Abstract
Nowadays fraud has been increasing due to the establishment of online payment mode on different E-commerce platform.A credit card is a form of payment that lets you buy goods or services on credit from an issuer, usually a bank. You can make purchases up to a specified limit and then pay them off over time either in full or with minimum payments.There are several types of security features including fraud protection, verified by visa and master card secure code, address verification systems, and biometric authentication. Additionally, some cards offer the additional security feature of a chip and pin system which requires that the cardholder enter a secret code to make purchases.Still fraud has been executed using this card. In this fraud, banks, merchants, and organisations are losing billions of dollars. According to one survey, the prevalence of credit card fraud is rising by 12.5% a year. It is crucial to identify fraud using secure and effective methods.
Nowadays, hybrid algorithms and artificial neural networks are used to detect fraud since they perform better than other methods. We will use dataset variables like "duration," "amount of transaction," and "V1 to V28" as derived parameters for this. We will build a model that will separate out fraudulent transactions from other transactions using machine learning techniques or algorithms.
Key-Words / Index Term
Machine learning, Hybrid algorithms, Fraud, Fraudulent and Credit card
References
[1] D. Tanouz, R. R. Subramanian, D. Eswar, G. V. P. Reddy, A. R. Kumar and C. V. N. M. Praneeth, "Credit Card Fraud Detection Using Machine Learning," 5th International Conference on Intelligent Computing and Control Systems (ICICCS), pp. 967-972, 2021.
[2] M. R. Dileep, A. V. Navaneeth and M. Abhishek, "A Novel Approach for Credit Card Fraud Detection using Decision Tree and Random Forest Algorithms," Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV),pp. 1025-1028, 2021.
[3] AnastasiiaIzotova, Adel Valiullin, “Comparison of Poisson process and machine learning algorithms approach for credit card fraud detection” Procedia Computer Science,Volume 186,Pages 721-726,2021
[4] A. A. Taha and S. J. Malebary, "An Intelligent Approach to Credit Card Fraud Detection Using an Optimized Light Gradient Boosting Machine," in IEEE Access, vol. 8, pp. 25579-25587, 2020.
[5] M. Azhan and S. Meraj, "Credit Card Fraud Detection using Machine Learning and Deep Learning Techniques," 3rd International Conference on Intelligent Sustainable Systems (ICISS), pp. 514-518, 2020.
[6] R. Sailusha, V. Gnaneswar, R. Ramesh and G. R. Rao, "Credit Card Fraud Detection Using Machine Learning," 4th International Conference on Intelligent Computing and Control Systems (ICICCS), pp. 1264-1270, 2020.
[7] VaishnaviNathDornadula, S Geetha,"Credit Card Fraud Detection using Machine Learning Algorithms,Procedia Computer Science",Volume 165,Pages 631-641,2019.
[8] D. Varmedja, M. Karanovic, S. Sladojevic, M. Arsenovic and A. Anderla, "Credit Card Fraud Detection - Machine Learning methods," 18th International Symposium INFOTEH-JAHORINA (INFOTEH),pp. 1-5, 2018.
[9] A. Thennakoon, C. Bhagyani, S. Premadasa, S. Mihiranga and N. Kuruwitaarachchi, "Real-time Credit Card Fraud Detection Using Machine Learning,"9th International Conference on Cloud Computing, Data Science & Engineering (Confluence), pp. 488-493, 2019.
[10] S. Mittal and S. Tyagi, "Performance Evaluation of Machine Learning Algorithms for Credit Card Fraud Detection," 9th International Conference on Cloud Computing, Data Science &Engineering (Confluence), pp. 320-324, 2019.
[11] V. CeronmaniSharmila, K. K. R., S. R., S. D. and H. R., "Credit Card Fraud Detection Using Anomaly Techniques," 1st International Conference on Innovations in Information and Communication Technology (ICIICT),pp. 1-6, 2019.
[12] S. Dhankhad, E. Mohammed and B. Far, "Supervised Machine Learning Algorithms for Credit Card Fraudulent Transaction Detection: A Comparative Study," IEEE International Conference on Information Reuse and Integration (IRI), pp. 122-125, 2018.
[13] S. Xuan, G. Liu, Z. Li, L. Zheng, S. Wang and C. Jiang, "Random forest for credit card fraud detection," IEEE 15th International Conference on Networking, Sensing and Control (ICNSC),pp. 1-6, 2018.
[14] Okfalisa, &Gazalba, Ikbal&Mustakim, Mustakim& Reza, Nurul.Comparative analysis of k-nearest neighbor and modified k-nearest neighbor algorithm for data classification. pp. 294-298, 2017.
