Forecasting financial distress in brazilian healthcare cooperatives: a regularized Logit approach

Abstract

Brazil's Supplemental Health sector includes a significant presence of health cooperatives, which account for approximately 38% of the market. These cooperatives face unique financial challenges from their regulatory complexity, mutualistic nature, and the conflict between cooperative principles and competitive pressures for economic efficiency. Despite their importance, the literature on insolvency prediction for cooperatives is scarce, largely relying on models designed for conventional firms. To fill this gap, this paper makes use of a comprehensive database from the National Supplemental Health Agency (ANS) and the Brazilian Cooperative Organization System (Sistema OCB) and employs a logistic regression with Lasso regularization to forecast financial distress in healthcare cooperatives. This approach simultaneously promotes variable selection and model regularization, while maintaining its interpretability. Overall classification performance is robust, with an accuracy of 94% and a specificity of 95%, successfully minimizing false negatives. Academically, applying Lasso regularization in this context is a significant methodological contribution, filling a literature gap by integrating advanced statistics with the unique challenges of health cooperative management. Practically, the model serves as an early warning system for managers and regulators, guiding financial strategies and risk management practices to enhance the long-term sustainability of these entities within the Brazilian healthcare system.