A hybrid approach to credit risk assessment using bill payment habits data and explainable artificial intelligence

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Tarih

2025

Dergi Başlığı

Dergi ISSN

Cilt Başlığı

Yayıncı

MDPI Publishing

Erişim Hakkı

info:eu-repo/semantics/openAccess

Araştırma projeleri

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Özet

Credit risk is one of the most important issues in the rapidly growing and devel oping finance sector. This study utilized a dataset containing real information about the bill payments of individuals who made transactions with a payment institution operating in Turkey. First, the transactions in the dataset were analyzed based on the bill type and the individual and features reflecting the payment habits were extracted. For the target class, real credit scores generated by the Credit Registry Office for the individuals whose payment habits were extracted were used. The dataset is a multi-class, unbalanced, and alternative dataset. Therefore, the dataset was prepared for the analysis by using data cleaning, feature selection, and sampling techniques. Then, the dataset was classified using various classification and evaluation methods. The best results were obtained with a model consisting of ANOVA F-Test, SMOTE, and Extra Tree algorithms. With this model, 80.49% accuracy, 79.89% precision, and 97.04% UAC rate were obtained. These results are quite efficient for an alternative dataset with 10 classes. This model was transformed into an explainable and interpretable form using LIME and SHAP, which are XAI techniques. This study presents a new hybrid model for credit risk assessment based on a multi-class and imbalanced alternative dataset and machine learning.

Açıklama

Anahtar Kelimeler

Credit Risk Assessment, Machine Learning, Explainable Artificial Intelligence (XAI), Resampling, Local Interpretable Model Agnostic Explanations (LIME), Shapley Additive Explanations (SHAP)

Kaynak

Applied Sciences

WoS Q Değeri

Q3

Scopus Q Değeri

Q1

Cilt

15

Sayı

10

Künye

Bulut, C., & Arslan, E. (2025). A hybrid approach to credit risk assessment using bill payment habits data and explainable artificial intelligence. Applied Sciences, 15(10), pp. 1-28. https://doi.org/10.3390/app15105723