Improved Instagram Spam Detection with Convolutional Attention Networks and Water Wave Optimization

Abstract

Financial fraud costs people and businesses billions worldwide. In the digital age, traditional fraud detection methods are inadequate. This paper reviews recent real-time financial fraud detection methods, including behavioral analytics, blockchain, AI, and ML. We also provide data-driven insights on detection rates, cost efficiency, and industry-specific challenges via case studies and practical implementations. Instagram comments are notorious for spam and fraud, and the finance sector is no exception. Each day brings new casualties. The Instagram spam filter isn't good, and most study has concentrated on theoretical possibilities. Few practical implementations have been reviewed. Machine learning for fraud detection faces data quality, scalability, model interpretability, and real-time processing issues. We also discuss ethical and privacy considerations in machine learning-based financial transactions. The research highlights these components to develop trust in financial institutions and produce more effective and ethical machine learning fraud detection systems. We use a Convolutional Attention-Based Mechanism (CBA) and Water Wave Optimization to improve spam classification. The attention mechanism enables convolutional layers obtain deep feature representations by focusing on notable patterns. To optimize performance, WWO modifies hyperparameters. We found that our model outperforms typical deep learning models in classification accuracy, recall, precision, and F1-score. This method detects spam accounts efficiently and reliably.