Algorithmic Fairness and Financial Inclusion in AI-Driven Credit Markets: A Critical Review

Zhi Zheng; Guoqing Chen

doi:10.47297/wspdecWSP2515-797316.20261004

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Algorithmic Fairness and Financial Inclusion in AI-Driven Credit Markets: A Critical Review

Volume 10, Issue 4

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Algorithmic Fairness and Financial Inclusion in AI-Driven Credit Markets: A Critical Review
Algorithmic Fairness and Financial Inclusion in AI-Driven Credit Markets: A Critical Review
School of Finance, Chengdu Jincheng College, Chengdu, Sichuan, 611731, China
Vol. 10, Issue 4, Pages: 85-88(2026)
Online First：29 July 2026，

Published：30 April 2026
DOI：10.47297/wspdecWSP2515-797316.20261004
Accepted：

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Zheng Zhi,Chen Guoqing.Algorithmic Fairness and Financial Inclusion in AI-Driven Credit Markets: A Critical Review[J].Development Economics of China,2026,10(04):85-88.
DOI：

Zheng Zhi,Chen Guoqing.Algorithmic Fairness and Financial Inclusion in AI-Driven Credit Markets: A Critical Review[J].Development Economics of China,2026,10(04):85-88. DOI： 10.47297/wspdecWSP2515-797316.20261004.

摘要

Abstract

The integration of artificial intelligence into consumer credit markets promises to expand financial inclusion by leveraging alternative data to serve thin-file and unbanked borrowers

yet it simultaneously risks automating and scaling historical discrimination. This paper provides a critical review that synthesizes empirical evidence on algorithmic bias in credit scoring and develops a normative framework for fairness. We identify three primary sources of bias—historical

representation

and measurement—that cause machine learning models to perpetuate inequality even when protected attributes are excluded. To reconcile predictive efficiency with equitable treatment

we propose a three-dimensional fairness framework encompassing formal equality of opportunity

substantive fairness

and procedural transparency. A comparative analysis of regulatory responses in the European Union

United States

Singapore

and China reveals divergent governance models

from mandatory high-risk classification to voluntary principles and state-centric filing systems. Effective governance

we argue

requires embedding fairness constraints into algorithmic design

mandating independent bias audits

strengthening borrowers' right to explanation

and enhancing international coordination. The paper highlights the paradox that AI-driven credit can democratize access or deepen exclusion depending on deliberate institutional choices

and it calls for future research on dynamic fairness metrics and predatory inclusion.

关键词

Keywords

references

Gomber P , Kauffman RJ , Parker C , Weber BW . On the fintech revolution: Interpreting the forces of innovation, disruption, and transformation in financial services . Journal of Management Information Systems . 2018 ; 35 ( 1 ): 220 - 265 .

Berg T , Burg V , Gombovic A , Puri M . On the rise of fintechs: Credit scoring using digital footprints . The Review of Financial Studies . 2020 ; 33 ( 7 ): 2845 - 2897 .

Björkegren D , Grissen D . The potential of digital credit to bank the poor . AEA Papers and Proceedings . 2018 ; 108 : 68 - 71 .

Buchak G , Matvos G , Piskorski T , Seru A . Fintech, regulatory arbitrage, and the rise of shadow banks . Journal of Financial Economics . 2018 ; 130 ( 3 ): 453 - 483 .

Thakor AV . Fintech and banking: What do we know? Journal of Financial Intermediation . 2020 ; 41 : 100833 .

Bartlett R , Morse A , Stanton R , Wallace N . Consumer-lending discrimination in the FinTech era . Journal of Financial Economics . 2022 ; 143 ( 1 ): 30 - 56 .

Yang S , Ma W , Zhu S , Shu A , Zhou Y . Data bias, algorithmic discrimination and the fairness issues of individual credit accessibility . Journal of Accounting Literature . 2025 .

Boustani NM . Artificial intelligence impact on banks clients and employees in an Asian developing country . Journal of Asia Business Studies . 2022 ; 16 ( 2 ): 267 - 278 .

Tantri P . Fintech for the poor: Financial intermediation without discrimination . Review of Finance . 2021 ; 25 ( 2 ): 561 - 593 .

Anil K , Misra A . Artificial intelligence in peer-to-peer lending in India: A cross-case analysis . International Journal of Emerging Markets . 2022 ; 17 ( 4 ): 1085 - 1106 .

Zanin L . Combining multiple probability predictions in the presence of class imbalance to discriminate between potential bad and good borrowers in the peer-to-peer lending market . Journal of Behavioral and Experimental Finance . 2020 ; 25 : 100272 .

Fuster A , Plosser M , Schnabl P , Vickery J . The role of technology in mortgage lending . The Review of Financial Studies . 2019 ; 32 ( 5 ): 1854 - 1899 .

Schwab B , Kriebel J . Mitigating adversarial attacks on transformer models in credit scoring . European Journal of Operational Research . 2025 ; 328 ( 1 ): 309 - 323 .

Kozodoi N , Jacob J , Lessmann S . Fairness in credit scoring: Assessment, implementation and profit implications . European Journal of Operational Research . 2022 ; 315 ( 3 ): 1083 - 1094 .

Dobbie W , Liberman A , Paravisini D , Pathania V . Measuring bias in consumer lending . The Review of Economic Studies . 2021 ; 88 ( 6 ): 2799 - 2832 .

Chen Y , Giudici P , Liu K , Raffinetti E . Measuring fairness in credit ratings . Expert Systems with Applications . 2024 ; 258 : 125184 .

Consumer Financial Protection Bureau . Consumer Financial Protection Circular 2022-03: Adverse action notification requirements in connection with credit decisions based on complex algorithms . 2022 .

European Parliament . Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act) . Official Journal of the European Union . 2024 .

Tigges M , Mestwerdt S , Tschirner S , Mauer R . Who gets the money? A qualitative analysis of fintech lending and credit scoring through the adoption of AI and alternative data . Technological Forecasting and Social Change . 2024 ; 205 : 123491 .

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