Comparação de Algoritmos de Aprendizado de Máquina para Predição de Pontuação de Crédito

Resumo

ABSTRACT
According to the Central Bank of Brazil, the total value of credit
operations in Brazil reached R$4.2 trillion in May 2021. Financial
institutions must consider the risk of default associated with each
operation. Credit analysis, which evaluates this risk, can be performed
using machine learning algorithms. These algorithms compare
new loan proposals to historical data to estimate the default probability
based on the proposal and proponent characteristics. The
accuracy of the model is critical to the profitability of institutions,
so choosing the right algorithm is crucial. This study compares
the performance of machine learning algorithms on three public
datasets in the task of credit risk estimation. The results show that a
stack of multiple classifiers achieved the highest accuracy at 81.41%,
followed by XGBoost at 80.87% and Regressão Logística at 80.48%.