Analysis on Credit Risk Assessment for a Multi-Purpose Cooperative Using Neural Network Algorithm

Machine learning has become a useful tool in improving financial decision-making, especially in predicting credit risk. For multipurpose cooperatives in the Philippines, accurately identifying members who are likely to repay or default on loans is important to maintain financial stability and fairness in lending. This study aimed to compare the performance of four neural network algorithms in credit risk assessment using real-world cooperative data from 2019 to 2025. The models were evaluated based on accuracy, precision, recall, F1 score, and ROC AUC. Results showed that ANN performed the best overall, with an accuracy of 86%, a precision of 70%, a recall of 60%, an F1 score of 65%, and a high ROC AUC of 90%. RNN also showed good results, while CNN, though high in precision, had low recall. Based on the findings, ANN and RNN are recommended for cooperatives as reliable tools to support loan decision-making, helping reduce financial risks while promoting responsible and inclusive lending.