<ul><li>Financial institutions are required to monitor vast amounts of transactions for money laundering.</li><li>Traditional machine learning models have limitations in adapting to dynamic environments for AML detection.</li><li>Continual graph learning approaches can enhance AML practices by incorporating new information while retaining prior knowledge.</li><li>Experimental evaluations show that continual learning improves model adaptability and robustness in detecting money laundering.</li></ul>

Advances in Continual Graph Learning for Anti-Money Laundering Systems: A Comprehensive Review

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