Financial Transactional Fraud Detection using a Hybrid BiLSTM with Attention-Based Autoencoder

Sudharson K; Varsha S; Rajalakshmi S; Rajalakshmi D; Santhiya R

doi:10.54392/irjmt25211

Authors

Sudharson K Department of AIML, R.M.D. Engineering College, Chennai, Tamil Nadu, India. Author https://orcid.org/0000-0002-2923-8990
Varsha S Department of AIML, R.M.D. Engineering College, Chennai, Tamil Nadu, India. Author https://orcid.org/0009-0005-5030-0532
Rajalakshmi S Department of Computer Science (Cyber Security), Velammal Engineering College, Tamil Nadu, India. Author https://orcid.org/0000-0002-2963-9057
Rajalakshmi D Department of Computer Science and Engineering, R.M.D. Engineering College, Chennai, Tamil Nadu, India Author
Santhiya R Department of Computer Science and Engineering, Saveetha Engineering College, Chennai, Tamil Nadu, India Author

DOI:

https://doi.org/10.54392/irjmt25211

Keywords:

Convolutional Autoencoder, Attention Mechanism, Fraudulent Transactions, Anomaly Detection, Feature Extraction

Abstract

In this study, we propose an original hybrid model that consists of a Bidirectional LSTM (BiLSTM) and an Attention-Based Convolutional Autoencoder (CAE) designed for fraud detection in financial transactions. The structure of the model is constructed with three Conv1D layers on the CAE and a dense layer that functions as a bottleneck for effectively squeezing relevant information from the transaction data. The importance of certain http transactions can be highlighted using an attention mechanism which helps the model to concentrate on the important features. These features are further fed into the BiLSTM, where the BiLSTM learns to model the context from both past and future sequences of transactions, thus providing a more complete picture of the transactions. To this extent, the model evaluates the reconstruction losses to label the types of fraudulent transaction activity. The performance of this model is found to be very good as it achieved an accuracy of 97% and a high Area Under the Curve in ROC analysis out of the total 100 percent showcasing the model's ability to correctly classify the non-fraudulent and fraudulent transactions.

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