Abstract
Machine learning models are an important tool that provide a scientific method to identify potential debtors early and predict which clients are more likely to default on their debts, improving the accuracy of assessment in credit risk analysis in financial companies. The purpose of this study was to analyze the performance of gradient boosting machine learning algorithms (CatBoost, LightGBM, and XGBoost) in predicting customer default risk, and the ability of the RandomUnderSampler sampling technique to address unbalanced categories of credit risk. The exploratory analysis of the data set was carried out, then the data preprocessing, finally the training with hyperparameter adjustments with the GridSearchCV method to identify the largest number of clients with credit risk. The model is evaluated based on metrics of sensitivity, specificity and precision, on a set of consumer credit data. Among the proposed algorithms, XGBoost outperformed the LightGBM and catBoost models. Experimental results confirmed that the XGBoost model performs better for credit risk prediction with historical data.
Original language | English |
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Title of host publication | Intelligent Technologies |
Subtitle of host publication | Design and Applications for Society - Proceedings of CITIS 2022 |
Editors | Vladimir Robles-Bykbaev, Josefa Mula, Gilberto Reynoso-Meza |
Publisher | Springer Science and Business Media Deutschland GmbH |
Pages | 81-95 |
Number of pages | 15 |
ISBN (Print) | 9783031243264 |
DOIs | |
State | Published - 2023 |
Event | 8th International Conference on Science, Technology and Innovation for Society, CITIS 2022 - Guayaquil, Ecuador Duration: 22 Jun 2022 → 24 Jun 2022 |
Publication series
Name | Lecture Notes in Networks and Systems |
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Volume | 607 LNNS |
ISSN (Print) | 2367-3370 |
ISSN (Electronic) | 2367-3389 |
Conference
Conference | 8th International Conference on Science, Technology and Innovation for Society, CITIS 2022 |
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Country/Territory | Ecuador |
City | Guayaquil |
Period | 22/06/22 → 24/06/22 |
Bibliographical note
Publisher Copyright:© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Keywords
- Binary classification
- Credit risk
- Gradient boosting
- Machine learning