## 2 Related Works  
The underlying models chosen for this study are SRN (simple recurrent network) [3], LSTM (long short-term memory) [4], and GRU (gate recurrent unit) [5]. The SRN proposed by [PERSON] is an recurrent neural network (RNN) with three layers, only one of which is the hidden layer, which may face the problem of gradient disappearance or gradient explosion when dealing with complex long-term dependencies, resulting in degraded model performance. In 1997, [PERSON] et al. proposed LSTM, a special type of RNN, which effectively controls information flow and memory updates through a gating mechanism, overcomes the problem of gradient disappearance and gradient explosion, can better handle long-term dependencies, and has contributed to many breakthroughs in natural language processing, such as anomaly detection [6], text state representation [7], multi-label document classification [8], etc. In 2014, [PERSON] et al. proposed GRU, which has a simpler structure and fewer parameters, converges faster, takes less time compared to LSTM, and can speed up the iterative process. The online payment model of e-commerce has increased the risk of online fraud. Due to the increase in fraud rate, researchers have started to use various machine learning methods to detect and analyse fraud in online transactions. [PERSON] et al. proposed a novel fraud detection method [9] for streaming transaction data with the aim of analysing the details of customers' past transactions and extracting behavioural patterns. Cardholders are clustered into different groups based on the amount of their transactions. The transaction information of cardholders in different groups is then aggregated using a sliding window strategy to extract behavioural patterns for each group separately, using features such as maximum, minimum, and average transaction amounts. The different classifiers are then trained separately to extract the fraud characteristics, and the classifier with the better scores is selected as the best fraud prediction method. However, this method has some drawbacks. In credit card fraud detection, fraudulent transactions are usually very rare, so the dataset usually suffers from category imbalance, i.e., the number of normal transaction samples far exceeds the number of fraudulent transaction samples. This can lead to poor performance of the model in detecting fraudulent transactions. Extracting useful features from the raw transaction data is also a challenge. Proper feature selection and construction is critical to the performance of the model. Improper feature selection can result in the model failing to capture key characteristics of fraudulent transactions. [PERSON] et al. used various machine learning and deep learning methods to detect credit card fraud [10], different algorithms such as naive Bayes, logistic regression, K-nearest neighbour (KNN), random forest, and sequential convolutional neural network are used to train other standard and unusual transaction features to detect credit card fraud. All algorithms go through data collection, data preprocessing, data analysis, training with different classifiers separately, and data testing before generating classifiers. In the preprocessing phase, the data are converted into a usable format using a mixture of undersampling (negative class) and oversampling (positive class) techniques. In the training phase, the preprocessed data are fed into the classifier and the test data are evaluated to assess the accuracy of fraud detection, and different models are evaluated based on accuracy and best performance. The results show that KNN performs best. However, the disadvantage of this approach is that the initialization of the weights is very random, which can affect the training process. Fraud is dynamic and patternless, and therefore not easy to detect. [PERSON] et al.