Integrating AI-powered knowledge graphs and NLP for intelligent interpretation, summarization, and cross-border financial reporting harmonization

In an environment of increasingly complicated and globally interconnected financial systems, challenges related to harmonization in cross-border reporting are magnifying. Differences in regulation, language, data siloing, and the further proliferation of unstructured disclosures remain obstacles to the success of transparency, compliance and efficiency initiatives. In this paper we discuss a new integration of AI-driven Knowledge Graphs (KG) and NLP that we believe can form part of this solution; a new way of thinking about financial interpretation and summarization over jurisdictions. As structured semantic representations of financial entities and their attributes and inter-relationships, KGs facilitate machines to perceive and put information into context. And when combined with state-of-the-art NLP models like transformers and domain-specific large language models (LLMs), this architecture is able to accurately and interpretably extract, disambiguate, and summarize financial disclosures, audit reports, and regulatory filings. These capabilities are particularly useful for multinationals, auditors, and regulators which, for example, are looking to cross-mapp divergent financial standards (such as IFRS and GAAP) or even automate compliance mapping. The paper describes a system design that exploits mutli-source data, entity recognition, relation extraction, and multilingual semantic alignment based on AI-enhanced ontologies. Real-world examples from the EU, ASEAN and North America shows how artificial-intelligence-powered tools can cut through manual ground work, spot discrepancies in reporting and create reconciled summaries for stakeholders on both sides of the border. The results highlight the potential of NLP applied to Knowledge Graphs not only for the automation of reporting workflows but as a framework for delivering smart, explainable financial governance systems.