Financial Data Mining Using Sparse Attention and Knowledge-Augmented Models

Abstract

Financial data mining has become increasingly essential in making informed investment decisions, predicting market trends, and detecting fraudulent transactions. As the financial sector continues to generate high-dimensional, temporally sequenced, and complex data, there is an emerging need for models that are not only powerful in representation but also efficient and interpretable. In this study, we propose a hybrid framework that leverages sparse attention mechanisms along with knowledge-augmented models to perform efficient and explainable financial data mining. Sparse attention selectively focuses on relevant parts of the input sequence, reducing computational complexity while maintaining performance. Knowledge augmentation introduces structured financial domain knowledge into the model to improve context understanding and accuracy. The inclusion of domain-specific ontologies and knowledge graphs also enhances interpretability, enabling better trust and transparency in financial decision-making. This work contributes a step forward in intelligent financial systems, providing a scalable and interpretable solution to the challenges of financial data analysis.