A New Form Textbook Framework for Financial Big Data Analysis Based on RPA
- DOI
- 10.2991/978-2-38476-346-7_15How to use a DOI?
- Keywords
- RPA; Financial Big Data Analysis; New Form Textbook
- Abstract
Big data has put forward new requirements for traditional financial accounting theory and practice. However, the research and publication of textbooks on financial big data analysis are still in their infancy. RPA supports code-free development, which is relatively easier to use than Python and more universally convenient than BI. However, current research on RPA in the field of finance and accounting and the textbooks published lack a comprehensive and complete analysis of financial big data. This study proposes a new form of textbook construction framework path for financial big data analysis based on RPA and provides specific implementation plans. It mainly includes building project-based, digital, and modular textbooks; summarizing the basic operations of using RPA for financial big data collection and organization; realizing specific RPA operations for different aspects of solvency, operation, profitability, and development capabilities; and completing comprehensive capability analysis and forming analysis reports.
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Lei Sun AU - Zhuojing Fu PY - 2024 DA - 2024/12/27 TI - A New Form Textbook Framework for Financial Big Data Analysis Based on RPA BT - Proceeding of the 2024 International Conference on Diversified Education and Social Development (DESD 2024) PB - Atlantis Press SP - 107 EP - 113 SN - 2352-5398 UR - https://doi.org/10.2991/978-2-38476-346-7_15 DO - 10.2991/978-2-38476-346-7_15 ID - Sun2024 ER -