Assembled Feature Selection for Credit Scoring in Microfinance with Non-traditional Features-Reference-Cited by-同舟云学术

Assembled Feature Selection for Credit Scoring in Microfinance with Non-traditional Features

Published:2020 Issue: Volume: Page:207-216
ISSN:0302-9743
Container-title:Discovery Science
language:
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Author:

Ruiz Saulo,Gomes Pedro,Rodrigues Luís,Gama João

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-61527-7_14

Reference19 articles.

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2. Biçer, I., Sevis, D., Bilgic, T.: Bayesian credit scoring model with integration of expert knowledge and customer data. In: International Conference 24th Mini EURO Conference “Continuous Optimization and Information-Based Technologies in the Financial Sector” (MEC EurOPT 2010), pp. 324–329. Vilnius Gediminas Technical University Publishing House “Technika” (2010)

3. Bjorkegren, D., Grissen, D.: Behavior revealed in mobile phone usage predicts loan repayment (2015)

4. Blanco, A., Pino-Mejías, R., Lara, J., Rayo, S.: Credit scoring models for the microfinance industry using neural networks: evidence from Peru. Expert Syst. Appl. 40(1), 356–364 (2013)

5. De Cnudde, S., et al.: Who cares about your Facebook friends? Credit scoring for microfinance. Technical report (2015)

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