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会计和财务报表自动分析系统.docx
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会计和财务报表自动分析系统.docx
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California
State
University,
San
Bernardino
CSUSB
ScholarWorks
Electronic
Theses,
Projects,
and
Dissertations
Office of Graduate Studies
5-
2023
ACCOUNTING AND FINANCIAL STATEMENTS AUTO ANALYSIS
SYSTEM
Zhen
Jia
Follow this and additional works at: https://scholarworks.lib.csusb.edu/etd
Part of the Business Analytics Commons, Business Intelligence Commons, and the Systems Science
Commons
Recommended
Citation
Jia, Zhen, "ACCOUNTING AND FINANCIAL STATEMENTS AUTO ANALYSIS SYSTEM" (2023). Electronic
Theses, Projects, and Dissertations. 1675.
https://scholarworks.lib.csusb.edu/etd/1675
This Project is brought to you for free and open access by the Office of Graduate Studies at CSUSB ScholarWorks.
It has been accepted for inclusion in Electronic Theses, Projects, and Dissertations by an authorized administrator
of CSUSB ScholarWorks. For more information, please contact scholarworks@csusb.edu.
ACCOUNTING AND FINANCIAL STATEMENTS
AUTO ANALYSIS SYSTEM
A Project
Presented to the
Faculty of
California State University,
San Bernardino
In Partial Fulfillment
of the Requirements for the Degree
Master of Science
in
Information Systems and Technology
by
Zhen Jia
May 2023
ACCOUNTING AND FINANCIAL STATEMENTS
AUTO ANALYSIS SYSTEM
A Project
Presented to the
Faculty of
California State University,
San Bernardino
by
Zhen Jia
May 2023
Approved by:
Dr. Lewis A. Njualem, Committee Chair
Dr. Conrad Shayo, Member, Chair of the Information and Decision Sciences
Department
© 2023 Zhen Jia
iii
ABSTRACT
This project was motivated by the need to revolutionize the generation of
financial statements and financial analysis process thus speeding up business
decision making. The research questions were: 1) How can machine learning
[37] increase the speed of financial statement preparation and automate financial
statements analysis? 2) How can businesses balance the benefits of automating
financial analysis with potential concerns around privacy, data security, and bias?
3) Can the Java J2EE framework [32] provide a reliable running environment for
machine learning?
The findings were: 1) Machine learning can significantly increase the
accuracy and speed of financial analysis. Using machine learning algorithms,
financial data can be processed and analyzed in real-time, allowing for quicker
and more precise financial analysis. Machine learning models can identify
patterns and trends in financial data that may not be easily detectable by
humans, leading to more accurate financial statements and analysis.
Additionally, machine learning can automate repetitive tasks in the financial
analysis process, saving time and resources for businesses. 2) Businesses need
to carefully balance the benefits of automating financial analysis with potential
concerns around privacy, data security, and bias. While machine learning can
offer significant advantages in terms of accuracy and speed, it also requires
handling sensitive financial data. Therefore, it is crucial for businesses to
implement robust data security measures to protect against potential data
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