Artikel
Machine learning predictivity applied to consumer creditworthiness
Credit risk evaluation has a relevant role to financial institutions, since lending may result in real and immediate losses. In particular, default prediction is one of the most challenging activities for managing credit risk. This study analyzes the adequacy of borrower's classification models using a Brazilian bank's loan database, and exploring machine learning techniques. We develop Support Vector Machine, Decision Trees, Bagging, AdaBoost and Random Forest models, and compare their predictive accuracy with a benchmark based on a Logistic Regression model. Comparisons are analyzed based on usual classification performance metrics. Our results show that Random Forest and Adaboost perform better when compared to other models. Moreover, Support Vector Machine models show poor performance using both linear and nonlinear kernels. Our findings suggest that there are value creating opportunities for banks to improve default prediction models by exploring machine learning techniques.
- Sprache
-
Englisch
- Erschienen in
-
Journal: Future Business Journal ; ISSN: 2314-7210 ; Volume: 6 ; Year: 2020 ; Issue: 1 ; Pages: 1-14 ; Heidelberg: Springer
- Klassifikation
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Management
- Thema
-
Consumer lending
Credit risk
Default prediction
Machine learning
Performance analysis
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Aniceto, Maisa Cardoso
Barboza, Flavio Luiz de Moraes
Kimura, Herbert
- Ereignis
-
Veröffentlichung
- (wer)
-
Springer
- (wo)
-
Heidelberg
- (wann)
-
2020
- DOI
-
doi:10.1186/s43093-020-00041-w
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:43 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Artikel
Beteiligte
- Aniceto, Maisa Cardoso
- Barboza, Flavio Luiz de Moraes
- Kimura, Herbert
- Springer
Entstanden
- 2020
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