Phishing has been one of the most successful attacks in recent years. Criminals are motivated by increasing financial gain and constantly improving their email phishing methods. A key goal, therefore, is to develop effective detection methods to cope with huge volumes of email data. In this paper, a solution using BLSTM neural network and FastText word embeddings has been proposed. The solution uses preprocessing techniques like stop-word removal, tokenization, and padding. Two datasets were used in three experiments: balanced and imbalanced, whereas in the imbalanced dataset, the effect of maximum token size was investigated. Evaluation of the model indicated the best metrics: 99.12% accuracy, 98.43% precision, 99.49% recall, and 98.96% f1-score on the imbalanced dataset. It was compared to an existing solution that uses the DL model and word embeddings. Finally, the model and solution architecture were implemented as a browser plug-in.