Artificial intelligence in credit scoring : digitalization in the banking landscape in Germany

Detalhes bibliográficos
Autor(a) principal: Schmitz, Kristina
Data de Publicação: 2023
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.14/41642
Resumo: AI is rapidly transforming markets and challenging old business models. This dissertation examines the AI-readiness of German banks, specifically in credit scoring. For this purpose, three different data types were collected. A literature review shed light on the current credit system in Germany and, as a comparison, in China. Furthermore, expert interviews disclosed the potential chances and risks of AI-driven credit assessments. A quantitative survey complemented the expert opinions with those of potential users. The results indicated that the overall readiness of AI in the German credit sector is relatively low. Experts suggested that drivers to use this technology are risk optimization and cost reduction. The identified main barrier complicating the implementation stems from regulatory requirements. While advancements are low, the collected customer data showed that most survey participants would agree to an AI-driven creditworthiness assessment. A scenario analysis combined all collected insights and demonstrated potential future developments. From a management perspective, German banks need to be faster in their technological transformation, in order to not loose competitiveness in the future.
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spelling Artificial intelligence in credit scoring : digitalization in the banking landscape in GermanyArtificial intelligenceBankingTechnology acceptanceInnovationInteligência artificialBancaAceitação de tecnologiaInovaçãoDomínio/Área Científica::Ciências Sociais::Economia e GestãoAI is rapidly transforming markets and challenging old business models. This dissertation examines the AI-readiness of German banks, specifically in credit scoring. For this purpose, three different data types were collected. A literature review shed light on the current credit system in Germany and, as a comparison, in China. Furthermore, expert interviews disclosed the potential chances and risks of AI-driven credit assessments. A quantitative survey complemented the expert opinions with those of potential users. The results indicated that the overall readiness of AI in the German credit sector is relatively low. Experts suggested that drivers to use this technology are risk optimization and cost reduction. The identified main barrier complicating the implementation stems from regulatory requirements. While advancements are low, the collected customer data showed that most survey participants would agree to an AI-driven creditworthiness assessment. A scenario analysis combined all collected insights and demonstrated potential future developments. From a management perspective, German banks need to be faster in their technological transformation, in order to not loose competitiveness in the future.A IA está a transformar rapidamente os mercados e a desafiar velhos modelos de negócio. Esta dissertação examina a prontidão da AI dos bancos alemães, especificamente na pontuação de crédito. Para este fim, foram recolhidos três tipos de dados diferentes. Uma análise bibliográfica lança luz sobre o actual sistema de crédito na Alemanha e, como comparação, na China. Além disso, entrevistas de peritos revelaram as potenciais hipóteses e riscos das avaliações de crédito orientadas para a gripe aviária. Um inquérito quantitativo complementou as opiniões dos peritos com as dos potenciais utilizadores. Os resultados indicaram que a prontidão geral da AI no sector de crédito alemão é relativamente baixa. Os peritos sugeriram que os factores que impulsionam a utilização desta tecnologia são a optimização do risco e a redução de custos. A principal barreira identificada que complica a implementação deriva de requisitos regulamentares. Embora os avanços sejam baixos, os dados recolhidos dos clientes mostraram que a maioria dos participantes no inquérito concordariam com uma avaliação de solvabilidade orientada para a gripe aviária. Uma análise de cenários combinou todas as percepções recolhidas e demonstrou potenciais desenvolvimentos futuros. De uma perspectiva de gestão, os bancos alemães precisam de ser mais rápidos na sua transformação tecnológica, a fim de não perderem competitividade no futuro.Rajsingh, PeterVeritati - Repositório Institucional da Universidade Católica PortuguesaSchmitz, Kristina2023-07-11T08:07:46Z2023-05-082023-042023-05-08T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10400.14/41642TID:203300041enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-12T17:47:13Zoai:repositorio.ucp.pt:10400.14/41642Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T18:34:18.501261Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Artificial intelligence in credit scoring : digitalization in the banking landscape in Germany
title Artificial intelligence in credit scoring : digitalization in the banking landscape in Germany
spellingShingle Artificial intelligence in credit scoring : digitalization in the banking landscape in Germany
Schmitz, Kristina
Artificial intelligence
Banking
Technology acceptance
Innovation
Inteligência artificial
Banca
Aceitação de tecnologia
Inovação
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
title_short Artificial intelligence in credit scoring : digitalization in the banking landscape in Germany
title_full Artificial intelligence in credit scoring : digitalization in the banking landscape in Germany
title_fullStr Artificial intelligence in credit scoring : digitalization in the banking landscape in Germany
title_full_unstemmed Artificial intelligence in credit scoring : digitalization in the banking landscape in Germany
title_sort Artificial intelligence in credit scoring : digitalization in the banking landscape in Germany
author Schmitz, Kristina
author_facet Schmitz, Kristina
author_role author
dc.contributor.none.fl_str_mv Rajsingh, Peter
Veritati - Repositório Institucional da Universidade Católica Portuguesa
dc.contributor.author.fl_str_mv Schmitz, Kristina
dc.subject.por.fl_str_mv Artificial intelligence
Banking
Technology acceptance
Innovation
Inteligência artificial
Banca
Aceitação de tecnologia
Inovação
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
topic Artificial intelligence
Banking
Technology acceptance
Innovation
Inteligência artificial
Banca
Aceitação de tecnologia
Inovação
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
description AI is rapidly transforming markets and challenging old business models. This dissertation examines the AI-readiness of German banks, specifically in credit scoring. For this purpose, three different data types were collected. A literature review shed light on the current credit system in Germany and, as a comparison, in China. Furthermore, expert interviews disclosed the potential chances and risks of AI-driven credit assessments. A quantitative survey complemented the expert opinions with those of potential users. The results indicated that the overall readiness of AI in the German credit sector is relatively low. Experts suggested that drivers to use this technology are risk optimization and cost reduction. The identified main barrier complicating the implementation stems from regulatory requirements. While advancements are low, the collected customer data showed that most survey participants would agree to an AI-driven creditworthiness assessment. A scenario analysis combined all collected insights and demonstrated potential future developments. From a management perspective, German banks need to be faster in their technological transformation, in order to not loose competitiveness in the future.
publishDate 2023
dc.date.none.fl_str_mv 2023-07-11T08:07:46Z
2023-05-08
2023-04
2023-05-08T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.14/41642
TID:203300041
url http://hdl.handle.net/10400.14/41642
identifier_str_mv TID:203300041
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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