Consumers financial healthdoi:10.57979/P0Y3RSPOLEN DataHub2023-06-163Leonardo Vanneschi; Diego Costa Pinto, 2023, "Consumers financial health", https://doi.org/10.57979/P0Y3RS, POLEN DataHub, V3, UNF:6:MIqR/5By7Zi5KKYVPgtyuQ== [fileUNF]Consumers financial healthdoi:10.57979/P0Y3RSLeonardo VanneschiDiego Costa PintoDSAIPA/DS/0113/2019POLEN DataHubCosta, ElviraCosta, Elvira2023-05-22Over-indebtednessPoverty riskEconomic austerityCredit controlArtificial intelligenceAutomated machine learningThe 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 behaviorThe datasets in this project are confidential, sensitive, and may not be publicly available. They maybe available upon an evaluation of requests.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.ptBoto 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.2020.10.03510.1016/j.jbusres.2020.10.035Boto 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.2020.10.035Support.tab185text/tab-separated-valuesUNF:6:hsMykl6N+P5lGXWulG0k7w==XXX Description to be given afterdeco_2016_2017_ENG.csvXXX Description to be given aftertext/comma-separated-values