Author(s)

Yihan Peng ¹

Affiliation(s)

¹ School of management, HeFei University of Technology, Hefei, Anhui 230000

Corresponding Author

Yihan Peng

ABSTRACT

Machine learning plays an increasingly important role in credit evaluation. Compared with other methods, it can deal with more complex credit evaluation problems and improve the accuracy of prediction results. There are many kinds of research in the field of credit evaluation using machine learning methods. However, most of them combine independent machine learning classifiers, and few studies compare the impact of independent classifiers on the prediction results. In this paper, six different machine learning classifiers are used to do empirical research on credit data, and the binary prediction results of each classifier are analyzed and compared. Through experiments, it is found that the gradient boosting decision tree (GBDT) classifier performs best, with an accuracy of 94%. This study finds out the best method model and gives the binary prediction results to provide the bank decision-makers with a powerful basis for decision-making, thus promoting the construction of personal credit.

KEYWORDS

credit evaluation, machine learning, logistic regression, empirical analysis

CITE THIS PAPER

Yihan Peng. Personal Credit Assessment Based on Machine Learning Methods. Financial Engineering and Risk Management (2021) 4: 1-6. DOI: http://dx.doi.org/10.23977/ferm.2021.040301

REFERENCES

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