@inproceedings{hamotskyi-etal-2026-llm,
title = "{LLM}-Based Examination of Eligibility Criteria from Securities Prospectuses at the {G}erman Central Bank",
author = {Hamotskyi, Serhii and
Gautam, Akash Kumar and
H{\"a}nig, Christian},
editor = "El-Haj, Mo and
Moreno Sandoval, Antonio and
Garcia-Serrano, Ana and
Chen, Chung-Chi and
Rayson, Paul and
Torterolo Orta, Yanco Amor and
Martinez, Paloma and
Porta, Jordi",
booktitle = "The 7th Financial Narrative Processing Workshop",
month = may,
year = "2026",
address = "Palma de Mallorca, Spain",
publisher = "European Language Resources Association (ELRA)",
url = "https://aclanthology.org/2026.fnp-1.1/",
doi = "10.63317/3dpiow9bkvmm",
pages = "1--11",
abstract = "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."
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T LLM-Based Examination of Eligibility Criteria from Securities Prospectuses at the German Central Bank
%A Hamotskyi, Serhii
%A Gautam, Akash Kumar
%A Hänig, Christian
%Y El-Haj, Mo
%Y Moreno Sandoval, Antonio
%Y Garcia-Serrano, Ana
%Y Chen, Chung-Chi
%Y Rayson, Paul
%Y Torterolo Orta, Yanco Amor
%Y Martinez, Paloma
%Y Porta, Jordi
%S The 7th Financial Narrative Processing Workshop
%D 2026
%8 May
%I European Language Resources Association (ELRA)
%C Palma de Mallorca, Spain
%F hamotskyi-etal-2026-llm
%X 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.
%R 10.63317/3dpiow9bkvmm
%U https://aclanthology.org/2026.fnp-1.1/
%U https://doi.org/10.63317/3dpiow9bkvmm
%P 1-11
Markdown (Informal)
[LLM-Based Examination of Eligibility Criteria from Securities Prospectuses at the German Central Bank](https://aclanthology.org/2026.fnp-1.1/) (Hamotskyi et al., FNP 2026)
ACL