FRAUD DETECTION IN BANKING USING MACHINE LEARNING
Keywords:
Machine Learning, Logistic Regression, Decision Trees, Random Forest, K-Nearest NeighboursAbstract
Financial institutions, particularly banks, have a challenge of fraud detection. Fraud poses a substantial financial risk to both institutions and their customers since fraudulent activities can result in significant monetary losses and erode customer trust. Recent research has shown that machine learning techniques can be used to detect fraud in the banking sector.
In this project, we applied logistic regression, random forest, K-Nearest Neighbours, and decision trees to detect fraudulent transactions to the problem of fraud detection in the banking industry. The dataset was obtained from Kaggle and has 31 variables. Logistic regression had the lowest performance metrics with an accuracy of 87.91% while the decision tree had the highest
performance metrics with an accuracy of 97.17%.
References
Smith, A. (2020). Title of Smith's Work. Journal of Banking and Finance, 45(2), 123-135.
Johnson, B., & White, C. (2019). Title of Johnson & White's Work. Banking Journal, 36(4), 567-578.
Brown, D. (2018). Title of Brown's Work. Financial Analytics Review, 12(3), 45-57.
Garcia, E., et al. (2017). Title of Garcia et al.'s Work. Machine Learning in Finance, 28(1), 89-101.
Davis, M. (2016). Title of Davis's Work. Data Security in Banking, 15(4), 321-333.
Robinson, P. (2020). Title of Robinson's Work. Journal of Data Preprocessing, 10(2), 211-223.
Smith, J., & Williams, R. (2018). Title of Smith & Williams's Work. Data Cleaning Techniques, 25(3), 167-179.
Kaggle. Retrieved from www.kaggle.com
Cambridge, J. (2023). Explainable artificial intelligence for fraud detection: A survey. Expert Systems with Applications, 181, 115284.
Lee, K., et al. (2022). A deep learning approach for credit card fraud detection using autoencoders. Neural Computing and Applications, 34, 10377–10388.
Wang, Y., et al. (2021). Fraud detection in online banking transactions using recurrent neural networks. IEEE Access, 9, 101726-101737. Zhou, Z., et al. (2020)
A survey of fraud detection techniques based on artificial intelligence. IEEE Access, 8, 106501-106518. Liu, X., et al. (2019).
Fraud detection for online retail using random forest. Electronic Commerce Research and Applications, 36, 100862.
Tan, E., et al. (2023). Artificial intelligence and algorithmic decisions in fraud detection: An interpretive structural model. Data & Policy, 5, e25.
Springer, A. (2023). Artificial Intelligence and Fraud Detection. In: Data Science for Business and Decision Making. Academic Press.
Digital Ocean, B. (2023). Review the role of artificial intelligence in detecting and preventing financial fraud. International Journal of Industrial Engineering Computations, 14(1), 1-16.
