Application of Big Data Technology, Text Classification, and Azure Machine Learning for Financial Risk Management Using Data Science Methodology

Aug 4, 2026

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Data science is essential for optimizing data-driven opportunities within financial risk management by identifying, assessing, and mitigating risks. The study aims to reduce uncertainty, strengthen regulatory compliance, improve decision-making, and support long-term sustainability. It examines three data science project components: customer understanding and loan default prediction using big data tools such as Hadoop and Spark; fraud prevention and risk assessment using NLP to classify emails into spam, ham, and phishing with model evaluation; and Azure Machine Learning for loan default identification, comparing algorithms and performance metrics to select the best predictive model.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages164

Reading Time6 h 53 min

AccessFree to download

Acknowledgments

List of Figures

List of Tables

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Chapter 1

Georgia Southern University Digital Commons@Georgia Southern | Electronic Theses and Dissertations | Jack N. Averitt College of Graduate Studies | | --- | --- | | Fall 2023
Application of Big Data Technology, Text Classification, and Azure Machine Learning for Financial Risk
Management Using Data Science Methodology Oluwaseyi A. Ijogun
Follow this and additional works at: [https://digitalcommons.georgiasouthern.edu/etd](https://digitalcommons.georgiasouthern.edu/etd)
Part of the Business Analytics Commons, Business Intelligence Commons, Computer Sciences Commons, Data Science Commons, Risk Analysis Commons, and the Technology and Innovation Commons | | 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](https://digitalcommons.georgiasouthern.edu/etd/2654) This thesis (open access) is brought to you for free and open access by the Jack N. Averitt College of Graduate Studies at Digital Commons@Georgia Southern. It has been accepted for inclusion in Electronic Theses and Dissertations by an authorized administrator of Digital Commons@Georgia Southern. For more information, please [contact digitalcommons@georgiasouthern.edu](contact digitalcommons@georgiasouthern.edu). APPLICATION OF BIG DATA TECHNOLOGY, TEXT CLASSIFICATION, AND AZURE MACHINE LEARNING FOR FINANCIAL RISK MANAGEMENT USING DATA SCIENCE METHODOLOGY by OLUWASEYI ADEMOLA IJOGUN (Under the Direction of Hayden Wimmer) 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 decisionmaking, 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. INDEX WORDS: Big data technology, Text analysis, Data science, Cyber security, Machine learning, NLP, Azure machine learning, Financial risk management, Hadoop, Spark. APPLICATION OF BIG DATA TECHNOLOGY, TEXT CLASSIFICATION, AND AZURE MACHINE LEARNING FOR FINANCIAL RISK MANAGEMENT USING DATA SCIENCE METHODOLOGY by OLUWASEYI ADEMOLA IJOGUN M.S., Georgia Southern University, 2023 A Thesis Submitted to the Graduate Faculty of Georgia Southern University in Partial Fulfillment of the Requirements for the Degree MASTER OF SCIENCE STATESBORO, GEORGIA © 2023 OLUWASEYI IJOGUN All Rights Reserved APPLICATION OF BIG DATA TECHNOLOGY, TEXT CLAS

FAQs

What role does data science play in financial risk management in this thesis?

It enables organizations to identify, assess, and mitigate financial risks while reducing uncertainty, improving regulatory compliance, and supporting better decisions.

How is big data technology used in the first project?

Hadoop and Spark are used to enhance financial risk management by predicting loan defaulters and estimating repayment likelihood.

What NLP task is performed in the fraud prevention project?

Natural Language Processing and machine learning classify emails into categories such as spam, ham, and phishing, followed by rigorous model performance evaluation.

Aug 4, 2026

Upload by Maeve