Abstract
Credit risk assessment is the major concerning issue for every financial organization. A simple change in evaluation procedure can have a large influence on the financial market. The impact of machine learning techniques in credit risk assessment is highly influential. The intention of this research is to give insight into ensemble strategies for credit risk assessment and to evaluate with other standalone methods. The paper compares four tree-based classifiers namely—Decision Tree (DT), Random Forest (RF), AdaBoost and XGBoost using three different train-test splits for two publicly available German and Australian datasets. As per the findings, bagging based Random Forest classifier outperforms all the other classifiers for both datasets.
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Notes
- 1.
Staglog (German Credit Data) Data Set—https://archive.ics.uci.edu/ml/datasets/statlog+(german+credit+data).
- 2.
Staglog (Australian Credit Data) Data Set—https://archive.ics.uci.edu/ml/datasets/statlog+(australian+credit+approval).
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Bhattacharya, A., Parui, S.K., Biswas, S.K., Mandal, A. (2023). An Empirical Study on Credit Risk Assessment Using Ensemble Classifiers. In: Chakraborty, B., Biswas, A., Chakrabarti, A. (eds) Advances in Data Science and Computing Technologies. ADSC 2022. Lecture Notes in Electrical Engineering, vol 1056. Springer, Singapore. https://doi.org/10.1007/978-981-99-3656-4_16
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