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Natural language processing in finance: A survey
Information Fusion ( IF 14.7 ) Pub Date : 2024-10-28 , DOI: 10.1016/j.inffus.2024.102755
Kelvin Du, Yazhi Zhao, Rui Mao, Frank Xing, Erik Cambria

This survey presents an in-depth review of the transformative role of Natural Language Processing (NLP) in finance, highlighting its impact on ten major financial applications: (1) financial sentiment analysis, (2) financial narrative processing, (3) financial forecasting, (4) portfolio management, (5) question answering, virtual assistant and chatbot, (6) risk management, (7) regulatory compliance monitoring, (8) Environmental, Social, Governance (ESG) and sustainable finance, (9) explainable artificial intelligence (XAI) in finance and (10) NLP for digital assets. With the integration of vast amounts of unstructured financial data and advanced NLP techniques, the study explores how NLP enables data-driven decision-making and innovation in the financial sector, alongside the limitations and challenges. By providing a comprehensive analysis of NLP applications combining both academic and industrial perspectives, this study postulates the future trends and evolution of financial services. It introduces a unique review framework to understand the interaction of financial data and NLP technologies systematically and outlines the key drivers, transformations, and emerging areas in this field. This survey targets researchers, practitioners, and professionals, aiming to close their knowledge gap by highlighting the significance and future direction of NLP in enhancing financial services.

中文翻译:


金融领域的自然语言处理:一项调查



本调查深入回顾了自然语言处理 (NLP) 在金融领域的变革性作用,强调了其对十个主要金融应用的影响:(1) 金融情绪分析,(2) 金融叙事处理,(3) 金融预测,(4) 投资组合管理,(5) 问答、虚拟助手和聊天机器人,(6) 风险管理,(7) 监管合规监控,(8) 环境、社会、 治理 (ESG) 和可持续金融,(9) 金融领域的可解释人工智能 (XAI) 和 (10) 数字资产的 NLP。通过整合大量非结构化金融数据和先进的 NLP 技术,该研究探讨了 NLP 如何在金融领域实现数据驱动的决策和创新,以及局限性和挑战。通过结合学术和工业视角对 NLP 应用进行全面分析,本研究假设了金融服务的未来趋势和演变。它引入了一个独特的审查框架,以系统地理解金融数据和 NLP 技术的相互作用,并概述了该领域的关键驱动因素、转型和新兴领域。本调查以研究人员、从业者和专业人士为对象,旨在通过强调 NLP 在增强金融服务方面的重要性和未来方向来缩小他们的知识差距。
更新日期:2024-10-28
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