JETI

JETI Admin

Abstract

Fraudulent transactions are increasing daily and previous statistical techniques yielded low accuracy and high processing time. Hence, this research employed Stacked Long Short-term memory (LSTM) and artificial neural networks for credit card fraud detection. The dataset for training the model was obtained from Kaggle and pre-processed by filling missing data and removing irrelevant data. The preprocessed dataset was trained using artificial neural network and stacked LSTM. Accuracy, Precision, Recall, F1-measure, and Receiver Operating Characteristics Curve (ROC) metrics were employed to determine the performance of the models. Results show that ANN recorded an accuracy of 99% while stacked LSTM gave an accuracy of 92% but stacked LSTM recorded a lower processing time than ANN. Therefore, ANN performed better than stacked LSTM for credit card fraud detection based on the dataset used for training the models. However, future research should consider acquiring local dataset to make the research indigenous and more suitable for use in Nigeria.

References

[1] Lakshmi S, Kavila SD. (2018). Machine Learning For Credit Card Fraud Detection System. International Journal of Applied Engineering Research, 13(24), 16819–16824.

[2] Dornadula VN, Geetha, S. (2020). ScienceDirect Credit Card Fraud Detection using Machine Learning Algorithms Credit Card Fraud Detection using Machine Learning Algorithms. Procedia Computer Science, 165, 631–641. https://doi.org/10.1016/j.procs.2020.01.057

[3] Khatri S, Arora A, Agrawal AP. (2020). Card Fraud Detection : A Comparison. 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence), 680–683.

[4] Nur-E-Arefin, M (2020). A Comparative Study of Machine Learning Classifiers for Credit Card Fraud Detection. International Journal of Innovative Technology and Interdisciplinary Sciences, 3(1), 395–406.

[5] Goyal, R. (2021). Review on Credit Card Fraud Detection using Data Mining Classification Techniques & Machine Learning Algorithms. SSRN Electronic Journal, 7(2020), 972–976.

[6] Zhao H, You D, Jin Y, Xiaoxin T, Minyi G. (2021). Online Credit Card Fraud Detection: A Hybrid Framework with Big Data Technologies, IEEE TrustCom/BigDataSE/ISPA, PP 1644-1653.

[7] Wang J, Xu M, Wang H, Zhang J. (2021), Classification of Imbalanced Data by Using the SMOTE Algorithm and Locally Linear Embedding, Signal Processing

[8] Brownlee J. (2018). Better Deep Learning - Train Faster, Reduce Overfitting, and Make Better Predictions”. Available: https://machinelearningmastery.com/better-deep-learning/.

[9] Prusti D, Rath SK. (2019). Web service based credit card fraud detection by applying machine learning techniques.

[10] Cravit R. (2021). What is a Decision Tree and How to Make One

[11] Sadgali I, Sael N, Benabbou, F (2019). Fraud detection in credit card transaction using machine learning techniques. March.

[12] Sangeetha S, Selvi T, Reena S. (2022). Credit Card Fraud Detection Using Artificial Neural Network (Ann) Algorithm. International Research Journal of Engineering and Technology (IRJET), Volume: 09 Issue: 07 

[13] Izotova A, Valiullin A. (2021). ScienceDirect Comparison of Poisson process and machine learning algorithms Comparison of Poisson process and machine learning algorithms approach for credit card fraud detection approach for credit card fraud detection. Procedia Computer Science, 186, 721–726. https://doi.org/10.1016/j.procs.2021.04.214

[14] Eldin H, Abd M, Abdou EA. (2019). Machine Learning Techniques for Credit Card Fraud Detection Machine Learning Techniques for Credit Card Fraud Detection. 4(2).

[15] Thennakoon, A, Bhagyani, C, Premadasa, S, Mihiranga, S, & Kuruwitaarachchi, N. (2019). Real-time Credit Card Fraud Detection Using Machine Learning. 2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence), July, 488–493. https://doi.org/10.1109/CONFLUENCE.2019.8776942

[16] Maniraj SP, Saini A, Sarkar SD, Ahmed, S. (2019). IJERT-Credit Card Fraud Detection using Machine Learning and Data Science Credit Card Fraud Detection using Machine Learning and Data Science. International Journal of Engineering Research & Technology (IJERT), 8(09), 110–115.

[17] Trivedi NK, Simaiya S, Sharma SK. (2020). An Efficient Credit Card Fraud Detection Model Based on Machine Learning Methods An Efficient Credit Card Fraud Detection Model Based on Machine Learning Methods Chitkara University Institute of Engineering and Technology , Chitkara. January.

[18] Najadat H, Altiti O. (2020). Credit Card Fraud Detection Based on Machine and Deep Learning. May. https://doi.org/10.1109/ICICS49469.2020.239524

[19] Abakarim Y, Lahby M, Attioui A. (2018). An Efficient Real Time Model For Credit Card Fraud Detection Based On Deep Learning. July 2019. https://doi.org/10.1145/3289402.3289530

[20] Varmedja D, Karanovic M, Sladojevic S, Arsenovic M, Anderla A. (2019). Credit Card Fraud Detection - Machine Learning methods. 2019 18th International Symposium INFOTEH-JAHORINA (INFOTEH), March, 1–5. https://doi.org/10.1109/INFOTEH.2019.8717766

[21] Yousefi N, Alaghband M, Garibay I. (2019). A Comprehensive Survey on Machine Learning Techniques and User Authentication Approaches for Credit Card Fraud Detection arXiv : 1912 . 02629v1 [ cs . LG ] 2 Dec 2019.

PDF