Predictive Financial Risk Analytics for Early Detection of Suspicious Transactions and Strengthening Banking System Resilience

Abstract

The main contribution of this research is Deep Learning-Based Multichannel Neural Network Architecture, which utilizes multiple channels of neural network to learn different representations of text and efficiently analyze the sentiment. The suggested framework integrates different complementary feature extraction approaches, which allows the model to capture the local semantic patterns, contextual dependency, and the salient sentiment-related features in the input text. Advanced deep learning techniques are used to process text for each channel and the representations obtained from each channel are combined for the final sentiment classification. The performance of the proposed multichannel architecture is evaluated by its performance on benchmark sentiment analysis datasets showing that it outperforms conventional machine learning methods and single channel deep learning models in terms of accuracy, precision, recall and F1-score. The results show that using multiple representations of the neural networks has a good feature learning ability and can provide a powerful solution to sentiment classification problems. The proposed approach can be used for multiple applications such as customer feedback analysis, opinion mining, social media monitoring and automated decision-support systems.