Authors : Atta Yaw Agyeman; Samuel Gbli Tetteh

Volume/Issue : Volume 9 - 2024, Issue 7 - July

Google Scholar : https://tinyurl.com/4swnb2y3

Scribd : https://tinyurl.com/57dyv4x7

DOI : https://doi.org/10.38124/ijisrt/IJISRT24JUL959

Abstract : In the dynamic landscape of the financial sector, the escalating menace of financial fraud presents pervasive implications for businesses and consumers alike. Particularly, detecting credit card fraud in real- time transactions has become a pivotal concern within the financial industry. This abstract delves into the critical role of data mining in addressing the complexities of credit card fraud detection, shedding light on the multifaceted challenges that confront this domain. The realm of financial business is increasingly besieged by the spectre of financial fraud, necessitating robust measures to combat its detrimental effects. As the sophistication and prevalence of fraudulent activities continue to evolve, the imperative of deploying effective strategies for fraud detection becomes more pronounced. Applying data mining techniques in this context is paramount in identifying and mitigating credit card fraud. Leveraging advanced data mining methodologies is essential for scrutinising live transactions and discerning anomalous patterns indicative of fraudulent behaviour. Credit card fraud detection poses formidable challenges, primarily attributable to two compelling factors. Firstly, the inherent dynamism of normal and fraudulent behavioural profiles engenders a perpetual need for adaptive and responsive detection mechanisms. Secondly, the highly imbalanced nature of credit card fraud data sets further complicates accurately identifying fraudulent activities, necessitating nuanced approaches to discern anomalies amidst voluminous transactional data effectively. In light of the foregoing, this abstract underscore the criticality of data mining in addressing the intricate landscape of credit card fraud detection, emphasising the need for agile and sophisticated methodologies to navigate the evolving nature of fraudulent behaviours and the skewed distribution of fraud-related data sets. By comprehensively elucidating these challenges, this abstract provides a foundational understanding of the nuanced complexities inherent in combatting financial fraud through the lens of data mining.

Keywords : Fraud Detection; Support Vector Machine Classifier; Naïve Bayes Classifier; Random Forest; Majority Voting.

References :

1. H. Harwani, J. Jain, C. Jadhav, and M. Hodavdekar, “Credit Card Fraud Detection Technique using Hybrid Approach: An Amalgamation of Self Organizing Maps and Neural Networks,” Int. Res. J. Eng. Technol., no. July, pp. 5071–5075, 2020, [Online]. Available: www.irjet.net
2. J. O. Awoyemi and S. A. Oluwadare, “Credit card fraud detection using Machine Learning Techniques : A Comparative Analysis,” 2017.
3. S. Wang, G. Liu, Z. Li, S. Xuan, C. Yan, and C. Jiang, “Credit Card Fraud Detection Using Capsule Network,” Proc. - 2018 IEEE Int. Conf. Syst. Man, Cybern. SMC 2018, pp. 3679–3684, 2019, doi: 10.1109/SMC.2018.00622.
4. A. I. Kokkinaki, “On atypical database transactions: Identification of probable frauds using machine learning for user profiling,” Proc. IEEE Knowl. Data Eng. Exch. Work. KDEX, pp. 107–113, 1997, doi: 10.1109/kdex.1997.629848.
5. Y. Kültür and M. U. Çağlayan, “Hybrid approaches for detecting credit card fraud,” Expert Syst., vol. 34, no. 2, pp. 1–13, 2017, doi: 10.1111/exsy.12191.
6. A. Dal Pozzolo, G. Boracchi, O. Caelen, C. Alippi, and G. Bontempi, “Credit card fraud detection: A realistic modeling and a novel learning strategy,” IEEE Trans. Neural Networks Learn. Syst., vol. 29, no. 8, pp. 3784–3797, 2018, doi: 10.1109/TNNLS.2017.2736643.
7. E. Aleskerov, B. Freisleben, and B. Rao, “CARDWATCH: A neural network based database mining system for credit card fraud detection,” IEEE/IAFE Conf. Comput. Intell. Financ. Eng. Proc., pp. 220–226, 1997, doi: 10.1109/cifer.1997.618940.
8. K. Randhawa, C. K. Loo, M. Seera, C. P. Lim, and A. K. Nandi, “Credit Card Fraud Detection Using AdaBoost and Majority Voting,” IEEE Access, vol. 6, pp. 14277–14284, 2018, doi: 10.1109/ACCESS.2018.2806420.
9. P. K. Chan, L. Ave, and N. York, “Distributed Data Mining in Credit Card Fraud Detection 1 Introduction,” pp. 1–17, 1999.
10. A. O. Toluwase and S. A. Olumide, “A framework for detecting credit card f...sitive meta-learning ensemble approach.pdf.” p. 15, 2020.
11. V. Dheepa and R. Dhanapal, “Hybrid Approach for Improvis...on Collective Animal Behaviour and SVM.pdf.” p. 10, 2013.
12. N. Shirodkar, R. Sakhalkar, P. Mandrekar, K. M. C. Kumar, R. S. Mandrekar, and S. Aswale, “Credit Card Fraud Detection Techniques – A Survey,” pp. 1–7, 2020, doi: 10.1109/ic-ETITE47903.2020.112.
13. H. Jiawei, Micheline Kamber, and P. Jian, Data Mining Concepts and Techniques, Third Edit. 225Wyman Street, Waltham: Morgan Kaufmann Publishers, 2012.
14. D. M. B, B. Janani, S. Gayathri, and N. Indira, “CREDIT CARD FRAUD DETECTION USING RANDOM FOREST,” pp. 6662–6666, 2019.
15. I. Kaur and M. Kalra, “Ensemble Classification Method for Credit Card Fraud Detection,” no. 3, pp. 423–427, 2019, doi: 10.35940/ijrte.C4213.098319.
16. M. Zareapoor and P. Shamsolmoali, “Application of Credit Card Fraud Detection_ Based on Bagging Ensemble Classifier.pdf.” International Conference On Intelligent Computing Communication & Convergence, p. 8, 2015.
17. F. N. Ogwueleka, “DATA MINING APPLICATION IN CREDIT CARD FRAUD DETECTION SYSTEM,” vol. 6, no. 3, pp. 311–322, 2011.
18. S. K.R. and M. Zareapoor, “FraudMiner: A Novel Credit Card Fraud Detection Model Based On Frequent Itemset Mining.” Hindawi Publishing Corporation, p. 11, 2014.
19. V. Dheepa and R. Dhanapal, “BEHAVIOR BASED CREDIT CARD FRAUD DETECTION USING SUPPORT VECTOR MACHINES,” vol. 6956, no. July, pp. 391–397, 2012, doi: 10.21917/ijsc.2012.0061.