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LLM-Based Examination of Eligibility Criteria from Securities Prospectuses at the German Central Bank

Verifying the eligibility of securities as collateral is a key responsibility of the Deutsche Bundesbank. However, manually verifying these assets against legal and financial criteria within lengthy, semi-structured, and often bilingual prospectuses is a resource-intensive task. While previous efforts utilized traditional Named Entity Recognition (NER) for information extraction, these methods often struggle with OCR noise, linguistic variance, and rigid span-based constraints, as well as requiring manual annotation of documents to generate adequate training data for all the required annotation types. In this paper, we present the first case study applying Large Language Models (LLMs) to the eligibility examination process, shifting the paradigm toward a generative Information Extraction pipeline. Our approach decomposes the task into extraction, normalization, and interpretation, allowing for greater flexibility in handling noisy text and interleaved German-English content. We further introduce a value-based evaluation methodology using LLM-as-a-judge, which offers a more semantic assessment than offset-based metrics. Our results demonstrate that LLM-based systems achieve high precision (up to 91%) in document-level eligibility, exhibiting a conservative operating profile that minimizes false acceptance.