References
Abbas, R. y., & Aida, M. (2016). Fraud Detection and Prevention: A Comprehensive Review. Ahmed, A., Aslam, M., & Farooq., B. (2015). Fraud Detection Using Machine Learning: A Comprehensive Review. Anuradha, V., & Rawte, G. (2015). Fraud Detection in Health Insurance using Data Mining Techniques. Asuk, M. K. (2012). A fraud detection approach with data mining in health insurance. Bolton, R. J. (2002). A statistical fraud detection system for insurance data. Insurance: Mathematics and Economics , 239-255. Borgelt, C. (2009). Methods for data analysis and mining. John Wiley & Sons. Graphical models. Brause, T. R., Langsdorf, T., & (2000)., M. H. (n.d.). Breiman, L. (2001). Random forests. Machine learning. 5-32. Chen Q., L. W. (2017). Enhancing Fraud Detection Models with Ensemble Learning Techniques. . Journal of Computational Finance, 22(4), , 210-232. Chen, Y. e. (2020). Enhancing Fraud Detection Through Logistic Regression: A Case Study in the Banking Sector. Journal of Financial Analytics, 35(1) , 78-94. Chen, Y. e. (2019). Predictive Modeling in Fraud Detection: An Analysis of Logistic Regression Approaches. Journal of Computational Finance, 36(2) , 89-110. Chengwei Liu, Y. C. (2015). Financial Fraud Detection Model: Based on Random Forest. International Journal of Economics and Finance . Christopher M. Bishop. (2011). Pattern Recognition and Machine Learning. Efstathios Kirkos, C. S. (2016). Data Mining techniques for the detection of fraudulent financial statements. Efstathios, K., & Spathis, C. Y. (2007). Data Mining techniques for the detection of fraudulent financial statements. Expert Systems with Applications 32 (2007) , 995– Farzi, S. Z. (2023). fraud detection in financial statements using data mining and GAN models. journal Expert Systems with Applications . Fisch, B. S. (2020). Human-in-the-Loop Fraud Detection. . arXiv preprint arXiv:2009.03473. Flach, P. (2009). Machine Learning: The Art and Science of Algorithms that Make Sense of Data. Fu, C. L. (2015). Financial Fraud Detection Model: Based on Random Forest. International Journal of Economics and Finance; Vol. 7, No. 7; 2015 , 178-188. Garcia, R. e. (2017). Strategies for Mitigating False Positives in Fraud Detection: A Comparative Study. Journal of Financial Analytics, 28(1) , 56-78. Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. . (2013). Introduction to Statistical Learning. Springer. George Fei, e. a. (2018). Machine Learning for Anomaly Detection and Fraud Prevention in Banking. Gupta, R. &. (2016. ). Feature Selection Strategies in Data Mining for Financial Fraud Detection. Journal of Business Analytics, 5(1), , 45-68. Hall, W., Frank, E., & Mark, A. (2016). Data Mining: Practical Machine Learning Tools and Techniques. Han, J., Kamber, M., & Pei, J. (2011). Data Mining: Concepts and Techniques. Han, Jiawei; Kamber, Micheline; Pei, Jian. (2011). Data Mining: Concepts and Techniques. Johnson, M. &. (2019). Data Mining Techniques for Fraud Detection: A Comprehensive Review. Journal of Data Analytics in Finance, 40(2), , 245-268. Jones A., &. W. (2018). A Comparative Analysis of Machine Learning Approaches for Financial Fraud Detection. . International Journal of Finance and Data Analysis, 15(2), , 67-89. Kumar, R. (2019). Research Methodology: A Step-by-Step Guide for Beginners. . SAGE Publications. Li, Q. &. (2020). Ensemble Techniques in Fraud Detection: A Comparative Analysis of Random Forest. Expert Systems with Applications, 48(3), , 210-230. Li, Q. &. (2018). Random Forest Applications in Financial Fraud Detection. Expert Systems with Applications, 45(1), , 123-134. Li, Y. F. ( 2020). Collaborative Learning for Fraud Detection using Federated Learning. . arXiv preprint arXiv:2001.05065 . Li, Y. W. (2019). Real-time Fraud Detection in Financial Transactions Using Streaming Data Mining. Journal of Financial Engineering, 18(5), , 301-325. Mahmood Mohammadi, S. Y. (2020). Financial Reporting Fraud Detection: An Analysis of Data Mining Algorithms. International Journal of Finance and Managerial Accounting, Vol.4, No.16 . Malhotra, S. M. (2022). Anomaly Detection Using GANs for Uncovering Financial Forgeries. . Journal of King Saud University -Computer and Information Sciences, 34(8), , 7614-7625. Maloof, M. A. (2006). Machine Learning and Data Mining for Computer Security: Methods and Applications. Marakas, G.M. (2003). Modern Data Warehousing, Mining, and Visualization: Core Concepts; Prentice Hall: Upper Saddle River,. NJ, USA, . Meenatkshi, R. &. (2016). Fraud Detection in Financial Statement using Data Mining Technique and Performance Analysis. . International Science Press, 9(27), , 407-413. Mehta, R. a. (2019). Data Mining Techniques in Fraud Detection. Mousa and Albashrawi. (2016). Detecting Financial Fraud Using Data Mining Techniques: A Decade Review from 2004 to 2015. Journal of Data Science 14() , 553-570. Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective. O'Neil, C. &. (2013). Doing Data Science: Straight Talk from the Frontline. O'Reilly Media. Pressman, R. S. (2022). Software Engineering: A Practitioner's Approach (9th ed.). McGraw-Hill Education. Ribeiro, M. T. (2016). Why should I trust you? Explaining the Predictions of any Machine Learning Model. . In Proceedings of the 38th International Conference on Machine Learning, (pp. (pp. 1135-1144).). Smith J., J. M. (2015 ). Data Mining Techniques for Fraud Detection in Financial Transactions. Journal of Financial Analytics, 10(3), , 123-145. Smith, A. J. (2018). Advances in Fraud Detection: A Comprehensive Review. Journal of Financial Security, 32(1), , 45-68. Smith, A. J. (2017). The Evolving Landscape of Financial Fraud. Journal of Financial Security, 25(3), , 112-130. Thompson, R. e. (2021). Financial Metrics and Fraud Detection: A Longitudinal Analysis. International Journal of Accounting and Finance, 54(4), , 321-340. Thompson, R. e. (2021). The Impact of Fraud Detection on Financial Performance Metrics. International Journal of Finance and Economics, 50(4) , 521-539. Trevor Hastie, R. T. (2009). The Elements of Statistical Learning. Springer. Zhang, J. L. (2023). Detecting Financial Insider Trading using Graph Networks: A Deep Learning Approach. . International Journal of Financial Engineering, 1(1), , 1-15.