2023
DOI: 10.21203/rs.3.rs-2530874/v1
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Predicting Suspicious Money Laundering Transactions using Machine Learning Algorithms

Abstract: This study employs machine learning techniques to identify key drivers of suspicious activity reporting. The data for this study comes from all suspicious activities reported to the California government in 2018. In total, there were 45,000 records of data that represent various features. The paper uses linear regression along with Lasso, Ridge, and Elastic Net to perform feature regularization and address overfitting with the data. Other probabilistic and non-linear algorithms, namely, support vector machines… Show more

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Cited by 2 publications
(1 citation statement)
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“…Therefore, it is important to use human expertise in combination with machine learning to ensure that potential cases of money laundering are thoroughly investigated and verified. Additionally, there are legal and ethical concerns that must be addressed when using machine learning in the fight against money laundering, such as data privacy and bias in algorithmic decision-making (Lokanan & Maddhesia, 2023). This research proposes that the time has come for Pakistani financial institutions to use machine learning models.…”
Section: Machine Learningmentioning
confidence: 99%
“…Therefore, it is important to use human expertise in combination with machine learning to ensure that potential cases of money laundering are thoroughly investigated and verified. Additionally, there are legal and ethical concerns that must be addressed when using machine learning in the fight against money laundering, such as data privacy and bias in algorithmic decision-making (Lokanan & Maddhesia, 2023). This research proposes that the time has come for Pakistani financial institutions to use machine learning models.…”
Section: Machine Learningmentioning
confidence: 99%