Leveraging Machine Learning in Classifying Fraudulent and Legitimate Transactions in Banking Sector
Keywords:
Machine Learning, Fraud Detection, Artificial Neural Network, Imbalanced Dataset, Graphical User Interface
Abstract
ABSTRACT:
This research explores the application of machine learning, specifically the Artificial Neural Network (ANN) algorithm, for the identification of fraudulent and legitimate transactions in the context of a European transaction dataset. The investigation encompasses various phases, including data preprocessing, exploratory data analysis, and the implementation of theSynthetic Minority Oversampling Technique(SMOTE) Tomek technique to address the imbalanced nature of the dataset. Leveraging Python libraries such as pandas, matplotlib, seaborn, and scikit-learn, the study incorporates advanced techniques like Principal Component Analysis (PCA) for dimensionality reduction. The ANN algorithm is chosen for its adaptability to complex patterns and high-dimensional datasets, and a graphical user interface (GUI) using the tkinter library to enhance user interactions with the developed sentiment analysis model. The results of the study demonstrate the efficacy of the proposed sentiment analysis model, with the ANN classifier achieving an impressive accuracy of 97.86%. This outperforms existing studies, such as Asha and Kumar's use of Support Vector Machine (SVM) (85%), K-Nearest Neighbour (KNN) (79%), and ANN (89%), and Yee, Sagadevan, and Malim's Bayesian network classifiers (95%). The model excels in minimizing false positives and false negatives, as indicated by maximized precision, recall, and F1-score metrics for both classes. The confusion matrix reinforces the model's outstanding performance, showcasing its potential for real-world applications in fraud detection within the banking sector. The Study's significance lies in its combination of sophisticated techniques, including feature engineering, algorithm selection, and a user-friendly interface, contributing to advancements in transaction classification and fraud detection in the financial domain.
Keywords: Machine Learning, Fraud Detection, Artificial Neural Network, Imbalanced Dataset, Graphical User Interface
References
References
Aggarwal, C. C., & Aggarwal, C. C. (2015). Data classification (pp. 285-344). Springer International Publishing.
Ahmed, M.H. & Butt, A.H. (2023) Credit Card Fraud Detection in Banks using Machine Learning Algorithms. ScienceOpen Preprints.
Aida A., Mariyam S.S. & Anca R. (2015) Classification with class imbalance problem: A review. Int. J. Advance Soft Compu. Appl, Vol. 5(3)
Alfaiz, N.S. & Fati, S.M. (2022). Enhanced credit card fraud detection model using machine learning. Electronics, 11(4), 662.
Asha, R. B., & Kurma, S. K. (2021). Credit card fraud detection using artificial neural network. Global Transitions Proceedings, 2(1), 35-41.
Aslam, A., & Hussain, A. (2024). A Performance Analysis of Machine Learning Techniques for Credit Card Fraud Detection.
Babu, A. M., & Pratap, A. (2020). Credit card fraud detection using deep learning. In 2020 IEEE Recent Advances in Intelligent Computational Systems (RAICS) (pp. 32-36). IEEE.
Batra, M. & Agrawal, R. (2018). Comparative analysis of decision tree algorithms. In Nature Inspired Computing: Proceedings of CSI 2015 (pp. 31-36). Springer Singapore.
Bin Sulaiman, R., Schetinin, V., & Sant, P. (2022). Review of machine learning approach on credit card fraud detection. Human-Centric Intelligent Systems, 2(1-2), 55-68.
Chawla, N. V., Lazarevic, A., Hall, L. O., & Bowyer, K. W. (2003). SMOTEBoost: Improving prediction of the minority class in boosting. In Knowledge Discovery in Databases: PKDD 2003: 7th European Conference on Principles and Practice of Knowledge Discovery in Databases, Cavtat-Dubrovnik, Croatia, September 22-26, 2003. Proceedings 7 (pp. 107-119). Springer Berlin Heidelberg.
Chuprina, R. (2021). Credit Card Fraud Detection: Top ML Solutions in 2021 - SPD Group Blog. Full-cycle Software Development Solutions. Retrieved
from www.spd.group/machine-learning/credit-card-fraud-detection.
Diksha S. & Neeraj K. (2017) A Review on Machine Learning Algorithms, Tasks and Applications. International Journal of Advanced Dissertation in Computer Engineering & Technology (IJARCET) 6(10)
Dornadula, V. N., & Geetha, S. (2019). Credit card fraud detection using machine learning algorithms. Procedia computer science,165,631-641.doi.org/10.1016/ j.procs.2020.01.057.
El Hlouli, F. Z., Riffi, J., Mahraz, M. A., Yahyaouy, A., El Fazazy, K., & Tairi, H. (2024). Credit Card Fraud Detection: Addressing Imbalanced Datasets with a Multi-phase Approach. SN Computer Science, 5(1), 173.
FATF (2022), Partnering in the Fight Against Financial Crime: Data Protection, Technology and Private Sector Information Sharing, FATF, Paris, France, https://www.fatf-gafi.org/publications/digitaltransformation/documents/partnering-in-the-fightagainst-financial-crime.html.
Gasso, G. (2019). Logisticregression. INSA Rouen-ASI DepartementLaboratory: Saint-Etienne-du-Rouvray, France, pp.1-30.
Gholami, R. and Fakhari, N., (2017). Support vector machine: principles, parameters, and applications. In Handbook of neural computation (515-535). Academic Press.
Gupta, P., Varshney, A., Khan, M. R., Ahmed, R., Shuaib, M., & Alam, S. (2023). Unbalanced Credit Card Fraud Detection Data: A Machine Learning-Oriented Comparative Dissertation of Balancing Techniques. Procedia Computer Science, 218, 2575-2584.
Huang, M. (2020). July. Theory and Implementation of linear regression. In 2020 International conference on computer vision, image and deep learning (CVIDL) ( 210-217). IEEE.
Jonathan, B., Putra, P.H. &Ruldeviyani, Y., (2020), July. Observation imbalanced data text to predict users selling products on female daily with smote, tomek, and smote-tomek. In 2020 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) ( 81-85). IEEE.
Kasanda, E.N. & Phiri, J. (2019). ATM Security: A case Dissertation of Emerging Threats. International Journal of Advanced Studies in Computer Science and Engineering.
Kashif S., Hazrat A., & Zhongshan Z. (2018) Big Data Perspective and Challenges in Next Generation Networks. Future Internet Journal. 2018
Khalid, A. R., Owoh, N., Uthmani, O., Ashawa, M., Osamor, J., &Adejoh, J. (2024). Enhancing credit card fraud detection: An ensemble machine learning approach. Big Data and Cognitive Computing, 8(1), 6.
Kumar M. S., Soundarya V., Kavitha S., Keerthika E. S. & Aswini E. (2019) Credit Card Fraud Detection Using Random Forest Algorithm, 2019 3rd International Conference on Computing and Communications Technologies (ICCCT), 2019,149-153, doi:- 10.1109/ICCCT2.2019.8824930.
Latif, A., Rasheed, A., Sajid, U., Ahmed, J., Ali, N., Ratyal, N.I., Zafar, B., Dar, S.H., Sajid, M. & Khalil, T., (2019). Content-based image retrieval and feature extraction: a comprehensive review. Mathematical problems in engineering, 2019.
Lebichot, B., Paldino, G. M., Siblini, W., He-Guelton, L., Oblé, F., & Bontempi, G. (2021). Incremental learning strategies for credit cards fraud detection. International Journal of Data Science and Analytics, 12(2), 165-174.
Li, H., Gao, W., Xie, J., & Yen, G. G. (2023). Multiobjective bilevel programming model for multilayer perceptron neural networks. Information Sciences, 642, 119031.
Lucian C. (2020) Credit card skimmers explained: How they work and how to avoid them. CSO doi.csoonline.com/article/3530302/credit-card-skimmers-explained-how-they-work-and-how-to-protect-yourself.html
Makki, S., Assaghir, Z., Taher, Y., Haque, R., Hacid, M. S., &Zeineddine, H. (2019). An experimental Dissertation with imbalanced classification approaches for credit card fraud detection. IEEE Access, 7, 93010-93022.
Malik, E.F., Khaw, K.W., Belaton, B., Wong, W.P. & Chew, X. (2022). Credit card fraud detection using a new hybrid machine learning architecture. Mathematics, 10(9), p.1480.
Mittal, S. & Tyagi, S. (2019). January. Performance evaluation of machine learning algorithms for credit card fraud detection. In 2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence) ( 320-324). IEEE.
Mqadi, N. M., Naicker, N., & Adeliyi, T. (2021). Solving misclassification of the credit card imbalance problem using near miss. Mathematical Problems in Engineering, 2021.
Muhammad, I. & Yan, Z. (2015) Supervised Machine Learning Approaches: A Survey. ICTACT Journal on Soft Computing
Najadat, H., Altiti, O., Aqouleh, A. A., & Younes, M. (2020). Credit card fraud detection based on machine and deep learning. In 2020 11th International Conference on Information and Communication Systems (ICICS) (pp. 204-208). IEEE.
Nicola M., Alsafi Z., Sohrabi C., Kerwan A., Al-Jabir A., Iosifidis C., Agha M., & Agha R. (2020) The socio-economic implications of the coronavirus pandemic (COVID-19):Int J Surg.doi: 10.1016/j.ijsu.2020.04.018
Ojha, S. K., & Padmapriya, G. (2024). Integration of ANN classifier for automatic identification system of fake credit card transaction using novel ANN to improve fraud detection efficiency in comparison with SVM. In AIP Conference Proceedings (Vol. 2729, No. 1). AIP Publishing.
Omotehinwa, T. O., & Oyewola, D. O. (2023). Hyperparameter Optimization of Ensemble Models for Spam Email Detection. Applied Sciences (Switzerland), 13(3), 1971. https://doi.org/10.3390/APP13031971
Ozili P. K. (2018) Impact of digital finance on financial inclusion and stability; Borsa Istanbul 18(4),329-340.doi.10.1016/j.bir.2017.12.003
Pisner, D.A. & Schnyer, D.M. (2020). Support vector machine. In Machine learning Academic Press.
Rajora, S., Li, D. L., Jha, C., Bharill, N., Patel, O. P., Joshi, S. & Prasad, M. (2018). A comparative Dissertation of machine learning techniques for credit card fraud detection based on time variance. In 2018 IEEE symposium series on computational intelligence (SSCI) (pp. 1958-1963). IEEE.
Roy, A., Sun, J., Mahoney, R., Alonzi, L., Adams S. & Beling, P. (2018) Deep learning detecting fraud in credit card transactions, 2018 Systems and Information Engineering Design Symposium (SIEDS), 2018, 129-134, doi: 10.1109/SIEDS.2018.8374722
Roy, A., Sun, J., Mahoney, R., Alonzi, L., Adams, S., & Beling, P. (2018). Deep learning detecting fraud in credit card transactions. In 2018 Systems and Information Engineering Design Symposium (SIEDS) (pp. 129-134). IEEE.
Sailusha, R., Gnaneswar, V., Ramesh, R. & Rao, G.R. (2020). May. Credit card fraud detection using machine learning. In 2020 4th international conference on intelligent computing and control systems (ICICCS) (1264-1270). IEEE.
Salekshahrezaee, Z., Leevy, J. L., & Khoshgoftaar, T. M. (2023). The effect of feature extraction and data sampling on credit card fraud detection. Journal of Big Data, 10(1),6.
Saritas, M.M. & Yasar, A. (2019). Performance analysis of ANN and Naive Bayes classification algorithm for data classification. International Journal of Intelligent Systems and Applications in Engineering, 7(2), 88-91.
Sarker, I. H. (2021) Machine Learning: Algorithms, Real-World Applications and Dissertation Directions. SN COMPUT. SCI. 2, (160).doi.10.1007/s42979-021-00592-x
Schmidt, A.F. & Finan, C. (2018). Linear regression and the normality assumption. Journal of clinical epidemiology, 98,146-151.
Schonlau, M. & Zou, R.Y., (2020). The random forest algorithm for statistical learning. The Stata Journal, 20(1), pp.3-29.
Singh, A., Ranjan, R. K., & Tiwari, A. (2022). Credit card fraud detection under extreme imbalanced data: a comparative Dissertation of data-level algorithms. Journal of Experimental & Theoretical Artificial Intelligence, 34(4), 571-598. doi.10.1080/0952813X.2021.1907795.
Smadi B. A. & Min M. (2020) A Critical review of Credit Card Fraud Detection Techniques, 2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), 2020, 0732-0736, doi: 10.1109/UEMCON51285.2020.9298075
Sohony, I., Pratap, R., & Nambiar, U. (2018). Ensemble learning for credit card fraud detection. In Proceedings of the ACM India joint international conference on data science and management of data (pp. 289-294).
Unogwu, O. J., & Filali, Y. (2023). Fraud detection and identification in credit card based on machine learning techniques. Wasit Journal of Computer and Mathematics Science, 2(3), 16-22.
Usmonova, M. (2022). Legal Consequences OF Non-Authenticity of Transactions. Oriental renaissance: Innovative, educational, natural and social sciences, 2(5), 1020-1028.
Van Belle, R., Baesens, B.& De Weerdt, J. (2023). CATCHM: A novel network-based credit card fraud detection method using node representation learning. Decision Support Systems, 164, 113866.
Varmedja, D., Karanovic, M., Sladojevic, S., Arsenovic, M., & Anderla, A. (2019). Credit card fraud detection-machine learning methods. In 2019 18th International Symposium INFOTEH-JAHORINA (INFOTEH) (1-5). IEEE.
Viadinugroho, R. A. (2021). Imbalanced Classification in Python: SMOTE-Tomek Links Method | by Raden Aurelius AndhikaViadinugroho | Towards Data Science. Medium. https://towardsdatascience.com/imbalanced-classification-in-python-smote-tomek-links-method-6e48dfe69bbc
Yang, H. & Li, M., (2022), March. Software Defect Prediction Based on SMOTE-Tomek and XGBoost. In Bio-Inspired Computing: Theories and Applications: 16th International Conference, BIC-TA 2021, Taiyuan, China, December 17–19, 2021, Revised Selected Papers, Part II ( 12-31). Singapore: Springer Singapore.
Yee, O. S., Sagadevan, S. & Malim, N. H. A. H. (2018). Credit card fraud detection using machine learning as data mining technique. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(1-4), 23-27.
Yu X., Li X., Dong Y. & Zheng R. (2020) A Deep Neural Network Algorithm for Detecting Credit Card Fraud, 2020 International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), 2020, 181-183 doi10.1109/ICBAIE49996.2020.00045
Aggarwal, C. C., & Aggarwal, C. C. (2015). Data classification (pp. 285-344). Springer International Publishing.
Ahmed, M.H. & Butt, A.H. (2023) Credit Card Fraud Detection in Banks using Machine Learning Algorithms. ScienceOpen Preprints.
Aida A., Mariyam S.S. & Anca R. (2015) Classification with class imbalance problem: A review. Int. J. Advance Soft Compu. Appl, Vol. 5(3)
Alfaiz, N.S. & Fati, S.M. (2022). Enhanced credit card fraud detection model using machine learning. Electronics, 11(4), 662.
Asha, R. B., & Kurma, S. K. (2021). Credit card fraud detection using artificial neural network. Global Transitions Proceedings, 2(1), 35-41.
Aslam, A., & Hussain, A. (2024). A Performance Analysis of Machine Learning Techniques for Credit Card Fraud Detection.
Babu, A. M., & Pratap, A. (2020). Credit card fraud detection using deep learning. In 2020 IEEE Recent Advances in Intelligent Computational Systems (RAICS) (pp. 32-36). IEEE.
Batra, M. & Agrawal, R. (2018). Comparative analysis of decision tree algorithms. In Nature Inspired Computing: Proceedings of CSI 2015 (pp. 31-36). Springer Singapore.
Bin Sulaiman, R., Schetinin, V., & Sant, P. (2022). Review of machine learning approach on credit card fraud detection. Human-Centric Intelligent Systems, 2(1-2), 55-68.
Chawla, N. V., Lazarevic, A., Hall, L. O., & Bowyer, K. W. (2003). SMOTEBoost: Improving prediction of the minority class in boosting. In Knowledge Discovery in Databases: PKDD 2003: 7th European Conference on Principles and Practice of Knowledge Discovery in Databases, Cavtat-Dubrovnik, Croatia, September 22-26, 2003. Proceedings 7 (pp. 107-119). Springer Berlin Heidelberg.
Chuprina, R. (2021). Credit Card Fraud Detection: Top ML Solutions in 2021 - SPD Group Blog. Full-cycle Software Development Solutions. Retrieved
from www.spd.group/machine-learning/credit-card-fraud-detection.
Diksha S. & Neeraj K. (2017) A Review on Machine Learning Algorithms, Tasks and Applications. International Journal of Advanced Dissertation in Computer Engineering & Technology (IJARCET) 6(10)
Dornadula, V. N., & Geetha, S. (2019). Credit card fraud detection using machine learning algorithms. Procedia computer science,165,631-641.doi.org/10.1016/ j.procs.2020.01.057.
El Hlouli, F. Z., Riffi, J., Mahraz, M. A., Yahyaouy, A., El Fazazy, K., & Tairi, H. (2024). Credit Card Fraud Detection: Addressing Imbalanced Datasets with a Multi-phase Approach. SN Computer Science, 5(1), 173.
FATF (2022), Partnering in the Fight Against Financial Crime: Data Protection, Technology and Private Sector Information Sharing, FATF, Paris, France, https://www.fatf-gafi.org/publications/digitaltransformation/documents/partnering-in-the-fightagainst-financial-crime.html.
Gasso, G. (2019). Logisticregression. INSA Rouen-ASI DepartementLaboratory: Saint-Etienne-du-Rouvray, France, pp.1-30.
Gholami, R. and Fakhari, N., (2017). Support vector machine: principles, parameters, and applications. In Handbook of neural computation (515-535). Academic Press.
Gupta, P., Varshney, A., Khan, M. R., Ahmed, R., Shuaib, M., & Alam, S. (2023). Unbalanced Credit Card Fraud Detection Data: A Machine Learning-Oriented Comparative Dissertation of Balancing Techniques. Procedia Computer Science, 218, 2575-2584.
Huang, M. (2020). July. Theory and Implementation of linear regression. In 2020 International conference on computer vision, image and deep learning (CVIDL) ( 210-217). IEEE.
Jonathan, B., Putra, P.H. &Ruldeviyani, Y., (2020), July. Observation imbalanced data text to predict users selling products on female daily with smote, tomek, and smote-tomek. In 2020 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) ( 81-85). IEEE.
Kasanda, E.N. & Phiri, J. (2019). ATM Security: A case Dissertation of Emerging Threats. International Journal of Advanced Studies in Computer Science and Engineering.
Kashif S., Hazrat A., & Zhongshan Z. (2018) Big Data Perspective and Challenges in Next Generation Networks. Future Internet Journal. 2018
Khalid, A. R., Owoh, N., Uthmani, O., Ashawa, M., Osamor, J., &Adejoh, J. (2024). Enhancing credit card fraud detection: An ensemble machine learning approach. Big Data and Cognitive Computing, 8(1), 6.
Kumar M. S., Soundarya V., Kavitha S., Keerthika E. S. & Aswini E. (2019) Credit Card Fraud Detection Using Random Forest Algorithm, 2019 3rd International Conference on Computing and Communications Technologies (ICCCT), 2019,149-153, doi:- 10.1109/ICCCT2.2019.8824930.
Latif, A., Rasheed, A., Sajid, U., Ahmed, J., Ali, N., Ratyal, N.I., Zafar, B., Dar, S.H., Sajid, M. & Khalil, T., (2019). Content-based image retrieval and feature extraction: a comprehensive review. Mathematical problems in engineering, 2019.
Lebichot, B., Paldino, G. M., Siblini, W., He-Guelton, L., Oblé, F., & Bontempi, G. (2021). Incremental learning strategies for credit cards fraud detection. International Journal of Data Science and Analytics, 12(2), 165-174.
Li, H., Gao, W., Xie, J., & Yen, G. G. (2023). Multiobjective bilevel programming model for multilayer perceptron neural networks. Information Sciences, 642, 119031.
Lucian C. (2020) Credit card skimmers explained: How they work and how to avoid them. CSO doi.csoonline.com/article/3530302/credit-card-skimmers-explained-how-they-work-and-how-to-protect-yourself.html
Makki, S., Assaghir, Z., Taher, Y., Haque, R., Hacid, M. S., &Zeineddine, H. (2019). An experimental Dissertation with imbalanced classification approaches for credit card fraud detection. IEEE Access, 7, 93010-93022.
Malik, E.F., Khaw, K.W., Belaton, B., Wong, W.P. & Chew, X. (2022). Credit card fraud detection using a new hybrid machine learning architecture. Mathematics, 10(9), p.1480.
Mittal, S. & Tyagi, S. (2019). January. Performance evaluation of machine learning algorithms for credit card fraud detection. In 2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence) ( 320-324). IEEE.
Mqadi, N. M., Naicker, N., & Adeliyi, T. (2021). Solving misclassification of the credit card imbalance problem using near miss. Mathematical Problems in Engineering, 2021.
Muhammad, I. & Yan, Z. (2015) Supervised Machine Learning Approaches: A Survey. ICTACT Journal on Soft Computing
Najadat, H., Altiti, O., Aqouleh, A. A., & Younes, M. (2020). Credit card fraud detection based on machine and deep learning. In 2020 11th International Conference on Information and Communication Systems (ICICS) (pp. 204-208). IEEE.
Nicola M., Alsafi Z., Sohrabi C., Kerwan A., Al-Jabir A., Iosifidis C., Agha M., & Agha R. (2020) The socio-economic implications of the coronavirus pandemic (COVID-19):Int J Surg.doi: 10.1016/j.ijsu.2020.04.018
Ojha, S. K., & Padmapriya, G. (2024). Integration of ANN classifier for automatic identification system of fake credit card transaction using novel ANN to improve fraud detection efficiency in comparison with SVM. In AIP Conference Proceedings (Vol. 2729, No. 1). AIP Publishing.
Omotehinwa, T. O., & Oyewola, D. O. (2023). Hyperparameter Optimization of Ensemble Models for Spam Email Detection. Applied Sciences (Switzerland), 13(3), 1971. https://doi.org/10.3390/APP13031971
Ozili P. K. (2018) Impact of digital finance on financial inclusion and stability; Borsa Istanbul 18(4),329-340.doi.10.1016/j.bir.2017.12.003
Pisner, D.A. & Schnyer, D.M. (2020). Support vector machine. In Machine learning Academic Press.
Rajora, S., Li, D. L., Jha, C., Bharill, N., Patel, O. P., Joshi, S. & Prasad, M. (2018). A comparative Dissertation of machine learning techniques for credit card fraud detection based on time variance. In 2018 IEEE symposium series on computational intelligence (SSCI) (pp. 1958-1963). IEEE.
Roy, A., Sun, J., Mahoney, R., Alonzi, L., Adams S. & Beling, P. (2018) Deep learning detecting fraud in credit card transactions, 2018 Systems and Information Engineering Design Symposium (SIEDS), 2018, 129-134, doi: 10.1109/SIEDS.2018.8374722
Roy, A., Sun, J., Mahoney, R., Alonzi, L., Adams, S., & Beling, P. (2018). Deep learning detecting fraud in credit card transactions. In 2018 Systems and Information Engineering Design Symposium (SIEDS) (pp. 129-134). IEEE.
Sailusha, R., Gnaneswar, V., Ramesh, R. & Rao, G.R. (2020). May. Credit card fraud detection using machine learning. In 2020 4th international conference on intelligent computing and control systems (ICICCS) (1264-1270). IEEE.
Salekshahrezaee, Z., Leevy, J. L., & Khoshgoftaar, T. M. (2023). The effect of feature extraction and data sampling on credit card fraud detection. Journal of Big Data, 10(1),6.
Saritas, M.M. & Yasar, A. (2019). Performance analysis of ANN and Naive Bayes classification algorithm for data classification. International Journal of Intelligent Systems and Applications in Engineering, 7(2), 88-91.
Sarker, I. H. (2021) Machine Learning: Algorithms, Real-World Applications and Dissertation Directions. SN COMPUT. SCI. 2, (160).doi.10.1007/s42979-021-00592-x
Schmidt, A.F. & Finan, C. (2018). Linear regression and the normality assumption. Journal of clinical epidemiology, 98,146-151.
Schonlau, M. & Zou, R.Y., (2020). The random forest algorithm for statistical learning. The Stata Journal, 20(1), pp.3-29.
Singh, A., Ranjan, R. K., & Tiwari, A. (2022). Credit card fraud detection under extreme imbalanced data: a comparative Dissertation of data-level algorithms. Journal of Experimental & Theoretical Artificial Intelligence, 34(4), 571-598. doi.10.1080/0952813X.2021.1907795.
Smadi B. A. & Min M. (2020) A Critical review of Credit Card Fraud Detection Techniques, 2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), 2020, 0732-0736, doi: 10.1109/UEMCON51285.2020.9298075
Sohony, I., Pratap, R., & Nambiar, U. (2018). Ensemble learning for credit card fraud detection. In Proceedings of the ACM India joint international conference on data science and management of data (pp. 289-294).
Unogwu, O. J., & Filali, Y. (2023). Fraud detection and identification in credit card based on machine learning techniques. Wasit Journal of Computer and Mathematics Science, 2(3), 16-22.
Usmonova, M. (2022). Legal Consequences OF Non-Authenticity of Transactions. Oriental renaissance: Innovative, educational, natural and social sciences, 2(5), 1020-1028.
Van Belle, R., Baesens, B.& De Weerdt, J. (2023). CATCHM: A novel network-based credit card fraud detection method using node representation learning. Decision Support Systems, 164, 113866.
Varmedja, D., Karanovic, M., Sladojevic, S., Arsenovic, M., & Anderla, A. (2019). Credit card fraud detection-machine learning methods. In 2019 18th International Symposium INFOTEH-JAHORINA (INFOTEH) (1-5). IEEE.
Viadinugroho, R. A. (2021). Imbalanced Classification in Python: SMOTE-Tomek Links Method | by Raden Aurelius AndhikaViadinugroho | Towards Data Science. Medium. https://towardsdatascience.com/imbalanced-classification-in-python-smote-tomek-links-method-6e48dfe69bbc
Yang, H. & Li, M., (2022), March. Software Defect Prediction Based on SMOTE-Tomek and XGBoost. In Bio-Inspired Computing: Theories and Applications: 16th International Conference, BIC-TA 2021, Taiyuan, China, December 17–19, 2021, Revised Selected Papers, Part II ( 12-31). Singapore: Springer Singapore.
Yee, O. S., Sagadevan, S. & Malim, N. H. A. H. (2018). Credit card fraud detection using machine learning as data mining technique. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(1-4), 23-27.
Yu X., Li X., Dong Y. & Zheng R. (2020) A Deep Neural Network Algorithm for Detecting Credit Card Fraud, 2020 International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), 2020, 181-183 doi10.1109/ICBAIE49996.2020.00045
Published
2024-06-30
How to Cite
Abdullahi, M. J. (2024). Leveraging Machine Learning in Classifying Fraudulent and Legitimate Transactions in Banking Sector. Ilorin Journal of Computer Science and Information Technology, 7(1), 40-64. Retrieved from https://iljcsit.com.ng/index.php/ILJCSIT/article/view/103
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Articles
