.png){kind=icon}
.png){kind=icon}
Return
Natural language-driven data visualization using Kestrel AI: a novel algorithm for intelligent analytics
.png){kind=icon}
PRE
AI
DOI：10.1007/s11227-026-08560-z.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Generative AI is increasingly used in financial analytics to interpret large datasets and support time-sensitive decision making. However, access to financial databases still depends heavily on structured query language (SQL), which limits usability for non-technical users. Existing natural-language interfaces often perform well on simple requests but degrade on complex, nested, or domain-specific financial queries, especially under real-time constraints. This paper presents Kestrel AI, a natural-language-driven analytics system that combines advanced NLP with retrieval-augmented generation to produce executable SQL and corresponding visualizations. The system is designed for large-scale financial data, concurrent workloads, and low-latency execution through a modular architecture with GPU-accelerated inference, parallel retrieval, and caching. Experimental evaluation across multiple financial datasets reports an average SQL accuracy of 92%. The system achieves an average model inference latency of approximately 0.3 s, while end-to-end query execution time ranges from 1.2 to 4.7 s depending on query complexity. User studies with technical and non-technical participants show higher task completion and lower interaction time relative to selected baseline tools. Overall, the results indicate that retrieval-grounded generation combined with performance-aware system design can improve both accessibility and responsiveness in financial analytics workflows. The implementation and evaluation resources are publicly available at https://github.com/veerababulara/Natural-Language-Driven-Data-Visualization-Using-Kestrel-AI.git and archived with DOI https://doi.org/10.5281/zenodo.19642245 .
Keywords:
Generative AI
Financial analytics
NLP
RAG
SQL queries
User experience