Lstm-based automated learning with smart data to improve marketing fraud detection and financial forecasting

This proposed model is based on a deep recurrent neural network trained with Long Short- Term Memory Network (LSTM), used because of its ability to learn long term dependencies, taking the concatenated function and Financial data as input, while integrating encapsulations, using Smart Data and retrieving information by combining multiple search results (all the Web). It combines representation training with financial data while integrating encapsulations from multiple sources and retrieving information by combining multiple search results. It provides some good ideas that we have extended to improve Corporate Marketing and Business Strategies. We show that the proposed model learns to localize and recognize different aspects of Corporate Marketing and Business Strategies. We evaluate it on the challenging task of detecting Fraud in Financial Services and Financial Time Series Forecasting and show that it is more accurate than the state-of-the-art of other neural networks and that it uses fewer parameters and less computation.