Machine Learning Models for Predicting Professional Disqualification in Peruvian Association Members
The disqualification of licensed professionals for non-payment of their monthly fees constitutes a significant operational risk to the financial sustainability of professional associations. This problem highlights the need for predictive tools that can anticipate the risk of disqualification and pro...
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| Formato: | Artículo |
| Lenguaje: | inglés |
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2026
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| _version_ | 1881508903654522880 |
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| author | Pretel Pretel, Manuel Chávez Llempén, Yeny Sullon Macalupu, Abel Angel Rodrigues, Paulo Canas López Gonzales, Javier Linkolk Tocto Cano, Esteban |
| author_browse | Chávez Llempén, Yeny López Gonzales, Javier Linkolk Pretel Pretel, Manuel Rodrigues, Paulo Canas Sullon Macalupu, Abel Angel Tocto Cano, Esteban |
| author_facet | Pretel Pretel, Manuel Chávez Llempén, Yeny Sullon Macalupu, Abel Angel Rodrigues, Paulo Canas López Gonzales, Javier Linkolk Tocto Cano, Esteban |
| author_sort | Pretel Pretel, Manuel |
| collection | Producción científica |
| description | The disqualification of licensed professionals for non-payment of their monthly fees constitutes a significant operational risk to the financial sustainability of professional associations. This problem highlights the need for predictive tools that can anticipate the risk of disqualification and protect institutional stability. The main objective of this study was to develop a supervised machine learning model for estimating the risk of disqualification among registered professionals based on historical and contextual variables. An empirical, applied, and quantitative study was conducted by analyzing more than 5.7 million financial records corresponding to 27,964 registered professionals. Multiple supervised classification algorithms, including ensemble models such as CatBoost and XGBoost, were evaluated using stratified cross-validation and class-balancing techniques to address the substantial imbalance in the data. The results indicated that CatBoost performed best (F1-score = 57.96%; AUC = 0.72), whereas XGBoost showed greater stability across cross-validation folds. In conclusion, the model developed supports the timely identification of members at high-risk of disqualification, enabling the implementation of early warning systems and proactive institutional financial management strategies. |
| format | Article |
| id | oai:cris.upeu.edu.pe:20.500.15037/391 |
| institution | UPeU |
| language | English |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| record_format | dspace |
| spelling | oai:cris.upeu.edu.pe:20.500.15037/3912026-11-02T12:57:04ZMachine Learning Models for Predicting Professional Disqualification in Peruvian Association MembersPretel Pretel, ManuelChávez Llempén, YenySullon Macalupu, Abel AngelRodrigues, Paulo CanasLópez Gonzales, Javier LinkolkTocto Cano, Estebaninstitutional riskprofessional associationsprofessional disqualificationpredictive modelingsupervised machine learningsociodemographic variablesThe disqualification of licensed professionals for non-payment of their monthly fees constitutes a significant operational risk to the financial sustainability of professional associations. This problem highlights the need for predictive tools that can anticipate the risk of disqualification and protect institutional stability. The main objective of this study was to develop a supervised machine learning model for estimating the risk of disqualification among registered professionals based on historical and contextual variables. An empirical, applied, and quantitative study was conducted by analyzing more than 5.7 million financial records corresponding to 27,964 registered professionals. Multiple supervised classification algorithms, including ensemble models such as CatBoost and XGBoost, were evaluated using stratified cross-validation and class-balancing techniques to address the substantial imbalance in the data. The results indicated that CatBoost performed best (F1-score = 57.96%; AUC = 0.72), whereas XGBoost showed greater stability across cross-validation folds. In conclusion, the model developed supports the timely identification of members at high-risk of disqualification, enabling the implementation of early warning systems and proactive institutional financial management strategies.Articlehttps://doi.org/10.3390/data1105009810.3390/data11050098https://hdl.handle.net/20.500.15037/391W7160013057enData2306-57292026-04-302026-06-13T08:12:32Z2026-06-13T08:12:32Z |
| spellingShingle | institutional risk professional associations professional disqualification predictive modeling supervised machine learning sociodemographic variables Pretel Pretel, Manuel Chávez Llempén, Yeny Sullon Macalupu, Abel Angel Rodrigues, Paulo Canas López Gonzales, Javier Linkolk Tocto Cano, Esteban Machine Learning Models for Predicting Professional Disqualification in Peruvian Association Members |
| title | Machine Learning Models for Predicting Professional Disqualification in Peruvian Association Members |
| title_full | Machine Learning Models for Predicting Professional Disqualification in Peruvian Association Members |
| title_fullStr | Machine Learning Models for Predicting Professional Disqualification in Peruvian Association Members |
| title_full_unstemmed | Machine Learning Models for Predicting Professional Disqualification in Peruvian Association Members |
| title_short | Machine Learning Models for Predicting Professional Disqualification in Peruvian Association Members |
| title_sort | machine learning models for predicting professional disqualification in peruvian association members |
| topic | institutional risk professional associations professional disqualification predictive modeling supervised machine learning sociodemographic variables |
| topic_facet | institutional risk professional associations professional disqualification predictive modeling supervised machine learning sociodemographic variables |
| url | https://doi.org/10.3390/data11050098 https://hdl.handle.net/20.500.15037/391 |
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