Answer yes if you have ensured, as far as you are able, that your people (employees, managed contract resources or anyone else acting on behalf of your organisation) have reviewed and evaluated the AI model output before use. These processes should help mitigate the risks arising from inaccuracies or ‘hallucinations’ (plausible created statements) within AI outputs which, if applied without human review, can impact integrity and mislead decision-making.
This control is a legal and regulatory requirement for organisations that fall in scope for AML regulation. This is typically:
It is important that you know your organisations legal requirements for ensuring compliance with AML regulation, and we recommend seeking counsel if you are not sure about your applicability.
The term “anti-money laundering” refers to all policies and pieces of legislation that force institutions to proactively monitor their clients in order to prevent money laundering and corruption. These laws also require both that institutions report any financial crimes they find and that they do everything possible to stop them.
Consider the operational workflows in which AI processing is applied. Build opportunities for review into those workflows where practicable, and ensure service users are aware of the limitations of AI processing capability, examples of processing flaws, and consequential risks of using that information without review.
When working with generated metrics, consider presentation of the outputs in graphical format to more easily identify anomalous results and outliers compared with expected trends. Where processing is used to identify outliers to trigger remedial processes (e.g. in ICT process control or dynamic network/computing capacity control), consider if additional data processing and controls can be applied to moderate or verify AI results prior to a system taking action.
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