IJCOPE Journal

UGC Logo DOI / ISO Logo

International Journal of Creative and Open Research in Engineering and Management

A Peer-Reviewed, Open-Access International Journal Supporting Multidisciplinary Research, Digital Publishing Standards, DOI Registration, and Academic Indexing.
Journal Information
ISSN: 3108-1754 (Online)
Crossref DOI: Available
ISO Certification: 9001:2015
Publication Fee: 599/- INR
Compliance: UGC Journal Norms
License: CC BY 4.0
Peer Review: Double Blind
Volume 02, Issue 05

Published on: May 2026

HYBRID LSTM AND ENSEMBLE MODEL FOR CREDIT CARD FRAUD DETECTION

Ruchita Saraf Shravya Sanikere Tejas N G Shivam Savitha G

Department of Computer Science and Engineering RV Institute of Technology and Management Bengaluru India
DOI:https://doi.org/10.55041/ijcope.v2i5.537

Article Status

Plagiarism Passed Peer Reviewed Open Access

Available Documents

Download PDF Review Report

Abstract

Credit card fraud detection remains one of the most challenging tasks in financial machine learning due to extreme class imbalance, often less than 0.2% fraud, and the dynamic sequential nature of legitimate and fraudulent transactions. This study proposes a hybrid model that combines a Long Short-Term Memory (LSTM) network with an attention mechanism and a calibrated Random Forest (RF) classifier. The LSTM captures long-term temporal dependencies in transaction sequences with window size T = 5, while the attention layer dynamically weighs the importance of each time step. Concurrently, the Random Forest processes static features of the most recent transaction and outputs a calibrated probability. A weighted ensemble, Pfinal = 0.6PLSTM + 0.4PRF, fuses the two predictions. Using the publicly available Kaggle credit card dataset with 284,807 transactions and 0.18% fraud, the proposed approach achieves a Precision-Recall AUC of 0.806, a fraud class F1-score of 0.85, and near-perfect precision of 0.98 for fraud cases at the optimal decision threshold of 0.727. Extensive experiments compare the proposed method against baseline models including standalone LSTM, LSTM with attention, and Random Forest. All data statistics, score ranges, and classification metrics are reported exactly as obtained from the simulation environment.

Index Terms—Credit card fraud detection, attention mecha-nism, ensemble methods, imbalanced datasets, LSTM networks, Random Forest classifier.

How to Cite this Paper

Saraf, R., Sanikere, S., G, T. N., Shivam, & G, S. (2026). Hybrid LSTM and Ensemble Model for Credit Card Fraud Detection. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(05). https://doi.org/10.55041/ijcope.v2i5.537

Saraf, Ruchita, et al.. "Hybrid LSTM and Ensemble Model for Credit Card Fraud Detection." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 05, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i5.537.

Saraf, Ruchita,Shravya Sanikere,Tejas G, Shivam, and Savitha G. "Hybrid LSTM and Ensemble Model for Credit Card Fraud Detection." International Journal of Creative and Open Research in Engineering and Management 02, no. 05 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i5.537.

Search & Index

Google Scholar Crossref DOI ResearchGate

References


  1. Jurgovsky, M. Granitzer, K. Ziegler, S. Calabretto, P.-E. Portier, L. He-Guelton, and O. Caelen, “Sequence classification for credit-card fraud detection,” Expert Systems with Applications, vol. 100, pp. 234–245, 2018. Available: https://doi.org/10.1016/j.eswa.2018.01.037

  2. Breiman, “Random Forests,” Machine Learning, vol. 45, pp. 5–32, 2001. Available: https://doi.org/10.1023/A:1010933404324

  3. Benchaji, S. Douzi, B. El Ouahidi, and J. Jaafari, “Enhanced credit card fraud detection based on attention mechanism and LSTM deep model,” Journal of Big Data, vol. 8, p. 151, 2021. Available: https://doi.org/10.1186/s40537-021-00537-8

  4. Kaggle, “Credit Card Fraud Detection Dataset,” Available: https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud

  5. Vaswani et al., “Attention is all you need,” in Advances in Neural Information Processing Systems, 2017, pp. 5998–6008.

  6. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997.

  7. Dal Pozzolo, O. Caelen, R. A. Johnson, and G. Bontempi, “Cali-brating probability with undersampling for unbalanced classification,” in 2015 IEEE Symposium Series on Computational Intelligence, 2015,159–166.

  8. He and E. A. Garcia, “Learning from imbalanced data,” IEEE Transactions on Knowledge and Data Engineering, vol. 21, no. 9, pp. 1263–1284, 2009.

  9. De Luca, C. Caviglione, and A. Merlo, “A hybrid CNN-LSTM approach for credit card fraud detection,” in 2021 IEEE International Conference on Cyber Security and Resilience (CSR), 2021, pp. 1–6.

  10. B. M. Prihatini, I. K. G. D. Putra, and I. M. A. Pradnyana, “Credit card fraud detection using ensemble methods: Random Forest, XGBoost, and LightGBM,” International Journal of Engineering and Emerging Technology, vol. 5, no. 2, pp. 1–6, 2020.

Ethical Compliance & Review Process

  • •All submissions are screened under plagiarism detection.
  • •Review follows editorial policy.
  • •Authors retain copyright.
  • •Peer Review Type: Double-Blind Peer Review
  • •Published on: May 17 2026
CCBYNC

This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. You are free to share and adapt this work for non-commercial purposes with proper attribution.

View License
Back to Volume 02, Issue 05View All Issues Next Article

← Previous Article

Hybrid IOT Based Flood Early Warning System with Adaptive Water Rise Detection

Next Article →

Hybrid XGBoost–LSTM Model for State of Health and Remaining Useful Life Prediction of Lithium-Ion Batteries
Scroll to Top