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Evaluating the fairness of credit scoring models: A literature review on mortgage accessibility for under-reserved populations

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  • Evaluating The Fairness of Credit Scoring Models: A Literature Review On Mortgage Accessibility For Under-reserved Populations
  • Evaluating the fairness of credit scoring models: A literature review on mortgage accessibility for under-reserved populations

Taiwo Inumidun Adegoke 1, *, Onyeka Chrisanctus Ofodile 2, Nneka Adaobi Ochuba 3 and Olatunji Akinrinola 4

1 Independent Researcher, USA.
2 Sanctus Maris Concepts, Nigeria Ltd, Nigeria.
3 Independent Researcher, UK.
4 Independent Researcher, New York, USA.
 
Research Article
GSC Advanced Research and Reviews, 2024, 18(03), 189–199.
Article DOI: 10.30574/gscarr.2024.18.3.0104
DOI url: https://doi.org/10.30574/gscarr.2024.18.3.0104
Received on 01 February 2024; revised on 06 March 2024; accepted on 09 March 2024
 
This literature review critically evaluates the fairness of credit scoring models concerning mortgage accessibility for under-reserved populations. The review scrutinizes a diverse range of scholarly articles, reports, and empirical studies spanning various disciplines, including finance, economics, sociology, and public policy. It examines the methodologies, findings, and limitations of existing research to illuminate the multifaceted dimensions of credit scoring fairness and its implications for mortgage accessibility. Firstly, the review outlines the conceptual framework of credit scoring fairness, emphasizing the importance of equality, transparency, and accountability in credit assessment processes. It explores the evolution of credit scoring models and their impact on mortgage lending practices, particularly for historically marginalized communities such as racial minorities, low-income households, and individuals with limited credit histories. Secondly, the review analyzes the methodologies employed in evaluating the fairness of credit scoring models. It identifies key metrics and statistical techniques used to assess disparities in mortgage approval rates, interest rates, and loan terms across demographic groups. Thirdly, the review synthesizes empirical evidence on the extent and persistence of disparities in mortgage accessibility for under-reserved populations. It highlights systemic barriers, including discriminatory lending practices, redlining, and institutionalized biases embedded within credit scoring models. Fourthly, the review discusses the implications of credit scoring fairness for financial inclusion, social equity, and economic mobility. It underscores the need for innovative policy interventions, industry best practices, and consumer education initiatives to address systemic inequities in mortgage lending and promote inclusive homeownership opportunities for under-reserved, Finally, this literature review offers a comprehensive overview of the fairness of credit scoring models in the context of mortgage accessibility for under-reserved populations. By synthesizing empirical evidence, theoretical frameworks, and policy implications, it contributes to a deeper understanding of the challenges and opportunities in promoting equitable access to homeownership and financial security for all.
 
Credit scoring models; Fairness evaluation; Mortgage accessibility; Under-reserved populations; Literature review; Creditworthiness assessment
 
https://gscarr.gsconlinepress.com/sites/default/files/fulltext_pdf/GSCARR-2024-…

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Taiwo Inumidun Adegoke, Onyeka Chrisanctus Ofodile, Nneka Adaobi Ochuba and Olatunji Akinrinol. Evaluating the fairness of credit scoring models: A literature review on mortgage accessibility for under-reserved populations. GSC Advanced Research and Reviews, 2024, 18(3), 189-199. Article DOI: https://doi.org/10.30574/gscarr.2024.18.3.0104

Copyright © Author(s). All rights reserved. This article is published under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution, and reproduction in any medium or format, as long as appropriate credit is given to the original author(s) and source, a link to the license is provided, and any changes made are indicated.


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