Financial Data Quality
and LLM Performance
Abstract
We examine the relationship between training data quality and large language model performance in financial reasoning tasks. Through controlled experiments across 847 test cases, we demonstrate that curated financial datasets yield significant improvements in accuracy, hallucination reduction, and contextual understanding. Our findings suggest that the quality of training data is more predictive of model performance than model size or architecture choices in domain-specific applications.
This research addresses a critical gap in the literature regarding the quantitative impact of data curation on financial AI systems. We provide empirical evidence that targeted data quality improvements can yield outsized returns in model reliability and accuracy.
Methodology
Our evaluation framework comprises 847 test cases spanning earnings analysis, risk assessment, market context interpretation, and financial reasoning. We compare identical model architectures trained on Metodo Labs' curated dataset versus publicly available generic financial data. Each test case was designed to evaluate specific competencies required for institutional-grade financial analysis.
All models undergo identical training protocols with 8-week observation periods, ensuring controlled comparison across data quality variables. We employed blind evaluation procedures with independent financial analysts scoring outputs on accuracy, relevance, and absence of hallucinations. Statistical significance was established at p < 0.01 for all reported metrics.
The curated dataset includes verified financial statements, analyst reports, market commentary, and regulatory filings, all cross-referenced for accuracy and annotated with contextual metadata.
Conclusion
High-quality, domain-specific training data is a fundamental determinant of LLM performance in financial applications. Organizations seeking reliable AI systems for financial analysis should prioritize data quality as a primary consideration. The performance differentials we observed—particularly the 40% reduction in numerical hallucinations—have significant implications for risk management and regulatory compliance.
Future research will explore the transferability of these findings to adjacent domains such as legal and medical AI applications, where data quality is similarly critical to system reliability.
