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Data pooling and collaborative analytics, refers to a process where (digital) data from different sources are analysed (including by multiple parties). These pools may be organised in a centralised (data pooling) or a distributed way (collaborative analytics).

This paper addresses data pooling and collaborative analytics between financial institutions (FIs), including within and outside international financial groups. Data pooling and collaborative analytics carry benefits but also some significant risks.

It may enable the use of analytical tools that have the potential to strengthen the shared understanding, assessment and mitigation of money laundering (ML) and terrorist financing (TF) risks, resulting in a more dynamic, effective and efficient identification of these activities.

It can reduce the number of false positives, enabling more effective compliance by the private sector in a timelier and less burdensome manner. It can also help prevent the exploitation of information gaps that enable regulatory arbitrage by criminals, who may attempt to engage with multiple domestic and international FIs, each having a limited and partial view of transactions.

However, it may also infringe on the protection of individual and fundamental rights. Therefore, it is imperative that any exchange of information respects national and international data protection and privacy (DPP) legal frameworks.




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