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Machine Learning Models for Predicting Professional Disqualification in Peruvian Association Members
Author(s)
Pretel Pretel, Manuel
Chávez Llempén, Yeny
Rodrigues, Paulo Canas
Universidade Federal da Bahia
Date Issued
30 de abril de 2026
Type
Article
Volume
11
Issue
5
Series/Report No.
Data
Start Page
98
End Page
98
ISSN
2306-5729
Abstract
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.