Treffer: ACCOUNTING AND FINANCIAL STATEMENTS AUTO ANALYSIS SYSTEM

Title:
ACCOUNTING AND FINANCIAL STATEMENTS AUTO ANALYSIS SYSTEM
Authors:
Source:
Electronic Theses, Projects, and Dissertations
Publisher Information:
CSUSB ScholarWorks
Publication Year:
2023
Collection:
California State University, San Bernardino Research: CSUSB ScholarWorks
Document Type:
Fachzeitschrift text
File Description:
application/pdf
Language:
unknown
Accession Number:
edsbas.F2C1F656
Database:
BASE

Weitere Informationen

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 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 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 breaches and ensure compliance with privacy regulations. Additionally, businesses need to be mindful of potential biases in machine learning algorithms, as biased algorithms can result in biased financial analysis. Regular audits and monitoring of machine learning models should be conducted to address and mitigate any potential biases. 3) The Java J2EE framework can provide a reliable running environment for machine learning. Java J2EE (Java 2 Platform, Enterprise Edition) is a widely used and ...