Leveraging Generative AI for Enhanced Business Intelligence in Financial Analytics
DOI:
https://doi.org/10.63345/Keywords:
Generative artificial intelligence, business intelligence, financial analytics, retrieval-augmented generation, numerical reasoning, evidence traceability.Abstract
Financial business intelligence entails the proper combination of numerical indicators, corporate disclosures, and explanations. Dashboards enable structured reporting but provide little assistance if users need to link financial tables to textual evidence. This paper suggests an evidence-grounded framework combining financial documents retrieval, controlled numerical computation, and source-supported analytical reporting. The key research question is whether separation of language generation from arithmetic execution improves financial answer correctness without increasing response time. The evaluation protocol defines 600 analytical questions about financial extraction, ratio calculation, period comparison, and explanatory synthesis. Under provisional numerical assumptions, the framework yields 87.50% accuracy, in comparison with 65.00% for the language-model-only configuration and 78.00% for retrieval-augmented generation without computational controls. The framework allows for explicit abstention on questions without sufficient evidence. The paper contributes with a methodology of evaluating generative business intelligence based on the criteria of numerical correctness, evidence traceability, and accountable interpretation.References
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