Abstract
This study addresses the escalating problem of financial fraud, with a particular focus on credit card fraud, a phenomenon that has skyrocketed due to the increasing prevalence of online transactions. The research aims to strengthen anti-money laundering (AML) systems, thereby improving the detection and prevention of fraudulent transactions. For this study, a Dense Neural Network (DNN) has been developed to predict fraudulent transactions with efficiency and accuracy. The model is based on deep learning, and given the highly unbalanced nature of the dataset, balancing techniques were employed to mitigate the bias towards the minority class and improve performance. The DNN model demonstrated robust performance, generalizability, and reliability, achieving over 99% accuracy across training, validation, and test sets. This indicates the model's potential as a powerful tool in the ongoing fight against financial fraud. The results of this study could have significant implications for the financial sector, corporations, and governments, contributing to safer and more secure financial transactions.
References
- Awoyemi, J. O., Adetunmbi, A. O., & Oluwadare, S. A. (2017). Credit card fraud detection using machine learning techniques: A comparative analysis. 2017 International Conference on Computing Networking and Informatics (ICCNI). http://dx.doi.org/10.1109/ICCNI.2017.8123782
- Narayan, A., Kumar, S. D. M., & Chacko, A. M. (2023). A Review of Financial Fraud Detection in E-Commerce Using Machine Learning. First Online: 24 February 2023. 346 Accesses. Conference paper. http://dx.doi.org/10.1007/978-981-19-7524-0_21
- Ali, A., Abd Razak, S., Othman, S. H., Eisa, T. A. E., Al-Dhaqm, A., Nasser, M., Elhassan, T., et al. (2022). Financial Fraud Detection Based on Machine Learning: A Systematic Literature Review. Applied Sciences, 12(19), 9637. MDPI AG. http://dx.doi.org/10.3390/app12199637
- 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). https://doi.org/10.1109/infoteh.2019.8717766
- Xuan, S., Liu, G., Li, Z., Zheng, L., Wang, S., & Jiang, C. (2018). Random forest for credit card fraud detection. 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC). https://doi.org/10.1109/icnsc.2018.8361343
- Dornadula, V. N., & Geetha, S. (2019). Credit Card Fraud Detection using Machine Learning Algorithms. Procedia Computer Science, 165, 631–641. https://doi.org/10.1016/j.procs.2020.01.057
- John, H., & Naaz, S. (2019). Credit card fraud detection using local outlier factor and isolation forest. Int. J. Comput. Sci. Eng, 7(4), 1060-1064.
- Zadafiya, N., Karasariya, J., Kanani, P., & Nayak, A. (2022). Detecting Credit Card Frauds Using Isolation Forest And Local Outlier Factor-Analytical Insights. In 2022 4th International Conference on Smart Systems and Inventive Technology (ICSSIT) (pp. 1588-1594). IEEE
- Narayanan R, D. (2021). Credit Card Fraud. Kaggle. Available at: https://www.kaggle.com/datasets/dhanushnarayananr/credit-card-fraud
- Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012). Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580. https://doi.org/10.48550/arXiv.1207.0580
- Ioffe, S., & Szegedy, C. (2015, June). Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning (pp. 448-456). pmlr. https://doi.org/10.48550/arXiv.1502.03167
- Keras. Keras API Reference. Available online: https://keras.io/api. Last Accessed 28/3/2023
- Scikit-learn. (2023). Classification Report. Available at https://scikit-learn.org/stable.
- Ting, K.M. (2011). Confusion Matrix. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-30164-8_157
- Hoo, Z.H., Candlish, J., Teare, D., (2017). What is a ROC curve?. Emergency Medicine Journal 34, 357–359.. https://doi.org/10.1136/emermed-2017-206735
Abstract
This study addresses the escalating problem of financial fraud, with a particular focus on credit card fraud, a phenomenon that has skyrocketed due to the increasing prevalence of online transactions. The research aims to strengthen anti-money laundering (AML) systems, thereby improving the detection and prevention of fraudulent transactions. For this study, a Dense Neural Network (DNN) has been developed to predict fraudulent transactions with efficiency and accuracy. The model is based on deep learning, and given the highly unbalanced nature of the dataset, balancing techniques were employed to mitigate the bias towards the minority class and improve performance. The DNN model demonstrated robust performance, generalizability, and reliability, achieving over 99% accuracy across training, validation, and test sets. This indicates the model's potential as a powerful tool in the ongoing fight against financial fraud. The results of this study could have significant implications for the financial sector, corporations, and governments, contributing to safer and more secure financial transactions.
References
- Awoyemi, J. O., Adetunmbi, A. O., & Oluwadare, S. A. (2017). Credit card fraud detection using machine learning techniques: A comparative analysis. 2017 International Conference on Computing Networking and Informatics (ICCNI). http://dx.doi.org/10.1109/ICCNI.2017.8123782
- Narayan, A., Kumar, S. D. M., & Chacko, A. M. (2023). A Review of Financial Fraud Detection in E-Commerce Using Machine Learning. First Online: 24 February 2023. 346 Accesses. Conference paper. http://dx.doi.org/10.1007/978-981-19-7524-0_21
- Ali, A., Abd Razak, S., Othman, S. H., Eisa, T. A. E., Al-Dhaqm, A., Nasser, M., Elhassan, T., et al. (2022). Financial Fraud Detection Based on Machine Learning: A Systematic Literature Review. Applied Sciences, 12(19), 9637. MDPI AG. http://dx.doi.org/10.3390/app12199637
- 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). https://doi.org/10.1109/infoteh.2019.8717766
- Xuan, S., Liu, G., Li, Z., Zheng, L., Wang, S., & Jiang, C. (2018). Random forest for credit card fraud detection. 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC). https://doi.org/10.1109/icnsc.2018.8361343
- Dornadula, V. N., & Geetha, S. (2019). Credit Card Fraud Detection using Machine Learning Algorithms. Procedia Computer Science, 165, 631–641. https://doi.org/10.1016/j.procs.2020.01.057
- John, H., & Naaz, S. (2019). Credit card fraud detection using local outlier factor and isolation forest. Int. J. Comput. Sci. Eng, 7(4), 1060-1064.
- Zadafiya, N., Karasariya, J., Kanani, P., & Nayak, A. (2022). Detecting Credit Card Frauds Using Isolation Forest And Local Outlier Factor-Analytical Insights. In 2022 4th International Conference on Smart Systems and Inventive Technology (ICSSIT) (pp. 1588-1594). IEEE
- Narayanan R, D. (2021). Credit Card Fraud. Kaggle. Available at: https://www.kaggle.com/datasets/dhanushnarayananr/credit-card-fraud
- Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012). Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580. https://doi.org/10.48550/arXiv.1207.0580
- Ioffe, S., & Szegedy, C. (2015, June). Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning (pp. 448-456). pmlr. https://doi.org/10.48550/arXiv.1502.03167
- Keras. Keras API Reference. Available online: https://keras.io/api. Last Accessed 28/3/2023
- Scikit-learn. (2023). Classification Report. Available at https://scikit-learn.org/stable.
- Ting, K.M. (2011). Confusion Matrix. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-30164-8_157
- Hoo, Z.H., Candlish, J., Teare, D., (2017). What is a ROC curve?. Emergency Medicine Journal 34, 357–359.. https://doi.org/10.1136/emermed-2017-206735
Details
| Primary Language | English |
|---|---|
| Subjects | Software and Application Security |
| Journal Section | Research Articles |
| Authors | |
| Early Pub Date | March 10, 2024 |
| Publication Date | March 10, 2024 |
| Submission Date | January 30, 2024 |
| Acceptance Date | March 10, 2024 |
| Published in Issue | Year 2023 Volume: 3 Issue: 1 |
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