Big Data Analysis for Anti-Money Laundering: A Case of Open Source Greenplum Application

Hu, Chaochen; Li, Ran; Li, Chao; Miao, Hengshuo; Yang, Zongyou; Zhang, Tengda

doi:10.1007/978-3-031-20309-1_56

Web Information Systems and Applications

2022

DOI: 10.1007/978-3-031-20309-1_56

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Big Data Analysis for Anti-Money Laundering: A Case of Open Source Greenplum Application

Chaochen Hu

Ran Li

Chao Li

et al.

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“…In essence, riskbased AML principles in the financial sector hinge on two core requirements: the AML systems alone proved inadequate. The advent of big data analytics marked a new era in AML systems(Han et al, 2020;Hu et al, 2022), harnessing the power of sophisticated algorithms to sift through vast datasets, seeking out patterns and anomalies that could indicate suspicious JEMA…”

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A systematic review of anti-money laundering systems literature: Exploring the efficacy of machine learning and deep learning integration

Nadia Husnaningtyas,

Ghalizha Failazufah Hanin,

Totok Dewayanto

et al. 2023

JEMA

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Money laundering is a complex issue with global impact, leading to the increased adoption of artificial intelligence (AI) to bolster anti-money laundering (AML) measures. AI, with machine learning and deep learning as key drivers, has become an essential enhancement for AML strategies. Recognizing this emerging trend, this study embarks on a systematic literature review, aiming to provide novel insights into the implementation, effectiveness, and challenges of these sophisticated computational techniques within AML frameworks. A critical analysis of 26 selected studies published from 2018 to 2023 highlights the essential role of machine learning and deep learning in identifying money laundering schemes. Notably, the decision tree algorithm stands out as the most commonly utilized technique. The combined use of both learning models has proven to significantly increase the effectiveness of AML systems in detecting suspicious financial patterns. However, the optimization of these advanced methods is still constrained by issues related to data complexity, quality, and access.

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confidence: 99%

A systematic review of anti-money laundering systems literature: Exploring the efficacy of machine learning and deep learning integration

Nadia Husnaningtyas,

Ghalizha Failazufah Hanin,

Totok Dewayanto

et al. 2023

JEMA

View full text Add to dashboard Cite

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Big Data Analysis for Anti-Money Laundering: A Case of Open Source Greenplum Application

Cited by 1 publication

References 2 publications

A systematic review of anti-money laundering systems literature: Exploring the efficacy of machine learning and deep learning integration

A systematic review of anti-money laundering systems literature: Exploring the efficacy of machine learning and deep learning integration

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