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Predicting Fraudulence Transaction under Data Imbalance using Neural Network (Deep Learning)
2022
Data Science: Journal of Computing and Applied Informatics
The number of financial transactions has the potential to cause many violations of the law (fraud). Conventional machine learning has been widely used, including logistic regression, random forest, and gradient boosted. However, the machine learning can work as long as the dataset contains fraud. Many new financial technology companies need to anticipate the potential for fraud, which they have not experienced much. This potential for a crime can also be experienced by old service providers
doi:10.32734/jocai.v6.i2-8309
fatcat:wrsdntcttjgf3noryfqnxhnx3e