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Part 1: Document Description
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Citation |
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Title: |
Consumers financial health |
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Identification Number: |
doi:10.57979/P0Y3RS |
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Distributor: |
Dataverse Raíz |
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Date of Distribution: |
2023-06-16 |
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Version: |
3 |
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Bibliographic Citation: |
Leonardo Vanneschi; Diego Costa Pinto, 2023, "Consumers financial health", https://doi.org/10.57979/P0Y3RS, Dataverse Raíz, V3, UNF:6:MIqR/5By7Zi5KKYVPgtyuQ== [fileUNF] |
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Citation |
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Title: |
Consumers financial health |
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Identification Number: |
doi:10.57979/P0Y3RS |
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Authoring Entity: |
Leonardo Vanneschi (NOVA Information Management School) |
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Diego Costa Pinto (NOVA Information Management School) |
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Grant Number: |
DSAIPA/DS/0113/2019 |
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Distributor: |
Dataverse Raíz |
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Access Authority: |
Costa, Elvira |
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Depositor: |
Costa, Elvira |
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Date of Deposit: |
2023-05-22 |
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Holdings Information: |
https://doi.org/10.57979/P0Y3RS |
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Study Scope |
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Keywords: |
Computer and Information Science, Over-indebtedness, Poverty risk, Economic austerity, Credit control, Artificial intelligence, Automated machine learning |
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Abstract: |
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 |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Notes: |
The datasets in this project are confidential, sensitive, and may not be publicly available. They maybe available upon an evaluation of requests. |
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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 |
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Other Study Description Materials |
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Related Publications |
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Citation |
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Identification Number: |
10.1016/j.jbusres.2020.10.035 |
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Bibliographic Citation: |
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 |
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File Description--f44 |
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File: Support.tab |
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Notes: |
UNF:6:hsMykl6N+P5lGXWulG0k7w== |
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Label: |
deco_2016_2017_ENG.csv |
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Text: |
XXX Description to be given after |
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Notes: |
text/comma-separated-values |