Fraud Detection in Mobile Money Transactions Using Machine Learning

dc.contributor.advisor Anthony Townsend Kang, Haimeng
dc.contributor.department Information Systems and Business Analytics
dc.contributor.other Information Systems and Business Analytics 2020-01-07T20:11:02.000 2020-06-30T01:34:46Z 2020-06-30T01:34:46Z 2019-01-01
dc.description.abstract <p>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.</p>
dc.format.mimetype PDF
dc.identifier archive/
dc.identifier.articleid 1449
dc.identifier.contextkey 15899587
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath creativecomponents/402
dc.source.bitstream archive/|||Sat Jan 15 00:08:03 UTC 2022
dc.subject fraud detection
dc.subject classification
dc.subject random forest
dc.subject gradient boosting
dc.subject confusion matrix
dc.subject F1-score
dc.title Fraud Detection in Mobile Money Transactions Using Machine Learning
dc.type creativecomponent
dspace.entity.type Publication
relation.isOrgUnitOfPublication 0099bcd5-3121-4f25-813d-0ec68d96243f
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