Default is a significant challenge for companies, cooperatives, and associations, especially after the Covid-19 pandemic. This study aimed to create a model for predicting characteristics associated with lateness, profiles, and moments most likely to occur. The model utilized data from over 8,000 members from 2021 to 2023 in a Brazilian Vehicle Protection Association (VPA), employing Artificial Neural Networks (ANN) and Decision Trees, with Cross Validation (k-fold = 5) to assess accuracy, precision, and recall. Both models averaged 77% in these metrics. The analysis revealed valuable insights into default patterns, highlighting a critical period within the first four issued invoices. Based on these results, actions were implemented in collaboration with the collections department, including automated messages, phone calls, and reconciliation efforts. These measures reduced default from 12% to approximately 5.9%, with an estimated monthly revenue increase of 50,000 reais. This study underscores the effectiveness of predictive approaches and interdepartmental collaboration in default management, yielding positive outcomes for the APV. However, it emphasizes the importance of future research on data quality and effective post-sale strategies.