Fraud Detection in Mobile Money Transactions Using Machine Learning

dc.contributor.author Kang, Haimeng
dc.contributor.department Information Systems and Business Analytics
dc.contributor.majorProfessor Anthony Townsend
dc.date 2020-01-07T20:11:02.000
dc.date.accessioned 2020-06-30T01:34:46Z
dc.date.available 2020-06-30T01:34:46Z
dc.date.copyright Tue Jan 01 00:00:00 UTC 2019
dc.date.issued 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/lib.dr.iastate.edu/creativecomponents/402/
dc.identifier.articleid 1449
dc.identifier.contextkey 15899587
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath creativecomponents/402
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/16959
dc.source.bitstream archive/lib.dr.iastate.edu/creativecomponents/402/Creative_Component_Haimeng.pdf|||Sat Jan 15 00:08:03 UTC 2022
dc.subject.disciplines Business Analytics
dc.subject.keywords fraud detection
dc.subject.keywords classification
dc.subject.keywords random forest
dc.subject.keywords gradient boosting
dc.subject.keywords confusion matrix
dc.subject.keywords F1-score
dc.title Fraud Detection in Mobile Money Transactions Using Machine Learning
dc.type article
dc.type.genre creativecomponent
dspace.entity.type Publication
relation.isOrgUnitOfPublication 0099bcd5-3121-4f25-813d-0ec68d96243f
thesis.degree.discipline Information Systems
thesis.degree.level creativecomponent
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