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@article{151525,
        author = {Akansha Garg  and Ankita Tiwari and Nagresh Kumar},
        title = {FRAUD CUSTOMER PREDICTION BASED ON BANK LOAN DATA  ANALYSIS USING MACHINE LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {1},
        pages = {211-215},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=151525},
        abstract = {In our country, there  has been a huge demand  of personal loans arise from the citizens. There are so many people who are applying for the personal loan from banks as per their needs. But for the banks, it is difficult to detect the fraud customers that which customer will pay their loans & which will not due to the number of bank frauds is increasing day by day. To prevent this situation, we have explained how to create  predictive loan models . In steps , we have shown how to process the raw data, select relevant features, performed data analysis
& lastly built a model. In this paper, we have built some supervised learning models which are having  higher  accuracy  score  and  on  the  basis  of  requests   we  easily  determine   which transactions to  authorize. Classification report  having higher f-score, precision and  recall is considered as the best model among all the models.
},
        keywords = {Accuracy score, Classification Report, F-score, Precision and Recall},
        month = {},
        }