Application of Machine Learning Models in Fraud Detection in Financial Transactions
DOI:
https://doi.org/10.56294/dm2023109Keywords:
Fraud Detection, Machine Learning, Convolutional Neural Networks, Random Forests, Performance EvaluationAbstract
Introduction: fraud detection in financial transactions has become a critical concern in today's financial landscape. Machine learning techniques have become a key tool for fraud detection given their ability to analyze large volumes of data and detect subtle patterns.
Objective: evaluate the performance of machine learning techniques such as Random Forest and Convolutional Neural Networks to identify fraudulent transactions in real time.
Methods: a real-world data set of financial transactions was obtained from various institutions. Data preprocessing techniques were applied that include multiple imputation and variable transformation. Models such as Random Forest, Convolutional Neural Networks, Naive Bayes and Logistic Regression were trained and optimized. Performance was evaluated using metrics such as F1 score.
Results: random Forests and Convolutional Neural Networks achieved an F1 score greater than 95% on average, exceeding the target threshold. Random Forests produced the highest average F1 score of 0,956. It was estimated that the models detected 45 % of fraudulent transactions with low variability.
Conclusions: the study demonstrated the effectiveness of machine learning models, especially Random Forests and Convolutional Neural Networks, for accurate real-time fraud detection. Its high performance supports the application of these techniques to strengthen financial security. Future research directions are also discussed
References
1. Ali A, Abd Razak S, Othman SH, Eisa TAE, Al-Dhaqm A, Nasser M, et al. Financial Fraud Detection Based on Machine Learning: A Systematic Literature Review. Applied Sciences 2022;12:9637. https://doi.org/10.3390/app12199637.
2. Kaur D, Saini A, Gupta D. Credit Card Fraud Detection Using Machine Learning, Deep Learning, and Ensemble of the both. 2022 Seventh International Conference on Parallel, Distributed and Grid Computing (PDGC), Solan, Himachal Pradesh, India: IEEE; 2022, p. 484–9. https://doi.org/10.1109/PDGC56933.2022.10053175.
3. Al-Sayyed R, Alhenawi E, Alazzam H, Wrikat A, Suleiman D. Mobile money fraud detection using data analysis and visualization techniques. Multimed Tools Appl 2023. https://doi.org/10.1007/s11042-023-16068-4.
4. Phua C, Lee V, Smith K, Gayler R. A Comprehensive Survey of Data Mining-based Fraud Detection Research 2010. https://doi.org/10.48550/ARXIV.1009.6119.
5. Hilal W, Gadsden SA, Yawney J. Financial Fraud: A Review of Anomaly Detection Techniques and Recent Advances. Expert Systems with Applications 2022;193:116429. https://doi.org/10.1016/j.eswa.2021.116429.
6. Tique DH, Ordoñez JJP, Cano CAG. How do technology equipment companies implement new billing strategies? Metaverse Basic and Applied Research 2022;1:15–15. https://doi.org/10.56294/mr202215.
7. Bhattacharyya S, Jha S, Tharakunnel K, Westland JC. Data mining for credit card fraud: A comparative study. Decision Support Systems 2011;50:602–13. https://doi.org/10.1016/j.dss.2010.08.008.
8. Douceur JR. The Sybil Attack. In: Druschel P, Kaashoek F, Rowstron A, editors. Peer-to-Peer Systems, vol. 2429, Berlin, Heidelberg: Springer Berlin Heidelberg; 2002, p. 251–60. https://doi.org/10.1007/3-540-45748-8_24.
9. Yufeng Kou, Chang-Tien Lu, Sirwongwattana S, Yo-Ping Huang. Survey of fraud detection techniques. IEEE International Conference on Networking, Sensing and Control, 2004, vol. 2, Taipei, Taiwan: IEEE; 2004, p. 749–54. https://doi.org/10.1109/ICNSC.2004.1297040.
10. Akoglu L, Tong H, Koutra D. Graph-based Anomaly Detection and Description: A Survey 2014. https://doi.org/10.48550/ARXIV.1404.4679.
11. Sadgali I, Sael N, Benabbou F. Performance of machine learning techniques in the detection of financial frauds. Procedia Computer Science 2019;148:45–54. https://doi.org/10.1016/j.procs.2019.01.007.
12. Tiwari P, Mehta S, Sakhuja N, Kumar J, Singh AK. Credit Card Fraud Detection using Machine Learning: A Study 2021. https://doi.org/10.48550/ARXIV.2108.10005.
13. Abdallah A, Maarof MA, Zainal A. Fraud detection system: A survey. Journal of Network and Computer Applications 2016;68:90–113. https://doi.org/10.1016/j.jnca.2016.04.007.
14. Espinosa RDC, Caicedo-Erazo JC, Londoño MA, Pitre IJ. Inclusive Innovation through Arduino Embedded Systems and ChatGPT. Metaverse Basic and Applied Research 2023;2:52–52. https://doi.org/10.56294/mr202352.
15. Moreno MCC, Castro GLG. Strengthening Governance in Caquetá: The Role of Web-based Transparency Mechanisms for Public Information. Metaverse Basic and Applied Research 2022;1:16–16. https://doi.org/10.56294/mr202216.
16. Ngai EWT, Hu Y, Wong YH, Chen Y, Sun X. The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decision Support Systems 2011;50:559–69. https://doi.org/10.1016/j.dss.2010.08.006.
17. HaratiNik MR, Akrami M, Khadivi S, Shajari M. FUZZGY: A hybrid model for credit card fraud detection. 6th International Symposium on Telecommunications (IST), Tehran, Iran: IEEE; 2012, p. 1088–93. https://doi.org/10.1109/ISTEL.2012.6483148.
18. Boullé M. Compression-Based Averaging of Selective Naive Bayes Classifiers. Journal of Machine Learning Research 2007;8:1659–85.
19. Correa Bahnsen A, Aouada D, Ottersten B. Example-dependent cost-sensitive decision trees. Expert Systems with Applications 2015;42:6609–19. https://doi.org/10.1016/j.eswa.2015.04.042.
20. Forough J, Momtazi S. Ensemble of deep sequential models for credit card fraud detection. Applied Soft Computing 2021;99:106883. https://doi.org/10.1016/j.asoc.2020.106883.
21. Moreno MCC, Castro GLG. Unveiling Public Information in the Metaverse and AI Era: Challenges and Opportunities. Metaverse Basic and Applied Research 2023;2:35–35. https://doi.org/10.56294/mr202335.
22. Gupta B. Understanding Blockchain Technology: How It Works and What It Can Do. Metaverse Basic and Applied Research 2022;1:18–18. https://doi.org/10.56294/mr202218.
23. Jain N, Chaudhary A, Kumar A. Credit Card Fraud Detection using Machine Learning Techniques. 2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART), Moradabad, India: IEEE; 2022, p. 1451–5. https://doi.org/10.1109/SMART55829.2022.10047360.
24. Awoyemi JO, Adetunmbi AO, Oluwadare SA. Credit card fraud detection using machine learning techniques: A comparative analysis. 2017 International Conference on Computing Networking and Informatics (ICCNI), Lagos: IEEE; 2017, p. 1–9. https://doi.org/10.1109/ICCNI.2017.8123782.
25. Abiodun OI, Jantan A, Omolara AE, Dada KV, Mohamed NA, Arshad H. State-of-the-art in artificial neural network applications: A survey. Heliyon 2018;4:e00938. https://doi.org/10.1016/j.heliyon.2018.e00938.
26. Shenvi P, Samant N, Kumar S, Kulkarni V. Credit Card Fraud Detection using Deep Learning. 2019 IEEE 5th International Conference for Convergence in Technology (I2CT), Bombay, India: IEEE; 2019, p. 1–5. https://doi.org/10.1109/I2CT45611.2019.9033906.
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Copyright (c) 2023 Roberto Carlos Dávila Morán , Rafael Alan Castillo Sáenz, Alfonso Renato Vargas Murillo, Leonardo Velarde Dávila, Elvira García Huamantumba , Camilo Fermín García Huamantumba , Renzo Fidel Pasquel Cajas, Carlos Enrique Guanilo Paredes (Author)
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