[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123233-en":3,"doc-seo-123233-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123233,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Automating Data Labeling and Annotation Pipelines for Large Language Models (LLMs) in the Financial Industry using Machine Learning","The growing magnitude and intricacy of financial information create major barriers for machine learning systems, especially Large Language Models (LLMs) that require extensive, high-quality labeled datasets. Manual data labeling in finance is costly, slow, and difficult to scale, limiting LLM training and fine-tuning. This research proposes an automated data labeling and annotation framework using Principal Component Analysis (PCA) for dimensionality reduction and feature discovery, and Decision Trees (DT) for categorization and automation, aiming to improve labeling precision, efficiency, and scalability while preserving data integrity for ongoing LLM use.","Automating Data Labeling and Annotation Pipelines for Large Language Models (LLMs) in the Financial Industry using Machine Learning  \nSai Arundeep Aetukuri  \nData Analytics Engineer  \nOMV America LLC  \n1500 S Dairy Ashford Rd STE 242, Houston, TX 77077  \nEmail: [asaiarun996@gmail.com](asaiarun996@gmail.com)  \nAbstract  \nThe growing magnitude and intricacy of financial information present considerable obstacles for machine learning systems, especially Large Language Models (LLMs), which necessitate extensive, high-caliber labeled datasets for training. Conventional manual labeling approaches are ineffective and expensive, constraining the expandability of LLMs in the finance sector. This research introduces an automated data labeling and annotation framework utilizing Principal Component Analysis (PCA) and Decision Trees (DT), two robust machine learning methodologies, to optimize and improve the labeling procedure for financial information. PCA is utilized for reducing dimensionality, assisting in the identification of crucial features and trends in financial datasets, while DTs are employed to categorize data and automate the annotation process. The proposed system aims to enhance the precision, effectiveness, and scalability of the data labeling procedure, ultimately facilitating the ongoing training of LLMs with contextually pertinent, labeled financial data.  \nKeywords:Large Language Models (LLMs), Banking Industry,Machine learning(ML), data-driven technologies, banking industry, data governance, data quality, Principal Component Analysis(PCA), Decision Trees(DT), predictive accuracy, data integrity, data security.  \n1.Intruoduction  \nThe financial industry's increasing reliance on artificial intelligence, particularly Large Language Models (LLMs), has created an urgent need for efficient and accurate data labeling and annotation processes. This challenge is particularly acute given the industry's unique requirements for precision, regulatory compliance, and handling of sensitive information Recent advances in automated data labeling and annotation pipelines have emerged as a crucial solution to address the scalability and quality challenges in preparing financial datasets for LLM training and fine-tuning The automation of data labeling in financial contexts presents unique challenges, includingthe need to handle complex financial terminology, maintain consistency across diverse document types, and ensure compliance with regulatory requirements Traditional manual annotation methods are not only timeconsuming and expensive but also prone to inconsistencies and human error, making automation an attractive  \nalternative Recent developments in weak supervision, active learning, and semi-supervised learning have significantly contributed to the advancement of automated labeling systems These approaches have demonstrated particular promise in handling financial documents, from regulatory filings to market reports and transaction data The integration of domain-specific knowledge bases and ontologies has further enhanced the accuracy and reliability of automated labeling systems The emergence of specialized frameworks and tools has facilitated the implementation of automated labeling pipelines, enabling financial institutions to process large volumes of data more efficiently .These systems often incorporate quality control mechanisms and human-in-the-loop validation processes to ensure the accuracy of labeled datasets Furthermore, the application of transfer learning and few-shot learning techniques has reduced the initial data requirements for establishing effective labeling systems.  \nThe banking industry has increasingly turned to advanced machine learning techniques, particularly the combination of Principal Component Analysis (PCA) and Decision Trees (DT), to enhance decision-making processes, risk assessment, and customer service [1] . However, the efficacy of these models heavily relies on the quality of data used to train ","cbCaikTSxoNH2Dqw","https://ap.wps.com/l/cbCaikTSxoNH2Dqw","pdf",933632,1,13,"English","en",105,"# 1. Intruoduction\n## Financial industry needs for LLM data labeling\n## Limitations of manual labeling\n## Automated pipeline methods and supporting techniques\n## Data governance and data quality in ML performance","[{\"question\":\"Why is automated data labeling necessary for LLMs in the financial industry?\",\"answer\":\"Financial LLM training requires large, high-quality labeled datasets, while manual labeling is expensive, slow, and hard to scale. Automation addresses scalability and quality constraints for training and fine-tuning.\"},{\"question\":\"How do PCA and Decision Trees contribute in the proposed labeling framework?\",\"answer\":\"PCA reduces dimensionality and helps identify important features and trends in financial datasets. Decision Trees categorize data to automate the annotation process.\"},{\"question\":\"What role does data governance play in machine learning for banking?\",\"answer\":\"Data governance manages data availability, usability, integrity, and security, which directly supports data quality and model reliability. It also helps meet regulatory compliance requirements.\"}]","Automating Data Labeling and Annotation Pipelines for Large Language Models (LLMs) in the Financial Industry using Machine Learning | PDF",1785815370,33,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"automating-data-labeling-and-annotation-pipelines-for-large-language-models-llms-in-the-financial-industry-using-machine-learning","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/automating-data-labeling-and-annotation-pipelines-for-large-language-models-llms-in-the-financial-industry-using-machine-learning/123233/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-04",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is automated data labeling necessary for LLMs in the financial industry?","Question",{"text":76,"@type":77},"Financial LLM training requires large, high-quality labeled datasets, while manual labeling is expensive, slow, and hard to scale. Automation addresses scalability and quality constraints for training and fine-tuning.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How do PCA and Decision Trees contribute in the proposed labeling framework?",{"text":81,"@type":77},"PCA reduces dimensionality and helps identify important features and trends in financial datasets. Decision Trees categorize data to automate the annotation process.",{"name":83,"@type":74,"acceptedAnswer":84},"What role does data governance play in machine learning for banking?",{"text":85,"@type":77},"Data governance manages data availability, usability, integrity, and security, which directly supports data quality and model reliability. 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