arXiv:2509.09544v3 Announce Type: replace Abstract: Financial NLP has evolved rapidly since late 2022, outpacing narrative surveys. We introduce MetaGraph, a methodology for extracting typed knowledge graphs from scientific corpora using ontology-guided LLM extraction to enable structured, large-scale trend analysis. Applied to 681 papers on GenAI in Finance (2022-2025), MetaGraph reveals three phases: early LLM-driven expansion of tasks and datasets, growing emphasis on limitations and risk, and a shift toward modular, system-oriented methods (e.g., retrieval-augmented designs). We release th
The rapid evolution of generative AI in financial NLP since late 2022 necessitates new methods for structured trend analysis.
This meta-analysis provides a structured view of GenAI evolution in a critical sector, highlighting key phases and emerging design patterns like retrieval-augmented generation.
Understanding of GenAI's development in finance shifts from narrative surveys to data-driven trend analysis, revealing specific phases and architectural shifts.
- · Financial institutions adopting modular AI systems
- · Developers of RAG architectures
- · Researchers focused on AI limitations and risk
- · Companies relying on outdated GenAI models
- · Traditional narrative-based analysis methods
- · Financial data providers without GenAI integration
Financial NLP development will increasingly emphasize modular, system-oriented approaches.
Investment in AI governance and risk mitigation within financial applications will accelerate.
The application of this meta-analysis methodology could extend to other rapidly evolving AI application domains.
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