Automating financial reporting with natural language processing: A review and case analysis

Adedoyin Tolulope Oyewole,; Omotayo Bukola Adeoye,; Wilhelmina Afua Addy,; Chinwe Chinazo Okoye,; Onyeka Chrisanctus Ofodile,; Chinonye Esther Ugochukwu,

doi:10.30574/wjarr.2024.21.3.0688

Cited by 3 publications

(1 citation statement)

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“…In computer linguistics, AT Oyewole et al [14]. academics have studied the intricacies of language structure and semantics to create algorithms and models that can accurately process and analyze textual input.…”

Section: Related Workmentioning

confidence: 99%

Financial Statement Text Information Mining and Key Information Extraction Model Construction

Yi Xu

2024

jes

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Financial statement text information mining and key information extraction model design are critical areas of research that aim to use advanced computational approaches to extract important insights from textual data contained in financial documents. In this work, they look at methodologies, techniques, and applications that combine natural language processing (NLP) and machine learning to automate financial statement interpretation. To lay the groundwork for the research, researchers first conduct a thorough examination of existing literature in interdisciplinary domains such as computational linguistics, information retrieval, and finance. Building on insights from earlier studies, they design and use unique NLP approaches, such as named entity identification, syntactic parsing, sentiment analysis, and topic modelling, to extract essential financial metrics from textual data. Additionally, they create machine learning models that are suited to the peculiarities of financial terminology and reporting standards, combining domain-specific knowledge with linguistic experience to improve accuracy and reliability. They demonstrate the efficacy and scalability of the technique in automating the extraction of crucial financial information, such as revenue trends, cost patterns, and risk factors, through rigorous testing on real-world financial data. These results highlight the transformative power of natural language processing and machine learning in financial analysis, providing stakeholders in finance and accounting with actionable intelligence for informed decision-making, risk assessment, and compliance monitoring. By bridging the gap between computational linguistics and financial analysis, this study advances financial text analysis and provides the framework for future research and innovation in this emerging field.

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Section: Related Workmentioning

confidence: 99%

Financial Statement Text Information Mining and Key Information Extraction Model Construction

Yi Xu

2024

jes

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Analysis of Deep Learning Techniques to Facilitate Automation of Financial Statements for Accounting Programs in Higher Education Institutions

Liu

2024

Applied Mathematics and Nonlinear Sciences

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The rapid development of information technology and mobile Internet technology has brought about innovations in the field of education. The application of financial statement automation analysis methods in the teaching of accounting majors in higher vocational colleges and universities is conducive to improving the quality of the training of accounting talents in higher vocational colleges and universities so as to accelerate the construction of the modern accounting industry college. This study proposes the application of relevant deep learning techniques in the process of automated analysis of financial statements in terms of recognition and analysis, such as text detection based on CTPN and text recognition technology based on CRNN-Attention, which makes the processing of financial statements more automated and intelligent. It is also proposed to utilize the K-means algorithm to perform cluster analysis on the model output to extract the financial status of the company. To evaluate the model’s performance, several financial statement images of 12 companies are sampled for example analysis. The experimental data shows that the model has a high percentage of image skew correction, e.g., the ratio of image skew correction for the income statement and cash flow statement is 100%. The recognition speed of the model is also faster, with the consumption time of the profit and loss statement recognition task being around 0.3 seconds in 20 tests. In the financial statement analysis session, each of the three clusters of companies under the K-means algorithm clustering has its characteristics, with the first cluster of companies having a stronger quality of earnings, the third cluster of companies having outstanding profitability, and the second cluster of companies having average indicator data.

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IoT-Driven Transformation of Circular Economy Efficiency: An Overview

Turskis,

Šniokienė

2024

MCA

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The intersection of the Internet of Things (IoT) and the circular economy (CE) creates a revolutionary opportunity to redefine economic sustainability and resilience. This review article explores the intricate interplay between IoT technologies and CE economics, investigating how the IoT transforms supply chain management, optimises resources, and revolutionises business models. IoT applications boost efficiency, reduce waste, and prolong product lifecycles through data analytics, real-time tracking, and automation. The integration of the IoT also fosters the emergence of inventive circular business models, such as product-as-a-service and sharing economies, offering economic benefits and novel market opportunities. This amalgamation with the IoT holds substantial implications for sustainability, advancing environmental stewardship and propelling economic growth within emerging CE marketplaces. This comprehensive review unfolds a roadmap for comprehending and implementing the pivotal components propelling the IoT’s transformation toward CE economics, nurturing a sustainable and resilient future. Embracing IoT technologies, the authors embark on a journey transcending mere efficiency, heralding an era where economic progress harmonises with full environmental responsibility and the CE’s promise.

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Automating financial reporting with natural language processing: A review and case analysis

Cited by 3 publications

References 24 publications

Financial Statement Text Information Mining and Key Information Extraction Model Construction

Financial Statement Text Information Mining and Key Information Extraction Model Construction

Analysis of Deep Learning Techniques to Facilitate Automation of Financial Statements for Accounting Programs in Higher Education Institutions

IoT-Driven Transformation of Circular Economy Efficiency: An Overview

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