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Unstructured data - Augmenting Anti-Money Laundering Workflows with Supplementary Technologies

Augmenting Anti-Money Laundering Workflows with Supplementary Technologies

The banking sector is being seen as the first line of defense against money laundering, as regulators ask banks to gather customer information that will help surface, identify, and prevent financial crime. Even with increasing compliance-related costs, banks still face non-compliance issues. Emerging regulatory technologies can help facilitate compliance by helping AML analysts make informed decisions.

Anti-Money Laundering (AML) is a challenging regulation for banks – even more so for large, diverse financial institutions with a global footprint. What makes complying with AML regulations a challenge is because it involves complex data, documentation in multiple formats, and significant human intervention in the form of AML analysts. Regulations promulgated in the aftermath of the financial crisis, technological innovations, diverse identity regimes, and the need to correlate data from multiple sources, have created a complex environment of regulatory change for the banking sector. The banking sector has taken its compliance obligations seriously and has made significant investments in time, effort, and capital to meet compliance requirements.

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