Deconstructing Money Laundering Risk
De-risking, the Risk-based Approach and Risk Communication
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This book examines the interpretation of the risk-based approach (RBA) and its application across the banking industry. It explores the ways conflicting risk interpretation and deconstruction of money laundering risk have unintended consequences across the banking industry. Furthermore, it offers a theoretical framework that can be adopted and implemented by risk practitioners to address money laundering (ML) risks.The interpretation and application of the RBA influences the way money laundering risk is perceived, presented, and managed, often resulting in misalignment among stakeholders. More...
This book examines the interpretation of the risk-based approach (RBA) and its application across the banking industry. It explores the ways conflicting risk interpretation and deconstruction of money laundering risk have unintended consequences across the banking industry. Furthermore, it offers a theoretical framework that can be adopted and implemented by risk practitioners to address money laundering (ML) risks.
The interpretation and application of the RBA influences the way money laundering risk is perceived, presented, and managed, often resulting in misalignment among stakeholders. Moreover, AML practitioners interpret money laundering (ML) risk as an entity that can be contained, largely in ignorance of the fact that ML risk is self-referential.
The book therefore addresses complex inter-system feedback phenomena that lead to de-risking and re-risking, and offers a new ML risk communication framework on this basis. It will be of value to researchers and also to stakeholders within financial institutions, financial intelligence units and regulators in the fight against money laundering.
The interpretation and application of the RBA influences the way money laundering risk is perceived, presented, and managed, often resulting in misalignment among stakeholders. Moreover, AML practitioners interpret money laundering (ML) risk as an entity that can be contained, largely in ignorance of the fact that ML risk is self-referential.
The book therefore addresses complex inter-system feedback phenomena that lead to de-risking and re-risking, and offers a new ML risk communication framework on this basis. It will be of value to researchers and also to stakeholders within financial institutions, financial intelligence units and regulators in the fight against money laundering.
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