Nowadays, we are witnessing the growth of financial theft cases and thereby a high degree of financial losses concomitant with the growth of IoT technology, as well as development and utilization of this technology in banking and financial fields along with the increase in the volume of transactions. Financial fraud detection represents the challenge of finding anomalies in networks of financial transactions. In general, anomaly detection is the problem of distinguishing between normal data samples and well-defined patterns or signatures with those that do not conform to the expected profiles. Various methods have been introduced to identify, detect and prevent such thefts. This paper presents a LSTM based approach for detecting fraudulent transactions. I express the fraud detection problem as a sequence classification task and employ LSTM based method to incorporate transaction sequences. Then, the results are assessed using two classifier methods, namely random forest and decision tree and Mean Square Error. In the second case, because the data are imbalanced, undersampling and oversampling techniques have been used, after which I employ LSTM neural network and results assessed with MSE. Finally, the results of the two groups were compared with confusion matrix. According to the evaluations, my proposed method with Random forest classifier gives the best results.