Bringing big data analytics in accounting into classrooms: A case combing XBRL data and high-performance computing (HPC) resources
Yang, Liu
Yang, Liu
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Issue Date
2024-05-23
Type
Conference paper
Genre
Keywords
Accounting--data analytics,Data analytics,XBRL data,High-performance computing resources
Subjects (LCSH)
Citation
Yang, L. (2024). Bringing big data analytics in accounting into classrooms: A case combing XBRL data and high-performance computing (HPC) resources. 3rd OAK Supercomputing Conference. https://www.wichita.edu/services/hpc/oaksupercompute2024/index.php
Abstract
Big data analytics is the process of collecting, processing, and analyzing large and complex data sets to extract valuable insights and support decision making. Despite the critical importance of big data analytics skills in this age when data volume is experiencing explosive growth, very few accounting programs teach big data analytics effectively. To help students gain knowledge and experiences of applying big data analytics in accounting, we developed a case for financial misreporting detection using multiple large real-world financial statement datasets based on eXtensible Business Reporting Language (XBRL). To deal with the challenges of high requirements on computer resources for efficiently sharing and processing large XBRL formatted data, we take advantage of High-Performance Computing (HPC) resources that can be accessed by educators and students easily for free at most universities and other public research institutions. Through this case, students will learn about XBRL concepts such as extension taxonomies and how to use free and popular tools such as Python to process and analyze XBRL data. They will learn how to read in large raw data sets, extract financial line items, transform the data, and perform basic data analysis such as descriptive statistics and data visualization for the distribution of earnings and other financial line items that can indicate potential financial misreporting cases. This approach will equip accounting students who have little or no statistical background and programming skills with basic big data analytics skills for detailed financial data analysis.
Table of Contents
Description
3rd OAK Supercomputing Conference, Wichita State University, May 20-24, 2024.
Research presented on May 23, 2024.
See abstract here: https://www.wichita.edu/services/hpc/oaksupercompute2024/oak_abstracts.php
See abstract here: https://www.wichita.edu/services/hpc/oaksupercompute2024/oak_abstracts.php
Publisher
3rd OAK Supercomputing Conference