Financial distress prediction (FDP) is essential for maintaining a stable economy and mitigating systemic financial risks. Statistical and machine learning models have been applied to the FDP problem; however, their performance is limited due to the imbalanced nature of financial data. To address this challenge, we compare the use of resampling techniques and weighted loss functions in enhancing the predictive performance of a gated recurrent unit (GRU) model on a numerical financial dataset. The experiments show that the GRU model with \(\alpha \) -balanced Focal Loss consistently outperforms alternative approaches, achieving an AUC of 0.8104. In terms of time-dependency capturing, three years of historical data are found to be the optimal time range for the GRU model.