Attention-Based LSTM and Autoencoder Framework for Credit Card Fraud Detection Using SMOTE
Keywords:
Deep neural networks (DNNs), Exploratory data analysis (EDA), K-nearest neighbors (KNN), Long short-term memory (LSTM), Support vector machines (SVM)Abstract
The increase in the adoption of credit cards, especially through online channels, has caused an upsurge in the number of fraudulent activities. This has caused substantial losses to both financial institutions and their clients. Hence, there is a need for the development of efficient credit card fraud detection systems that can accurately detect any potential threats. The current paper suggests a new fraud detection system that combines two different methodologies. It uses autoencoders to carry out anomaly detection alongside a deep learning model that utilizes an attention-based long-short-term memory network. As opposed to the classical approach, which views each transaction as an independent unit, the current model takes into account the sequential nature of the transaction data, allowing it to recognize patterns and behavior of fraudsters. The synthetic minority oversampling technique is used during data preparation to solve the challenge of class imbalance prevalent in fraud datasets. Also, the attention mechanism will be implemented in the LSTM architecture to identify the important transactions involved in the process of fraud detection. The performance of the proposed architecture is analyzed and compared against that of existing machine learning algorithms.
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