People today tend to make multiple transactions every day. It has been observed that around 150 million transactions are being carried out every 24 hours. There are several modes through which these transactions can be accomplished, but amongst them, credit-based transactions stand ahead. Using credit system for negotiations is worthwhile for both the users and the credit providers. But with the advent of newer methodologies, illicit usage of the credit system has been growing. This situation seems like a stumbling block for both the users and the credit providers. In this pursuit, Big Data provides better and utilitarian methods and algorithms to overcome this snag. Big Data in this context helps in building an analytical model that can be integrated with Hadoop for storage and is feasible to implement pattern recognition algorithms that are aided by few machine learning algorithms to predict fraudulent patterns. This paper reflects that our proposed model comes withhigher accuracy rates when compared to the other existing decision making models.
CITATION STYLE
Jaidhan, B. J., Divya Madhuri, B., Pushpa, K., Lakshmi Devi, B. V. S., & Shanmuk Srinivas, A. (2019). Application of big data analytics and pattern recognition aggregated with random forest for detecting fraudulent credit card transactions (CCFD-BPRRF). International Journal of Recent Technology and Engineering, 7(6), 1082–1087.
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