Maestría en Ciencias de los Datos y Analítica (tesis)
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Examinando Maestría en Ciencias de los Datos y Analítica (tesis) por Materia "Accounting Standards Encoding"
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Publicación Respuestas a preguntas en contratos de arrendamiento bajo la normativa ASC (Accounting Standards Codification) 842 utilizando grandes modelos de lenguaje(Universidad EAFIT, 2025) Armendáriz Peña, David Adrián; Olarte Hernández, TomásThe ASC 842 standard, part of GAAP (Generally Accepted Accounting Principles) in the United States, establishes rules for recording leases in financial statements, enhancing transparency and comparability. However, its implementation poses significant challenges, such as interpreting complex contracts and extracting key information, tasks often performed manually, leading to high costs and errors. This thesis develops an automated system to address relevant questions about lease contracts using Natural Language Processing, Large Language Models, and Retrieval Augmented Generation. The goal is to reduce reliance on external consultants by identifying the information needed to draft technical accounting memos automatically. The GenAI Lifecycle methodology was employed, including text vectorization using embedding models and data storage in vector databases like Pinecone. Using lease contracts obtained from the Security Exchange Comission, the system was developed to answer key questions such as dates, purchase options, or renewal terms, achieving at least 70% accuracy. The results demonstrate that the system significantly reduces the time and costs associated with contract analysis, improving the accuracy in compliance with ASC 842. This approach has practical implications for the accounting industry, offering a scalable solution that democratizes access to advanced artificial intelligence tools, enabling companies to efficiently manage their regulatory processes. This work represents a significant step forward in integrating artificial intelligence to solve real-world accounting problems, fostering innovation in the extraction and analysis of regulatory information.