llm-based-examination-of-eligibility-criteria-from-securities-prospectuses-at-the-german-central-bank-37ae5b1a·1 events·first seen Aliases: LLM-Based Examination of Eligibility Criteria from Securities Prospectuses at the German Central Bank
Researchers present the first case study applying LLMs to the Deutsche Bundesbank's securities collateral eligibility verification process, replacing traditional NER-based pipelines with a generative information extraction approach. The system decomposes the task into extraction, normalization, and interpretation stages, handling OCR noise and bilingual (German-English) content in lengthy financial prospectuses. Results show up to 91% precision in document-level eligibility decisions with a conservative false-acceptance profile. The paper also introduces an LLM-as-a-judge evaluation methodology for semantic assessment of extraction quality.