Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Vidhani, Nilesh; Ramasamy, Gobi; Syam Mohan, E. Title A name given to the resource From Text to Ticker: A Comprehensive Survey and Methodological Guide to Named Entity Recognition in Finance Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Proceedings of 6th International Conference on Expert Clouds and Applications, ICOECA 2026;pp.749-755 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICOECA68095.2026.11485558" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICOECA68095.2026.11485558</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105038080798?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105038080798?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Vidhani N., Christ University, Department of Computer Science, Bangalore, India; Ramasamy G., Christ University, Department of Computer Science, Bangalore, India; Syam Mohan E., Christ University, Department of Computer Science, Bangalore, India Description An account of the resource The financial industry generates vast volumes of unstructured textual data from sources such as regulatory filings, news articles, social media, and earnings call transcripts. Extracting structured and actionable intelligence from this data remains a significant challenge. Named Entity Recognition (NER) is a fundamental task in natural language processing that supports this process by identifying and categorizing key information within text. However, the linguistic complexity, contextual ambiguity, and domain-specific terminology of financial text require specialized approaches that extend beyond general-purpose NER models. This paper presents a comprehensive survey and methodological guide to Financial Named Entity Recognition (Fin-NER). It begins by introducing the core concepts of NER while highlighting the unique challenges posed by financial text. The paper then reviews the evolution of Fin-NER approaches, ranging from rule-based systems and classical machine learning techniques to modern deep learning architectures. Furthermore, it analyzes the distinction between fine-tuned transformer-based models and general-purpose large language models in the current research. The study also examines commonly used datasets and evaluation metrics for benchmarking Fin-NER systems. Finally, it discusses key findings, existing methodological limitations, and future research directions, including hybrid modeling strategies, cross-lingual datasets, and the development of more reliable and explainable systems. Overall, this work serves both as a scholarly review of the Fin-NER field and as a practical guide for researchers and practitioners seeking to transform unstructured financial text into structured and informative representations. 2026 IEEE. Subject The topic of the resource Benchmark Analysis; Deep Learning; Domain Adaptation; Financial Natural Language Processing (Fin-NLP); Financial Text Mining; FinBERT; Information Extraction; Large Language Models (LLMs); Named Entity Recognition (NER); Transformer Models Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833157451-2; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online