{"dcterms:modified":"2026-03-26","dcterms:creator":"POLEN DataHub","@type":"ore:ResourceMap","schema:additionalType":"Dataverse OREMap Format v1.0.1","dvcore:generatedBy":{"@type":"schema:SoftwareApplication","schema:name":"Dataverse","schema:version":"6.6","schema:url":"https://github.com/iqss/dataverse"},"@id":"https://datahub.polen.fccn.pt/api/datasets/export?exporter=OAI_ORE&persistentId=https://doi.org/10.57979/P0Y3RS","ore:describes":{"citation:dsDescription":{"citation:dsDescriptionValue":"The dataset comprises a broad range of variables to understand the full picture of consumers’ financial health: family socio-demographics, total income, total expenses, employment information, as well as all credit details. The features considered for the analyses were: socio-demographic characterization (marital status, level of education completed, number of people in the household), the perceived causes for over-indebtedness (from a predetermined pool of causes), and data concerning their economic situation, including the total income and expenses of the household as well as data concerning their credits and debts (amount of the monthly installments for credit cards, housing credit, car credit, personal credit and other types of credit or debts; total monthly installment concerning all credits). Each household is represented by one record (one observation) of the dataset with many features to describe their characteristics and behavior","citation:dsDescriptionDate":"2023-01-01"},"citation:keyword":[{"citation:keywordValue":"Over-indebtedness"},{"citation:keywordValue":"Poverty risk"},{"citation:keywordValue":"Economic austerity"},{"citation:keywordValue":"Credit control"},{"citation:keywordValue":"Artificial intelligence"},{"citation:keywordValue":"Automated machine learning"}],"publication":{"publicationCitation":"Boto Ferreira, M., Costa Pinto, D., Maurer Herter, M., Soro, J., Vanneschi, L., Castelli, M., & Peres, F. (2021). Using artificial intelligence to overcome over-indebtedness and fight poverty. Journal of Business Research, 131, 411-425. [Advanced online publication on 19 October 2020]. https://doi.org/10.1016/j.jbusres.2020.10.035","publicationIDType":"doi","publicationIDNumber":"10.1016/j.jbusres.2020.10.035","publicationURL":"https://doi.org/10.1016/j.jbusres.2020.10.035"},"author":[{"citation:authorName":"Leonardo Vanneschi","citation:authorAffiliation":"NOVA Information Management School","authorIdentifierScheme":"ORCID","authorIdentifier":"0000-0003-4732-3328"},{"citation:authorName":"Diego Costa Pinto","citation:authorAffiliation":"NOVA Information Management School","authorIdentifierScheme":"ORCID","authorIdentifier":"0000-0003-4418-9450"}],"grantNumber":{"citation:grantNumberAgency":"Fundação para Ciência e a Tecnologia","citation:grantNumberValue":"DSAIPA/DS/0113/2019"},"citation:datasetContact":{"citation:datasetContactName":"Costa, Elvira","citation:datasetContactAffiliation":"NOVA Information Management School","citation:datasetContactEmail":"ecosta@novaims.unl.pt"},"dateOfDeposit":"2023-05-22","title":"Consumers financial health","citation:depositor":"Costa, Elvira","language":"Portuguese","@id":"https://doi.org/10.57979/P0Y3RS","@type":["ore:Aggregation","schema:Dataset"],"schema:version":"3.0","schema:name":"Consumers financial health","schema:dateModified":"Fri Jun 16 17:19:03 UTC 2023","schema:datePublished":"2023-06-16","schema:creativeWorkStatus":"RELEASED","dvcore:termsOfUse":"The datasets in this project are confidential, sensitive, and may not be publicly available. They maybe available upon an evaluation of requests.","dvcore:fileTermsOfAccess":{"dvcore:termsOfAccess":"The datasets in this project are confidential, sensitive, and may not be publicly available. access may be granted after an evaluation of the applicant's request. Requests can be sent to this email test@novaims.unl.pt","dvcore:fileRequestAccess":true},"schema:includedInDataCatalog":"POLEN DataHub","schema:isPartOf":{"schema:name":"Data Science and Over-Indebtedness: Use of Artificial Intelligence Algorithms in Credit Consumption and Indebtedness Conciliation in Portugal","@id":"https://datahub.polen.fccn.pt/dataverse/Data_Science_Over-Indebtedness","schema:description":"This is the dataverse collection of the research project \"AICE:Data Science and Over-Indebtedness [Ciência de dados e o sobre-endividamento: uso de algoritmos de inteligência artificial no consumo de crédito e conciliação de endividamento em Portugal].\nUse of Artificial Intelligence Algorithms in Credit Consumption and Indebtedness Conciliation in Portugal In the last decades, credit expanded in Portugal, developing different debtors’ classes and social strata. Despite the decline in unemployment, many families continue to face financial difficulties, and a representative part of the Portuguese population still cannot pay their debts. \nThis project proposes the use of Machine Learning (ML) for developing descriptive and predictive models, to understand the influencing factors of over-indebtedness on Portuguese consumers.\nDescriptive models will be obtained using Unsupervised ML algorithms like Self Organizing Maps and Agglomerative Hierarchical Clustering and will be used for establishing consumer clusters and guidelines for over-indebtedness regulation and consumer financial empowerment.","schema:isPartOf":{"schema:name":"MagIC Research Center, NOVA Information Management School","@id":"https://datahub.polen.fccn.pt/dataverse/_0000000458957593","schema:description":"

This is MagIC - NOVA IMS Dataverse Collection.The Information Management Research Center (MagIC) is the research center of the Nova Information Management School (NOVA IMS), the School of Information Management and Data Science at the NOVA University of Lisbon, one of the most prestigious Portuguese public higher education institutions, where the mission is to serve society at local, regional and global levels, fostering the advancement and understanding between cultures, societies, and people, through the dissemination of knowledge through excellence in teaching, research, and services for the community. Based in Lisbon, MagIC has over 80 researchers organized in 4 research streams - Data Science, Information Systems, Geoinformatics, and Data-driven Marketing - focusing on using information to target the resolution of problems that affect organizations and society, with its activity largely supported by responsible and internationally relevant collaborative partnerships.

","schema:isPartOf":{"schema:name":"POLEN DataHub","@id":"https://datahub.polen.fccn.pt/dataverse/polen","schema:description":"

O Repositório de Dados de Investigação POLEN DataHub é disponibilizado pela FCCN, serviços digitais FCT – Fundação para a Ciência e a Tecnologia, com o objetivo de promover a abertura e a partilha de dados resultantes de investigação financiada, servindo a comunidade científica nacional.

\n

Esta plataforma está alinhada com a prática da Ciência Aberta e com os princípios FAIR para a gestão de dados de investigação, estando integrada nos serviços abrangidos pelo Programa Nacional de Ciência Aberta e Dados Abertos de Investigação (PNCADAI), financiado pela medida RE-C05-i08 – Ciência Mais Digital, no âmbito do Programa de Recuperação e Resiliência (PRR).

\n

O POLEN DataHub está dividido em duas áreas: a área de Coleções Institucionais (dedicada às entidades aderentes) e a Área Geral (dedicada aos beneficiários FCT sem solução primária: repositório de dados institucional, temático ou Coleção Institucional no POLEN DataHub).

"}}},"schema:inLanguage":"en","ore:aggregates":[{"schema:description":"XXX Description to be given after","schema:name":"Support.xlsx","dvcore:restricted":true,"schema:version":1,"dvcore:datasetVersionId":9,"@id":"doi:10.57979/P0Y3RS/DOCJKT","schema:sameAs":"https://datahub.polen.fccn.pt/api/access/datafile/:persistentId?persistentId=doi:10.57979/P0Y3RS/DOCJKT&format=original","@type":"ore:AggregatedResource","schema:fileFormat":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","dvcore:filesize":53635,"dvcore:storageIdentifier":"minio://repositorio-polen:188c4b60db2-f91a4ecc7594","dvcore:currentIngestedName":"Support.tab","dvcore:UNF":"UNF:6:hsMykl6N+P5lGXWulG0k7w==","dvcore:rootDataFileId":-1,"dvcore:checksum":{"@type":"MD5","@value":"a3dad2aaa991dbe31bcef7f2daf910f3"}},{"schema:description":"XXX Description to be given after","schema:name":"deco_2016_2017_ENG.csv","dvcore:restricted":true,"schema:version":1,"dvcore:datasetVersionId":9,"@id":"doi:10.57979/P0Y3RS/FHXNGO","schema:sameAs":"https://datahub.polen.fccn.pt/api/access/datafile/:persistentId?persistentId=doi:10.57979/P0Y3RS/FHXNGO","@type":"ore:AggregatedResource","schema:fileFormat":"text/comma-separated-values","dvcore:filesize":630699,"dvcore:storageIdentifier":"minio://repositorio-polen:188c49c697e-b71f43816a46","dvcore:rootDataFileId":-1,"dvcore:checksum":{"@type":"MD5","@value":"413e6261f8e32ee7b2401d38d1397f02"}}],"schema:hasPart":["doi:10.57979/P0Y3RS/DOCJKT","doi:10.57979/P0Y3RS/FHXNGO"]},"@context":{"author":"http://purl.org/dc/terms/creator","authorIdentifier":"http://purl.org/spar/datacite/AgentIdentifier","authorIdentifierScheme":"http://purl.org/spar/datacite/AgentIdentifierScheme","citation":"https://dataverse.org/schema/citation/","dateOfDeposit":"http://purl.org/dc/terms/dateSubmitted","dcterms":"http://purl.org/dc/terms/","dvcore":"https://dataverse.org/schema/core#","grantNumber":"https://schema.org/sponsor","language":"http://purl.org/dc/terms/language","ore":"http://www.openarchives.org/ore/terms/","publication":"http://purl.org/dc/terms/isReferencedBy","publicationCitation":"http://purl.org/dc/terms/bibliographicCitation","publicationIDNumber":"http://purl.org/spar/datacite/ResourceIdentifier","publicationIDType":"http://purl.org/spar/datacite/ResourceIdentifierScheme","publicationURL":"https://schema.org/distribution","schema":"http://schema.org/","title":"http://purl.org/dc/terms/title"}}