Detecting Fraudulent Financial Transactions using Deep Learning and Transaction Log Analysis
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Date
2024-11-17
Journal Title
Journal ISSN
Volume Title
Publisher
International Journal of Information Security, Privacy and Digital Forensics (IJISPDF)
Abstract
The financial sector has seen a significant rise in fraudulent activities, costing businesses billions of naira annually.
Fraudulent transactions often involve unauthorized account access, manipulation of transaction data, or theft, leaving businesses
and consumers vulnerable. This study aims to enhance financial fraud detection using a deep learning approach combining
Bidirectional Long Short-Term Memory (BiLSTM) and Multilayer Perceptron (MLP). The goal is to develop a more efficient
model, especially for analyzing large datasets. Transaction log data from Kaggle, containing diverse fraudulent and non-fraudulent
transaction records worldwide, was used for this experiment. Data preprocessing involved imputation to fill empty entries. The
architectures of BiLSTM, MLP, and the hybrid BiLSTM-MLP were designed for smooth model integration and data pipeline
processes. After preprocessing, the data was input into the BiLSTM model, which reads data in forward and backward directions.
The output was passed to the MLP model layers for predicting the authenticity of transactions. The combined model was
evaluated using accuracy, precision, recall, F1 score, AUC, ROC, and confusion matrix. The standalone BiLSTM model achieved
89% accuracy, while the MLP model scored 92%. The hybrid BiLSTM-MLP also scored 92%, similar to the MLP model. Upon
critical examination, the combined model demonstrated better outcome in making accurate predictions compared to standalone
models.
Keywords: Multiple Layer perceptron; Bidirectional Long-short Term Memory; Deep Learning; Machine Learning; Fraudulent