Comprehensive Review on Natural Language Generation for Automated Report Writing in Finance.
Publication Date: 16/08/2024
Author(s): Abbas Sani, Faisal Rasheed, Bachcha Lal Pal, Ajay Singh Dhabariya, Usman Haruna, Babangida Salisi Mu'az, Abdulgaffar Abubakar Yahya.
Volume/Issue: Volume 7 , Issue 3 (2024)
Abstract:
The financial industry is transforming with the advent of Natural Language Generation (NLG), a subset of Natural Language Processing (NLP), which automates data conversion into coherent and contextually relevant narratives. This paper presents a comprehensive review of NLG's application in financial report automation, tracing its evolution from template-based methods to advanced deep learning and knowledge graph techniques. We discuss the relevance of NLG in automating report generation, its role in enhancing data analysis and decision-making, and its potential to improve investor communications and compliance with regulations. The paper identifies research gaps, including the need for optimization, accuracy improvement, and the integration of machine learning models for better classification and prediction. A proposed methodology for structured report generation is outlined, leveraging deep learning architectures such as RNNs and LSTMs. Future work aims to address these gaps and further integrate NLG into financial reporting, promising to streamline processes, reduce costs, and provide more personalized and insightful financial narratives.
Keywords:
Natural language processing, Natural language generation, Machine learning, deep learning, RNN, LSTM, Report Generation, Automated report writing, Financial Report Generation.
