Author(s)

Shenghao Deng ¹, Shengran Fu ²

Affiliation(s)

¹ School of Mathematics and Systems Science, Guangdong Polytechnic Normal University, Guangzhou, 510450, China
² School of Finance, Jilin University of Finance and Economics, Changchun, 130117, China

Corresponding Author

Shenghao Deng

ABSTRACT

The personal credit business is an integral component of the modern financial framework. The automation of personal credit evaluation and approval processes, facilitated by machine learning technologies, has the potential to significantly enhance the operational efficiency of financial services. Nevertheless, a critical challenge that must be confronted is the imbalance in sample proportions between defaulting and non-defaulting client categories. In light of this issue, the present study introduces a Bagging algorithm that incorporates proportional sampling and employs the stacking approach to reintegrate the Bagging model, aiming to augment the predictive capabilities of the model. This methodology serves a dual purpose: it mitigates the overfitting induced by imbalanced samples through strategic resampling, while also enhancing the accuracy and robustness of the prediction model via the application of model fusion techniques. Empirical data analysis corroborates that the proposed method outperforms several classical prediction models in terms of Area Under the Curve (AUC) scores and demonstrates superior robustness. Furthermore, since the base model within the Bagging algorithm is agnostic to the specific model, it allows for the fitting of flexible and varied connection functions. When the base model utilizes interpretable machine learning methods, it additionally enables the extraction of the significance of each credit feature in relation to the probability of default.

KEYWORDS

Credit Risk, Imbalanced Sample, Bagging, Stacking

CITE THIS PAPER

Shenghao Deng, Shengran Fu, Research on Personal Credit Risk Assessment Model Based on Proportional Bootstrap and Stacking. Financial Engineering and Risk Management (2024) Vol. 7: 77-84. DOI: http://dx.doi.org/10.23977/ferm.2024.070410.

REFERENCES

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