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

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


Department of Information Technology

Committee Chair

Hayden Wimmer

Committee Member 1

Jongyeop Kim

Committee Member 2

Meenalosini V. Cruz


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.

Research Data and Supplementary Material