Hybrid Explainable AI for Financial Risk Prediction: Integrating Ensemble Learning, CNN–LSTM Networks, and Financial Sentiment Analytics

The paper tackles the issue of increasing complexity associated with risk assessment in financial transactions due to limitations related to conventional machine learning approaches that cannot handle nonlinearity, context-based sentiment information, and imbalanced classes. Thus, an AI model is proposed for predicting financial risks with machine learning and deep learning algorithms utilizing both structured and unstructured data sources. In particular, structured information was gathered from the "Home Credit Default Risk" dataset consisting of 307,511 samples and 122 financial features, and unstructured information was analyzed by employing FinBERT for sentiment analysis of financial news and social media content.

In the end, the paper makes it clear—moving forward, it’s not just about making models accurate. Fraud detection needs to double down on transparency, fair practices, explainability, and being able to scale up operations. This review should help researchers, industry folks, policymakers, and financial institutions navigate the push for smarter, more dependable AI-powered fraud defenses.