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Leveraging machine learning to minimize fraudulent transactions: a strategic modeling approach
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| 2.57 MB | Adobe PDF |
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Orientador(es)
Resumo(s)
Fraud has increasingly gained prevalence as millions of transactions are done online. Various
stakeholders such as governments, organizations, and consumers have developed strategies to
detect fraud and other unusual behavior. Machine learning techniques have been leveraged
for fraud detection resulting in unique and sustainable solutions in financial transactions. In
the modern age, machine learning algorithms have been widely utilized as a data mining
technique for identifying issues with transactions. The current research aims to compare the
effectiveness of three distinct machine learning models including GaussianNB, XGBoost, and
Logistical Regression models by focusing on their precision, recall, and F1 score. Based on
the outcomes of the three machine learning models, XGBoost is considered to be the best
alternative for fraud detection at WeGoWin.
Descrição
Palavras-chave
Fraud detection Neural networks Machine learning Data mining