Aplicação de técnicas de machine learning para a classificação de crédito

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Xehetasun bibliografikoak
Egile nagusia:	Vaz, Jonas
Argitaratze data:	2023
Formatua:	Bachelor thesis
Hizkuntza:	por
Baliabidea:	Repositório Institucional da UPF
Download full:	https://repositorio.upf.br/handle/123456789/7485
Gaia:	This article aims to implement machine learning algorithms to analyze a set of historical data on loan concessions. The objective is to evaluate whether the machine learning approach in the area of credit granting can be an efficient practice. A comparison of different supervised algorithms to determine credit rating/approval performance was performed using, for this purpose, a dataset obtained from Kaggle, containing historical records of credit approvals. We recognize that credit approval is a complex process, involving many rules and factors, in addition to the need for manual checks on specific lines. This work demonstrated how machine learning can be applied to improve the performance of credit granting procedures.

Deskribapena
Gaia:	This article aims to implement machine learning algorithms to analyze a set of historical data on loan concessions. The objective is to evaluate whether the machine learning approach in the area of credit granting can be an efficient practice. A comparison of different supervised algorithms to determine credit rating/approval performance was performed using, for this purpose, a dataset obtained from Kaggle, containing historical records of credit approvals. We recognize that credit approval is a complex process, involving many rules and factors, in addition to the need for manual checks on specific lines. This work demonstrated how machine learning can be applied to improve the performance of credit granting procedures.

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