ArXiv

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

2026-06-25 · 17:29 UTC ·Serhii Hamotskyi, Akash Kumar Gautam, Christian Hänig ·1 min read
Authors
Serhii Hamotskyi, Akash Kumar Gautam, Christian Hänig
Categories
cs.CL
arXiv
https://arxiv.org/abs/2606.27316v1
PDF
https://arxiv.org/pdf/2606.27316v1

Brief

The paper addresses automated verification of securities collateral eligibility at the German Central Bank by replacing rigid span-based NER with a generative LLM information-extraction pipeline (extraction, normalization, interpretation) tailored for noisy, semi-structured, bilingual prospectuses. Presented as the first case study in this domain, the approach attains up to 91% document-level precision and introduces a value-based LLM-as-judge evaluation that better captures semantic correctness than location-based metrics.

Why it matters

LLM-based pipeline achieved up to 91% document-level precision for eligibility decisions, operating conservatively to minimize false acceptance (paper reports high precision at the document level).

Key details

  • This is the first case study applying generative LLM information-extraction to the German Central Bank’s collateral-eligibility checks, using a three-stage pipeline (extraction, normalization, interpretation) to handle noisy, bilingual (German–English) prospectuses.
  • Authors introduce a value-based evaluation with an LLM-as-a-judge for semantic assessment, contrasting prior span-based NER approaches that struggle with OCR noise, linguistic variance, and expensive manual annotation.
Source evidence

Abstract

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, and the need for manually annotated training data for each relevant annotation type. 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 location-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.

Journal: Proceedings of the 7th Financial Narrative Processing Workshop (FNP 2026) at LREC 2026, pp. 1-10, 2026