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Paper Details

E-commerce Merchant Fraud Detection using Machine Learning Approach
Fahim Hasan, Md Abdullah Al Mamun, Md. Rayhan Kabir, Nur Salman Rahman, Sourov Kumar Mondal,
At present, e-commerce has become a global phenomenon. With the great achievement of ecommerce, many are cruel Promotional services are also increasing: with the aim of growing sales, spiteful marketers try to improve their target spectators by improving the outcomes of an illegal search using false travel, shopping, etc. In this report, we read about the problem of deception in major commerce platforms. First, we want to list the merchant fraud, the names of those who have previously committed fraud in the business will be marked on the list. And will train machines using machine learning approach. So that, if a merchant id is given in the system, it can detect whether the id is fraud or not. Our lesson here paper is predictable to hut light on the defense in contradiction of e-commerce fraud of active commerce platforms. In this research report, we proposed a machine learning model to analyze and identify merchant fraud. As a machine learning model, we choose the Random forests, decision tree and logistic regression algorithm for our model.

Random Forest , Decision tree , Confusion matrix , Logistic regression , Ip-bound , Machine learning (ML) , Merchant registration date , Internet banking , Cash on delivery
Journal or Conference Name
7th International Conference on Communication and Electronics Systems, ICCES 2022 - Proceedings
Publication Year