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Financial Fraud Detection in Nigerian Banks: Data Mining Approach

Mohammed, Usman, Professor G. M. Wajiga and SAIDU, Hayatu Alhaji

Abstract

This research investigates the application of data mining techniques, specifically logistic regression and random forest, to detect financial fraud within Nigerian banks. Using individual bank statements and statutory bank charges, the study focuses on developing a robust system for identifying fraudulent transactions. The data preprocessing involves extracting key features such as transaction type, amount, balance, and transaction date. The dataset is split into training and testing sets, and both machine learning models are trained and evaluated based on metrics like accuracy, precision, recall, and F1-scores.The results indicate that the Random Forest model outperforms Logistic Regression, achieving higher accuracy and better handling of complex relationships within the data. Visualization tools like Matplotlib are used to present prediction probabilities, enhancing understanding of model behavior. The system's implementation includes secure access features, detailed transaction analysis, and comprehensive fraud summaries. Challenges such as data imbalance are addressed with techniques like SMOTE and advanced preprocessing methods. This study highlights the potential of using advanced machine learning models for effective fraud detection in financial transactions. The findings suggest that further improvements in feature extraction, data expansion, and exploring more sophisticated models can enhance system performance. This research contributes to the ongoing efforts to secure financial systems against fraudulent activities, offering valuable insights and practical solutions for the banking sector

Keywords

Component; Financial fraud detectionLogistic regressionRandom forest

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.

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