Degree Type

Creative Component

Semester of Graduation

Fall 2019

Department

Information Systems and Business Analytics

First Major Professor

Anthony Townsend

Degree(s)

Master of Science (MS)

Major(s)

Information Systems

Abstract

This study explores an effective data mining system for fraud detection in mobile financial transactions. Attempting two broad-used supervised machine learning models, random forest and gradient boosting, the study aims to test and compare their applicability in the detection of fraudulent records. Both classification models were developed using a synthetic dataset of mobile money transactions, which was generated based on a sample of real transactions extracted from an international mobile money service company.

Copyright Owner

Kang, Haimeng

Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License

File Format

PDF

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