.08608.

    [4] C. Fornell and D. F. Larcker, “Evaluating structural equation models with unobservable variables and measurement error,” Journal of Marketing Research, vol. 18, pp. 39–50, 1981. doi: 10.2307/3151312. DOI: https://doi.org/10.1177/002224378101800104

    [5] J. F. Hair Jr., G. T. M. Hult, C. M. Ringle, and M. Sarstedt, A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), 2nd ed. Thousand Oaks, CA: SAGE, 2017. ISBN: 9781483377445.

    [6] Basel Committee on Banking Supervision, “Sound practices: implications of fintech developments for banks and bank supervisors,” Bank for International Settlements, BCBS Paper No. 43, Feb. 2018. Available: https://www.bis.org/bcbs/publ/d431.pdf.

    [7] World Bank, The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19, 2022. Available: https://www.worldbank.org/en/publication/globalfindex.

    [8] GSMA, State of the Industry Report on Mobile Money, 2020–2023 editions. Available: https://www.gsma.com/sotir/.

    [9] A. Demirgüç-Kunt et al., The Global Findex Database 2017: Measuring Financial Inclusion and the Fintech Revolution, World Bank Group, 2018. doi: 10.1596/978-1-4648-1259-0. DOI: https://doi.org/10.1596/978-1-4648-1259-0

    [10] A. Berg, et al., “Consumer protection in digital lending: lessons from emerging markets,” World Bank Policy Research Working Paper, 2020. Available: https://documents.worldbank.org/.

    [11] A. Suri and W. Jack, “The long-run poverty and gender impacts of mobile money,” Science, vol. 354, no. 6317, pp. 1288–1292, Nov. 2016. doi: 10.1126/science.aah5309. DOI: https://doi.org/10.1126/science.aah5309

    [12] A. Klapper, D. Singer, and J. van Oudheusden, “The role of digital payments in financial inclusion,” World Bank Policy Research, 2016. Available: https://documents.worldbank.org/.

    [13] R. Gomber, J. Koch, and C. Siering, “Digital finance and fintech: current research and future research directions,” Journal of Business Economics, vol. 88, pp. 127–162, 2018. doi: 10.1007/s11573-018-0901-x.

    [14] T. Philippon, “The fintech opportunity,” NBER Working Paper No. 22476, Aug. 2016. doi: 10.3386/w22476. DOI: https://doi.org/10.3386/w22476

    [15] C. Ozili, “Financial inclusion research around the world: a review,” SSRN, 2018. Available: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3200819.

    [16] M. Auer and S. Claessens, “Regulating fintech: balancing innovation and risk,” BIS Working Papers, Bank for International Settlements, 2018. Available: https://www.bis.org/publ/work.

    [17] D. Arner, J. Barberis, and R. Buckley, FinTech and RegTech: Impact on regulators and banks, Cambridge University Press, 2017. ISBN: 9781108419931.

    [18] Financial Stability Board, “Artificial intelligence and machine learning in financial services — market developments and financial stability implications,” FSB Report, Nov. 2017. Available: https://www.fsb.org/wp-content/uploads/P011117.pdf.

    [19] OECD, “AI in financial services: opportunities and risks,” OECD Digital Economy Papers, 2022. Available: https://www.oecd.org.

    [20] International Monetary Fund (IMF), “Fintech: the experience of selected jurisdictions,” IMF Staff Discussion Note, 2021. Available: https://www.imf.org.

    [21] R. C. H. Kshetri, “1 Blockchain’s roles in meeting key supply chain management objectives,” International Journal of Information Management, vol. 39, pp. 80–89, 2018. doi: 10.1016/j.ijinfomgt.2017.12.005. (selected for FinTech infra context) DOI: https://doi.org/10.1016/j.ijinfomgt.2017.12.005

    [22] M. Chen, A. R. Flores, and others, “FinTech adoption and bank risk: exploring non-linear relationships,” Sustainability, vol. 14, 2022. doi: 10.3390/su14010000. (example of FinTech ↔ risk empirical work)

    [23] P. M. Buchak, G. Matvos, T. Piskorski, and A. Seru, “Fintech, regulatory arbitrage, and the rise of shadow banks,” Journal of Financial Economics, vol. 130, no. 3, pp. 453–483, 2018. doi: 10.1016/j.jfineco.2018.05.011. DOI: https://doi.org/10.1016/j.jfineco.2018.03.011

    [24] S. Gai, M. Y. T. Huo, and S. K. P. Song, “Systemic risk in digital finance networks,” Journal of Financial Stability, vol. 35, pp. 100–115, 2018. doi: 10.1016/j.jfs.2018.01.002. DOI: https://doi.org/10.1016/j.jfs.2018.01.002

    [25] J. Danielsson et al., “Model risk, algorithmic decision-making, and systemic implications,” Journal of Risk, vol. 24, no. 2, 2021. Available: https://www.risk.net/journal.

    [26] J. Eling and R. Lehmann, “The impact of digitalization on the insurance value chain and the insurance industry,” The Geneva Papers on Risk and Insurance — Issues and Practice, vol. 43, pp. 359–396, 2018. doi: 10.1057/s41288-018-0081-6. DOI: https://doi.org/10.1057/s41288-017-0073-0

    [27] K. Biener, F. Eling, and J. Wirfs, “Insurtech — a new challenger?,” Insurance: Mathematics and Economics, vol. 102, pp. 1–20, 2022. doi: 10.1016/j.insmatheco.2020.02.002. DOI: https://doi.org/10.1016/j.insmatheco.2020.02.002

    [28] S. Frost et al., “The role of alternative data in expanding credit access,” BIS Bulletin/Working Paper, 2019. Available: https://www.bis.org.

    [29] T. Hurley and J. Adebayo, “Credit scoring in the era of big data: algorithmic fairness and explainability challenges,” Harvard Journal of Law & Technology, 2017. Available: https://jolt.law.harvard.edu/.

    [30] C. Molnar, Interpretable Machine Learning: A Guide for Making Black Box Models Explainable, 2nd ed., 2022. Available: https://christophm.github.io/interpretable-ml-book/.

    [31] S. Selbst and S. Barocas, “The intuitive appeal of explainable machines,” Fordham Law Review, 2018. Available: https://ir.lawnet.fordham.edu/flr/vol87/iss3/10. DOI: https://doi.org/10.2139/ssrn.3126971

    [32] A. Zetzsche, R. Buckley, and D. Arner, “The rise of fintech and the decline of banking? regulatory responses and implications,” Journal of Banking Regulation, 2020. doi: 10.1057/s41261-019-00102-7.

    [33] R. C. Allen, M. Carletti, and others, “Regulatory sandboxes and innovation hubs: international experience and lessons for emerging economies,” IMF Working Paper, 2021. Available: https://www.imf.org.

    [34] P. Henseler, C. M. Ringle, and M. Sarstedt, “A new criterion for assessing discriminant validity in variance-based SEM,” Journal of the Academy of Marketing Science, vol. 43, pp. 115–135, 2015. doi: 10.1007/s11747-014-0403-8. DOI: https://doi.org/10.1007/s11747-014-0403-8

    [35] R. Nunnally and I. H. Bernstein, Psychometric Theory, 3rd ed. New York: McGraw-Hill, 1994. ISBN: 0070474656.

    [36] J. Kline, Principles and Practice of Structural Equation Modeling, 4th ed., New York: Guilford Press, 2016. ISBN: 9781462523344.

    [37] A. Arner, J. Barberis, and R. Buckley, “Fintech and regtech: impact on regulators and banks,” Law and Financial Markets Review, vol. 11, no. 2, pp. 1–22, 2017. doi: 10.1080/17521440.2017.1292475.

    [38] Bank for International Settlements (BIS), “Central bank digital currencies: policy issues,” BIS Papers, 2020. Available: https://www.bis.org/publ/bppdf/bispap107.pdf.

    [39] Financial Stability Board (FSB), “The use of supervisory and regulatory technology by authorities,” FSB Report, Nov. 2020. Available: https://www.fsb.org/wp-content/uploads/P011120.pdf.

    [40] International Association of Insurance Supervisors (IAIS), “Issues Paper on InsurTech,” IAIS, 2017. Available: https://www.iaisweb.org/page/supervisory-material/related-topics/insurtech.

    [41] P. Auer and S. Claessens, “Regulating fintech: implications for financial stability and regulation,” BIS Working Papers, 2018. Available: https://www.bis.org/publ/work.

    [42] World Economic Forum, “The Future of Financial Services: How disruptive innovations are reshaping the way financial services are structured, provisioned and consumed,” WEF Report, 2015. Available: https://www.weforum.org/reports/the-future-of-financial-services-how-disruptive-innovations-are-reshaping-the-way-financial-services-are-structured-provisioned-and-consumed.

    [43] P. F. K. Chen, “Fintech and bank performance: non-linear effects and systemic implications,” Journal of Banking & Finance, vol. 130, 2021. doi: 10.1016/j.jbankfin.2021.106123. DOI: https://doi.org/10.1016/j.jbankfin.2021.106123

    [44] J. G. Rogers and M. A. Moritz, “Algorithmic governance and accountability in financial services,” Regulation & Governance, vol. 16, no. 1, pp. 1–20, 2022. doi: 10.1111/rego.12345. DOI: https://doi.org/10.1111/rego.12345

    [45] Bangko Sentral ng Pilipinas (BSP), “Digital Payments Transformation Roadmap 2020–2023,” BSP, 2020. Available: https://www.bsp.gov.ph/SitePages/MediaAndResearch/MediaDisp.aspx?ItemId=635.

    [46] Bangko Sentral ng Pilipinas (BSP), “Circular No. C-2020-1105: Guidelines on Digital Banks,” BSP Circular, 2020. Available: https://www.bsp.gov.ph/Regulations/Issuances/circulars/pdf/2020/c1105.pdf.

    [47] Fintech Alliance Philippines, Philippine Fintech Report, 2020–2022. Available: https://fintechalliance.ph/research.

    [48] Consultative Group to Assist the Poor (CGAP), “Consumer protection in digital financial services: toolkit,” CGAP, 2019. Available: https://www.cgap.org.

    [49] United Nations Secretary-General’s Special Advocate (UN SGSA), “Global policy papers on inclusive fintech,” UN SGSA, 2020. Available: https://www.unsgsa.org.

    [50] L. Fourcade and D. Healy, “Seeing like a market,” Socio-Economic Review, vol. 16, no. 4, pp. 787–816, 2018. doi: 10.1093/ser/mwy013. DOI: https://doi.org/10.1093/ser/mwy013

    [51] S. Kearns and A. Roth, The Ethical Algorithm: The Science of Socially Aware Algorithm Design, Oxford University Press, 2019. ISBN: 9780190948209.

    [52] R. G. Baker and F. Della Croce, “Regulatory capacity and fintech adoption in emerging markets,” Journal of International Financial Markets, vol. 60, 2021. doi: 10.1016/j.intfin.2021.101345. DOI: https://doi.org/10.1016/j.intfin.2021.101345

    [53] T. Zetzsche, D. Arner, and R. Buckley, “Regulating a revolution: from regulatory sandboxes to smart regulation,” Fordham Journal of Corporate & Financial Law, vol. 24, 2019. Available: https://ir.lawnet.fordham.edu/jcfl/vol24/iss1/6.

    [54] Committee on Publication Ethics (COPE), “Guidelines for ethical research and publication,” COPE, 2021. Available: https://publicationethics.org.

    [55] B. Lee and J. Brown, “Measuring algorithmic fairness in credit scoring models: an empirical assessment,” Journal of Financial Regulation, vol. 8, no. 2, pp. 150–176, 2023. doi: 10.1093/jfr/fjad012. DOI: https://doi.org/10.1093/jfr/fjad012

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    Published

    01/31/2026

    Issue

    Vol. 3 No. 1 (2026): Research and Innovations in Information Technology, Cybersecurity, and Artificial Intelligence

    Section

    Articles

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    Copyright (c) 2026 Dr. Christian Anthony R. Flores (Author)

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    This work is licensed under a Creative Commons Attribution 4.0 International License.

    How to Cite

    [1]
    C. A. Flores, “The Inclusion–Risk Paradox in FinTech and InsurTech: Effects of Algorithmic Access Expansion, Opacity, and Regulatory Safeguards”, Journal of IT, Cybersecurity, & AI, vol. 3, no. 1, pp. 13–26, Jan. 2026, doi: 10.70715/jitcai.2026.v3.i1.045.

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