Fraud Detection in Mobile Money Transactions Using Machine Learning

Date
2019-01-01
Authors
Kang, Haimeng
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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.

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Keywords
fraud detection, classification, random forest, gradient boosting, confusion matrix, F1-score
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