Date of Award

5-2023

Document Type

Project

Degree Name

Master of Science in Information Systems and Technology

Department

Information and Decision Sciences

First Reader/Committee Chair

Njualem, Lewis

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 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 mature framework for developing enterprise applications, including machine learning applications. It offers scalability, reliability, and security features that are essential for running machine learning algorithms in a production environment. Java J2EE provides robust support for distributed computing, allowing for efficient processing of large financial datasets. Furthermore, it offers a wide range of libraries and tools for implementing machine learning algorithms, making it a viable choice for running machine learning applications in the financial industry.

The conclusions were: 1) Machine learning has the potential to significantly increase the accuracy and speed of financial analysis, thereby revolutionizing the generation of financial statements and the financial analysis process. Various machine learning algorithms, such as decision trees, random forests, and deep learning algorithms, can be utilized to identify patterns, trends, and hidden risks in financial data, leading to more informed and efficient business decision making. 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, there are ethical considerations that need to be addressed, such as ensuring data privacy, implementing effective data security measures, and mitigating biases in machine learning algorithms used in financial analysis. Businesses should adopt a responsible approach to machine learning implementation, considering the potential risks and benefits. 3) The Java J2EE framework can provide a reliable running environment for machine learning applications, but further research is needed to evaluate the performance and scalability of machine learning models in this framework. Identifying potential optimizations for running machine learning applications at scale in the Java J2EE framework can lead to more efficient and effective implementation of machine learning in financial analysis and decision-making processes. Further research in this area can contribute to the development of robust and scalable machine learning applications for financial analysis in the business domain.

Areas for further study include: 1) Exploring different machine learning algorithms and techniques to further improve the accuracy and speed of financial analysis. 2) Conducting research on the impact of machine learning on financial decision making and business performance. 3) Investigating methods for addressing and mitigating biases in machine learning algorithms used in financial analysis. 4) Evaluating the effectiveness of different data security measures in protecting sensitive financial data in machine learning applications. 5) Studying the performance and scalability of machine learning models in the Java J2EE framework and identifying potential optimizations for running machine learning applications at scale.

Download

  Contact Author

Share

COinS