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Case Study
Online Payment Fraud Detection Using Decission Tree and LSTM Neural Network
Abhinav Ranjan1
Arvind Kumar Jangir2
Krishna Abrol3
Suman Saurav4
1 2 3 4 Department of Computer Science Engineering in Data Science, Dayananda Sagar Academy of Technology and Management, Bengaluru, Karnataka, India.
Published Online: September-October 2025
Pages: 60-65
Cite this article
↗ https://www.doi.org/10.59256/ijsreat.20250505011
References
1. A.V. Oppenheim and R.W. Schafer, Digital Signal Processing, 3rd ed., Prentice Hall, 1975.
2. Hyunyoung Choi and Hal Varian, “Predicting the Present with Google Trends,” Google Inc., 2012.
3. Jeremy Ginsberg et al., “Detecting Influenza Epidemics Using Search Engine Query Data,” Nature, 2009.
4. Ulrike Vosen and Timo Schmidt, “Forecasting Private Consumption: Survey-Based Indicators vs. Google Trends,” RWI, 2011.
5. Alessandro Rovetta, “Reliability of Google Trends for Web Infoveillance During COVID-19,” 2021.
6. Abu Rayhan, “Exploring the Power of Google Trends,” CBECL, 2024.
7. J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques, 3rd ed., Morgan Kaufmann, 2011.
8. L. Breiman, “Random Forests,” Machine Learning, vol. 45, pp. 5–32, 2001.
9. S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Computation, vol. 9, no. 8, 1997.
10. N. V. Chawla et al., “SMOTE: Synthetic Minority Over-sampling Technique,” Journal of Artificial Intelligence Research, 2002.
11. J. Brownlee, “ROC Curves and AUC in Python,” Machine Learning Mastery, 2019.
12. Sharma, A., Gupta, R., Singh, P., “Online Payment Fraud Detection Using Machine Learning,” International Journal of Computer Applications, 2024.
13. Jurgovsky, J., Granitzer, M., Ziegler, K., “Sequence Classification for Credit Card Fraud Detection Using LSTM Networks,” Expert Systems with Applications, 2018.
14. Roy, A., Sunitha, R., Kumar, S.A., “Credit Card Fraud Detection Using Decision Tree and Random Forest Algorithms,” ICCCI, 2018.
15. Nashaat, D., Khorasgani, F., “Hybrid Model for Financial Fraud Detection Using Machine Learning and Deep Learning,” IEEE Access, 2021.
16. Bhattacharyya, S., Jha, S., Tharakunnel, K., Westland, J.C., “Data Mining Approaches for Credit Card Fraud Detection,” Expert Systems with Applications, 2011.
17. Fiore, U., De Santis, A., Perla, F., Zanetti, P., “Credit Card Fraud Detection with Machine Learning: LSTM Neural Networks and Feature Engineering,” Information Sciences, 2019.
18. Roy, S., Chatterjee, S., Dey, N., “A Deep Learning Based Hybrid Approach of Detecting Fraudulent Transactions,” Journal of Information Security and Applications, 2023.
19. Patel, M., Shah, R., “Fine-Tuned LSTM for Credit Card Fraud Detection and Classification,” International Journal of Advanced Computer Science and Applications, 2024.
20. Kumar, S., Das, A., “Online Payment Fraud Detection,” International Journal for Research in Applied Science and Engineering Technology (IJRASET), 2025.
21. Ahmed, K., Rahman, M., “A Fraud Detection System Using Decision Trees Classification in Online Transactions,” International Journal of Computer Science and Information Security, 2023.
22. Singh, V., Agarwal, R., “Detecting Credit Card Fraud Using Machine Learning,” International Journal of Engineering Research & Technology, 2024.
23. Zhou, Y., Li, X., Wang, H., “Securing Tomorrow: A Deep Hybrid Learning Framework Based on Auto-LSTM Algorithm for Fraud Detection and Prevention,” Journal of Financial Crime, 2024.
24. P. Domingos, “A Few Useful Things to Know About Machine Learning,” Communications of the ACM, vol. 55, 2012.
2. Hyunyoung Choi and Hal Varian, “Predicting the Present with Google Trends,” Google Inc., 2012.
3. Jeremy Ginsberg et al., “Detecting Influenza Epidemics Using Search Engine Query Data,” Nature, 2009.
4. Ulrike Vosen and Timo Schmidt, “Forecasting Private Consumption: Survey-Based Indicators vs. Google Trends,” RWI, 2011.
5. Alessandro Rovetta, “Reliability of Google Trends for Web Infoveillance During COVID-19,” 2021.
6. Abu Rayhan, “Exploring the Power of Google Trends,” CBECL, 2024.
7. J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques, 3rd ed., Morgan Kaufmann, 2011.
8. L. Breiman, “Random Forests,” Machine Learning, vol. 45, pp. 5–32, 2001.
9. S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Computation, vol. 9, no. 8, 1997.
10. N. V. Chawla et al., “SMOTE: Synthetic Minority Over-sampling Technique,” Journal of Artificial Intelligence Research, 2002.
11. J. Brownlee, “ROC Curves and AUC in Python,” Machine Learning Mastery, 2019.
12. Sharma, A., Gupta, R., Singh, P., “Online Payment Fraud Detection Using Machine Learning,” International Journal of Computer Applications, 2024.
13. Jurgovsky, J., Granitzer, M., Ziegler, K., “Sequence Classification for Credit Card Fraud Detection Using LSTM Networks,” Expert Systems with Applications, 2018.
14. Roy, A., Sunitha, R., Kumar, S.A., “Credit Card Fraud Detection Using Decision Tree and Random Forest Algorithms,” ICCCI, 2018.
15. Nashaat, D., Khorasgani, F., “Hybrid Model for Financial Fraud Detection Using Machine Learning and Deep Learning,” IEEE Access, 2021.
16. Bhattacharyya, S., Jha, S., Tharakunnel, K., Westland, J.C., “Data Mining Approaches for Credit Card Fraud Detection,” Expert Systems with Applications, 2011.
17. Fiore, U., De Santis, A., Perla, F., Zanetti, P., “Credit Card Fraud Detection with Machine Learning: LSTM Neural Networks and Feature Engineering,” Information Sciences, 2019.
18. Roy, S., Chatterjee, S., Dey, N., “A Deep Learning Based Hybrid Approach of Detecting Fraudulent Transactions,” Journal of Information Security and Applications, 2023.
19. Patel, M., Shah, R., “Fine-Tuned LSTM for Credit Card Fraud Detection and Classification,” International Journal of Advanced Computer Science and Applications, 2024.
20. Kumar, S., Das, A., “Online Payment Fraud Detection,” International Journal for Research in Applied Science and Engineering Technology (IJRASET), 2025.
21. Ahmed, K., Rahman, M., “A Fraud Detection System Using Decision Trees Classification in Online Transactions,” International Journal of Computer Science and Information Security, 2023.
22. Singh, V., Agarwal, R., “Detecting Credit Card Fraud Using Machine Learning,” International Journal of Engineering Research & Technology, 2024.
23. Zhou, Y., Li, X., Wang, H., “Securing Tomorrow: A Deep Hybrid Learning Framework Based on Auto-LSTM Algorithm for Fraud Detection and Prevention,” Journal of Financial Crime, 2024.
24. P. Domingos, “A Few Useful Things to Know About Machine Learning,” Communications of the ACM, vol. 55, 2012.
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