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All Journal International Journal of Informatics and Communication Technology (IJ-ICT)
K. Swanthana
Koneru Lakshmaiah Education Foundation
Computer Science & IT

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An integrated deep neural learning architecture with embedded feature reduction for customer lifetime value prediction in fintech firms G. Jyothi; A. Gopinath; Meenu Baliyan; Rachel Nallathamby; K. Swanthana; Murugesan Sundaram
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 4: December 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i4.pp1893-1901

Abstract

The financial technology (FinTech) sector has witnessed remarkable growth in recent years, significantly transforming the way individuals and businesses enable and handle financial services. Recently, deep learning (DL) models have been beneficial in attaining appropriate data concerning customer lifetime value (CLV) for the banking experts. This paper presents a deep neural network (DNN) model with embedded feature selection (FS) for CLVP (DNNEFS-CLVP) approach. An embedded FS mechanism is primarily introduced to automatically identify the most informative attributes while eliminating redundant and less relevant data features. To further enhance predictive capability, a deep hybrid architecture is developed by integrating the temporal convolutional network, a variational graph autoencoder, and a graph convolutional network. This combined framework is expected to increase the accuracy and robustness of CLV prediction while assuring efficient feature utilization. The experimental result values of the DNNEFS-CLVP method accomplishes higher proportion of very low error values across all evaluation metrics, as evidenced by mean squared error (MSE) of 0.000081, mean absolute error (MAE) of 0.007126, root mean squared error (RMSE) of 0.009011, and mean absolute percentage error (MAPE) of 0.114715, indicating strong predictive performance and minimal deviation from actual CLV values.
Adversarial-resilient iot-enabled financial intelligence system using explainable deep learning for sentiment-driven stock trading decisions Suresh Talamala; Sukhada Aloni; J. Vijayalakshmi; Subham Pankaj Samantaray; K. Swanthana; Nisarg Gandhewar
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 4: December 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i4.pp1944-1953

Abstract

Financial stock market trend decision systems support the investment decisions by predicting future price movements and analyzing large-scale market data. Despite, IoT enabled trading systems continuously collects and exchange sentiment and market information are exposed to adversarial attacks, which misleads automated trading algorithms and results in inaccurate investment decision systems. To face this challenge, this study proposes an adversarial-resilient explainable artificial intelligence for sentiment-driven stock trading (ARXAI-SST) architecture. The proposed framework combines graph neural networks (GNN) for adversarial attack detection, FinBERT based sentiment representation, BiLSTM with attention (BiLSTM-Att) for the sentiment analysis, and SHAP-based explainable artificial intelligence for enhancing model interpretability. The experimentation outcomes show that the proposed framework accomplishes higher accuracy and robustness compared with the existing approaches while providing transparent insights into the trading decisions. The proposed model offers an efficient and reliable solution for secure sentiment driven stock trading in IoT enabled financial environments.
Co-Authors A. Gopinath G. Jyothi J. Vijayalakshmi Meenu Baliyan Murugesan Sundaram Nisarg Gandhewar Rachel Nallathamby Subham Pankaj Samantaray Sukhada Aloni Suresh Talamala