Term of Award
Fall 2023
Degree Name
Master of Science, Information Technology
Document Type and Release Option
Thesis (open access)
Copyright Statement / License for Reuse
This work is licensed under a Creative Commons Attribution 4.0 License.
Department
Department of Information Technology
Committee Chair
Hayden Wimmer
Committee Member 1
Jongyeop Kim
Committee Member 2
Meenalosini V. Cruz
Abstract
Data science plays a crucial role in enabling organizations to optimize data-driven opportunities within financial risk management. It involves identifying, assessing, and mitigating risks, ultimately safeguarding investments, reducing uncertainty, ensuring regulatory compliance, enhancing decision-making, and fostering long-term sustainability. This thesis explores three facets of Data Science projects: enhancing customer understanding, fraud prevention, and predictive analysis, with the goal of improving existing tools and enabling more informed decision-making. The first project examined leveraged big data technologies, such as Hadoop and Spark, to enhance financial risk management by accurately predicting loan defaulters and their repayment likelihood. In the second project, we investigated risk assessment and fraud prevention within the financial sector, where Natural Language Processing and machine learning techniques were applied to classify emails into categories like spam, ham, and phishing. After training various models, their performance was rigorously evaluated. In the third project, we explored the utilization of Azure machine learning to identify loan defaulters, emphasizing the comparison of different machine learning algorithms for predictive analysis. The results aimed to determine the best-performing model by evaluating various performance metrics for the dataset. This study is important because it offers a strategy for enhancing risk management, preventing fraud, and encouraging innovation in the financial industry, ultimately resulting in better financial outcomes and enhanced customer protection.
OCLC Number
1417404735
Catalog Permalink
https://galileo-georgiasouthern.primo.exlibrisgroup.com/permalink/01GALI_GASOUTH/1r4bu70/alma9916562047902950
Recommended Citation
Ijogun, Oluwaseyi A., "Application of Big Data Technology, Text Classification, and Azure Machine Learning for Financial Risk Management Using Data Science Methodology" (2023). Electronic Theses and Dissertations. 2654.
https://digitalcommons.georgiasouthern.edu/etd/2654
Research Data and Supplementary Material
No
Included in
Business Analytics Commons, Business Intelligence Commons, Computer Sciences Commons, Data Science Commons, Risk Analysis Commons, Technology and Innovation Commons
