Publicação
AI vs. Human Credit Scoring: How Consumer Perceptions of Fairness, Discrimination, and Trust Shape Willingness to Use Credit Scoring Systems
| 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 |
| _version_ | 1868413955090677760 |
|---|---|
| author | Ramos, Francisco José Aça de Matos Nogueira dos |
| author_facet | Ramos, Francisco José Aça de Matos Nogueira dos |
| author_role | author |
| contributor_name_str_mv | Rohden, Simoni Fernanda RUN |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Ramos, Francisco José Aça de Matos Nogueira dos\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Rohden, Simoni Fernanda RUN |
| datacite.creators.creator.creatorName.fl_str_mv | Ramos, Francisco José Aça de Matos Nogueira dos |
| datacite.date.Accepted.fl_str_mv | 2025-10-27T00:00:00Z |
| datacite.date.available.fl_str_mv | 2025-11-06T09:35:22Z |
| datacite.date.embargoed.fl_str_mv | 2025-11-06T09:35:22Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | 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 |
| datacite.titles.title.fl_str_mv | AI vs. Human Credit Scoring: How Consumer Perceptions of Fairness, Discrimination, and Trust Shape Willingness to Use Credit Scoring Systems |
| dc.contributor.none.fl_str_mv | Rohden, Simoni Fernanda RUN |
| dc.creator.none.fl_str_mv | Ramos, Francisco José Aça de Matos Nogueira dos |
| dc.date.Accepted.fl_str_mv | 2025-10-27T00:00:00Z |
| dc.date.available.fl_str_mv | 2025-11-06T09:35:22Z |
| dc.date.embargoed.fl_str_mv | 2025-11-06T09:35:22Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10362/190174 |
| dc.language.none.fl_str_mv | eng |
| dc.rights.cclincense.fl_str_mv | http://creativecommons.org/licenses/by/4.0/ |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.subject.none.fl_str_mv | 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 |
| dc.title.fl_str_mv | AI vs. Human Credit Scoring: How Consumer Perceptions of Fairness, Discrimination, and Trust Shape Willingness to Use Credit Scoring Systems |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_bdcc |
| description | 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. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | masterThesis |
| fulltext.url.fl_str_mv | https://run.unl.pt/bitstreams/22f9ad68-adaf-4ece-aa42-f93703ff20e9/download |
| id | run_9c4dc96b5d01c4c19573e8cf5fb0d513 |
| identifier.url.fl_str_mv | http://hdl.handle.net/10362/190174 |
| instacron_str | unl |
| institution | Universidade Nova de Lisboa |
| instname_str | Universidade Nova de Lisboa |
| language | eng |
| network_acronym_str | run |
| network_name_str | Repositório Institucional da UNL |
| oai_identifier_str | oai:run.unl.pt:10362/190174 |
| organization_str_mv | urn:organizationAcronym:unl |
| person_str_mv | Ramos, Francisco José Aça de Matos Nogueira dos |
| publishDate | 2025 |
| reponame_str | Repositório Institucional da UNL |
| repository_id_str | urn:repositoryAcronym:run |
| service_str_mv | urn:repositoryAcronym:run |
| spelling | engpt_PTThe 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.application/pdfpt_PTAI vs. Human Credit Scoring: How Consumer Perceptions of Fairness, Discrimination, and Trust Shape Willingness to Use Credit Scoring SystemsRamos, Francisco José Aça de Matos Nogueira dosRohden, Simoni FernandaHostingInstitutionOrganizationalRUNe-mailmailto:run@unl.ptrun@unl.ptURNurn:tid:2040740452025-11-06T09:35:22Z2025-10-272025-10-27T00:00:00ZHandlehttp://hdl.handle.net/10362/190174http://purl.org/coar/access_right/c_abf2open accessArtificial IntelligenceCredit Scoring SystemsHuman Decision-MakingConsumer PerceptionsTrustFairnessDiscriminationSDG 8 - Decent work and economic growthSDG 10 - Reduced inequalitiesSDG 16 - Peace, justice and strong institutions5742379 bytesliteraturehttp://purl.org/coar/resource_type/c_bdccmaster thesis2025-10-27http://creativecommons.org/licenses/by/4.0/http://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://run.unl.pt/bitstreams/22f9ad68-adaf-4ece-aa42-f93703ff20e9/download |
| spellingShingle | AI vs. Human Credit Scoring: How Consumer Perceptions of Fairness, Discrimination, and Trust Shape Willingness to Use Credit Scoring Systems Ramos, Francisco José Aça de Matos Nogueira dos 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 |
| status | SINGLETON |
| subject.fl_str_mv | 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 |
| title | AI vs. Human Credit Scoring: How Consumer Perceptions of Fairness, Discrimination, and Trust Shape Willingness to Use Credit Scoring Systems |
| title_full | AI vs. Human Credit Scoring: How Consumer Perceptions of Fairness, Discrimination, and Trust Shape Willingness to Use Credit Scoring Systems |
| title_fullStr | AI vs. Human Credit Scoring: How Consumer Perceptions of Fairness, Discrimination, and Trust Shape Willingness to Use Credit Scoring Systems |
| title_full_unstemmed | AI vs. Human Credit Scoring: How Consumer Perceptions of Fairness, Discrimination, and Trust Shape Willingness to Use Credit Scoring Systems |
| title_short | AI vs. Human Credit Scoring: How Consumer Perceptions of Fairness, Discrimination, and Trust Shape Willingness to Use Credit Scoring Systems |
| title_sort | AI vs. Human Credit Scoring: How Consumer Perceptions of Fairness, Discrimination, and Trust Shape Willingness to Use Credit Scoring Systems |
| topic | 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 |
| topic_facet | 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 |
| url | http://hdl.handle.net/10362/190174 |
| visible | 1 |