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AI vs. Human Credit Scoring: How Consumer Perceptions of Fairness, Discrimination, and Trust Shape Willingness to Use Credit Scoring Systems

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Resumo:The increasing use of AI-driven credit scoring systems by financial institutions has raised critical concerns about fairness, particularly for low-income consumers. These systems often rely on historical data, which can perpetuate existing biases and exacerbate financial inequalities. This study addresses the gap in the literature by investigating how AI-driven credit scoring influences consumer perceptions, focusing on key factors such as fairness, discrimination, trust, and privacy. Using a quantitative experimental survey design, this research compares consumer responses to credit decisions made by AI systems versus human agents. The findings revealed significant differences in perceptions, with AI systems potentially viewed as more objective but less trustworthy. Insights from this study contribute to reducing bias and improving consumer trust in AI-driven credit systems, offering practical recommendations for financial institutions seeking to create fairer and more inclusive financial environments.
Autores principais:Ramos, Francisco José Aça de Matos Nogueira dos
Assunto:Artificial Intelligence Credit Scoring Systems Human Decision-Making Consumer Perceptions Trust Fairness Discrimination SDG 8 - Decent work and economic growth SDG 10 - Reduced inequalities SDG 16 - Peace, justice and strong institutions
Ano:2025
País:Portugal
Tipo de documento:dissertação de mestrado
Tipo de acesso:acesso aberto
Instituição associada:Universidade Nova de Lisboa
Idioma:inglês
Origem:Repositório Institucional da UNL

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