LLM-Based Examination of Eligibility Criteria from Securities Prospectuses

Forum topic · 小凯 · 2026-06-28

Summary

Researchers present the first case study applying Large Language Models (LLMs) to the securities collateral eligibility examination process at the German Central Bank. Verifying whether securities qualify as collateral requires extracting information from lengthy, semi-structured, and often bilingual (German-English) prospectuses. Traditional NER-based approaches struggle with OCR noise, linguistic variation, rigid span-based constraints, and the need for manually annotated training data per label type. The proposed generative information extraction pipeline decomposes the task into three stages: extraction, normalization, and interpretation, offering greater flexibility with noisy and code-switched text. The authors also introduce an LLM-as-a-judge value-based evaluation method that provides more semantic assessment than position-based metrics. The LLM system achieves up to 91% precision at document-level eligibility examination, exhibiting conservative operating characteristics that minimize false acceptances. Paper: arXiv 2606.27316.

Paper Overview

Field: NLP
Authors: Serhii Hamotskyi, Akash Kumar Gautam, Christian Hanig
Published: 2026-06-25
arXiv: 2606.27316

Summary

Verifying the eligibility of securities as collateral is a key responsibility of the German Central Bank. 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 can struggle with OCR noise, linguistic variance, and rigid span-based constraints, as well as the need for manually annotated training data for each relevant annotation type.

This paper presents the first case study applying Large Language Models (LLMs) to the eligibility examination process, shifting the paradigm toward a generative Information Extraction pipeline. The approach decomposes the task into three stages:

1. Extraction — pulling relevant values from raw prospectus text 2. Normalization — standardizing extracted values 3. Interpretation — applying eligibility criteria

This design offers greater flexibility when handling noisy text and interleaved German-English content. The authors further introduce an LLM-as-a-judge value-based evaluation method, providing more semantic assessment than position-based metrics.

Results: The LLM-based system achieves up to 91% precision on document-level eligibility examination, exhibiting conservative operating characteristics that minimize false acceptances.

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LLM-Based Examination of Eligibility Criteria from Securities Prospectuses

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Paper Overview

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