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Original Research

Algorithmic Credit, Digital Financial Literacy, and Institutional Safeguards: Evidence from Digital Lending Adoption in an Emerging Market

Business Fora: Business and Allied Industries International Journal

ISSN Online: 3028-1334 | Print: 3028-1326

Volume 6 | Issue 2 | 2026 | 69 – 84

Christian Anthony R. Flores, DBA, ORCID No. 0009-0004-6054-5798

La Consolacion University Philippines, Capitol View Park Subdivision, Bulihan, City of Malolos, Bulacan, Philippines

Article History:

Initial submission: 26 December 2025
First decision: 28 December 2025
Revision received: 20 January 2026
Accepted for publication: 28 January 2026
Online release: 02 February 2026

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Abstract

The rapid expansion of digital lending platforms and algorithmic credit scoring systems has reshaped access to credit in emerging market economies. While algorithmic credit is widely promoted as a driver of financial inclusion, growing evidence suggests that expanded access does not necessarily translate into improved borrower welfare. This study examines how algorithmic credit adoption influences borrower financial outcomes and investigates the moderating roles of digital financial literacy and institutional safeguards in an emerging market context. Guided by financial inclusion theory, behavioral finance, and institutional governance perspectives, the study employs a quantitative, cross-sectional research design using primary survey data from adult users of digital lending platforms. Descriptive statistics, correlation analysis, multiple regression, and moderation analysis were applied to examine the effects of algorithmic credit adoption on repayment behavior, perceived financial stress, and financial resilience, as well as the conditional roles of borrower capability and governance mechanisms. The results revealed that algorithmic credit adoption is significantly associated with improved repayment behavior and enhanced short-term financial resilience, but also with increased perceived financial stress among borrowers. Importantly, digital financial literacy significantly strengthened positive financial outcomes and mitigates stress-related effects, while institutional safeguards further moderate these relationships by enhancing transparency, accountability, and consumer protection. These findings indicate that the welfare effects of algorithmic credit are conditional rather than uniform. The study contributes to the digital finance and financial inclusion literature by demonstrating that algorithmic credit systems are neither inherently inclusive nor inherently harmful. Instead, their impact depends critically on the interaction between technological adoption, borrower capability, and institutional governance. The findings underscore the importance of integrating digital financial education and robust regulatory safeguards into fintech-driven financial inclusion strategies to promote sustainable and responsible digital lending in emerging markets.

Keywords: algorithmic credit; digital lending; digital financial literacy; financial inclusion; institutional safeguards; fintech governance; borrower financial outcomes; financial resilience; consumer protection; emerging markets

Cite this article

APA (7th edition)

Flores, C. A. R. (2026). Algorithmic credit, digital financial literacy, and institutional safeguards: Evidence from digital lending adoption in an emerging market. Business Fora: Business and Allied Industries International Journal, 6(2), 66–81. https://doi.org/10.62718/vmca.bf-baiij.6.2.SC-1225-021.docx&action=default&mobileredirect=true)

Author contributions

– (Not applicable).

Funding

None declared.

Conflict of interest

The authors declare that the research was conducted without commercial or financial relationships that could be construed as a potential conflict of interest.

Institutional ethics review statement

The study adhered to established ethical standards for social science research. Participation was voluntary, informed consent was obtained, and no personally identifiable information was collected. Data were stored securely and used solely for academic purposes, in accordance with ethical research guidelines.

Data availability statement

All information is contained within the published article.

Declaration of generative AI use/assistance

No AI tools were used in the preparation of this manuscript.

Acknowledgement

– (not  available).

Publisher’s disclaimer

The views expressed in this article are those of the authors and do not necessarily reflect the views of the publisher. The publisher disclaims any responsibility for errors or omissions.

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