A Novel Method for Detecting Financial Fraud Using Deep Learning in Online Retail

The Growing e-commerce is directly impacted by the rising number of drug users online. Alongside this expansion, there has been a discernible rise in online exchange fraud. This will lead to the creation of a system for identifying online purchase fraud that is machine literacy focused. To determine which of the four widely used machine learning algorithms-decision trees, naive Bayes, irregular timbers, and neural networks-is the most efficient, we compare them all in detail in this paper. Because the current understanding is unclear, customized data is created using the Synthetic Minority Oversampling Technique (SMOTE). Strict identification and prevention measures are necessary since financial fraud is occurring at previously unheard-of levels. Deep learning computing, which is renowned for its high recognition rate, adaptability, and wide range of implementation, is crucial in this situation. Our research proposes a deep learning-based approach that accounts for the vast and intricate data associated with online retailers in order to statistically detect tax fraud. Initially, the encoder is employed to extract characteristics from the gesture; point birth is constrained to the spatiotemporal volume of the thick line to reduce computing complexity. The data is then transformed into a visual depiction of the word gesture using a neural network model. The characteristics are subsequently merged and point bracket chopping is minimized using weighted correlation techniques. The application of minimal reconstruction crimes is the final technique for assessing and discovering fiscal fraud.