1. Legitimate Purpose: FIs’ programmes to combat financial crimes are anchored in regulatory requirements, and a commitment to help safeguard the integrity of the financial system, while reaching fair and effective outcomes. Responsible use of advanced technologies such as AI/ML, and the volume and type of data necessary for them to be effective, requires FIs to understand and guard against the potential for misuse or misrepresentation of data, and any bias that may affect the results of the AI/ML application. A key consideration for FIs implementing AI/ML is how to integrate an assessment of ethical and operational risks into their risk governance approach.
2.Proportionate Use: FIs should ensure that, in their development and use of AI/ML solutions for financial crimes compliance, they are balancing the benefits of use with appropriate management of the risks that may arise from these technologies. Additionally, the severity of potential financial crimes risk should be appropriately assessed against any AI/ML solutions’ margin for error. FIs should implement a programme that validates the use and configuration of AI/ML regularly, which will help ensure that the use of data is proportionate to the legitimate, and intended, financial crimes compliance purpose.
3.Design and Technical Expertise: FIs should carefully control the technology they rely on and understand the implications, limitations, and consequences of its use to avoid ineffective financial crime risk management. Teams involved in the creation, monitoring, and control of AI/ML should be composed of staff with the appropriate skills and diverse experiences needed to identify bias in the results. Design of AI/ML systems should be driven by a clear definition of the intended outcomes and ensure that results can be adequately explained or proven given the data inputs.
4. Accountability and Oversight: FIs are responsible for their use of AI/ML, including for decisions that rely on AI/ML analysis, regardless of whether the AI/ML systems are developed in-house or sourced externally. FIs should train staff on the appropriate use of these technologies and consider oversight of their design and technical teams by persons with specific responsibility for the ethical use of data in AI/ML, which may be through existing risk or data management frameworks.
5.Openness and Transparency: FIs should be open and transparent about their use of AI/ML, consistent with legal and regulatory requirements. However, care should be taken to ensure that this transparency does not facilitate evasion of the industry’s financial crime capabilities, or breach reporting confidentiality requirements and/or other data protection obligations inadvertently.
2.Proportionate Use: FIs should ensure that, in their development and use of AI/ML solutions for financial crimes compliance, they are balancing the benefits of use with appropriate management of the risks that may arise from these technologies. Additionally, the severity of potential financial crimes risk should be appropriately assessed against any AI/ML solutions’ margin for error. FIs should implement a programme that validates the use and configuration of AI/ML regularly, which will help ensure that the use of data is proportionate to the legitimate, and intended, financial crimes compliance purpose.
3.Design and Technical Expertise: FIs should carefully control the technology they rely on and understand the implications, limitations, and consequences of its use to avoid ineffective financial crime risk management. Teams involved in the creation, monitoring, and control of AI/ML should be composed of staff with the appropriate skills and diverse experiences needed to identify bias in the results. Design of AI/ML systems should be driven by a clear definition of the intended outcomes and ensure that results can be adequately explained or proven given the data inputs.
4. Accountability and Oversight: FIs are responsible for their use of AI/ML, including for decisions that rely on AI/ML analysis, regardless of whether the AI/ML systems are developed in-house or sourced externally. FIs should train staff on the appropriate use of these technologies and consider oversight of their design and technical teams by persons with specific responsibility for the ethical use of data in AI/ML, which may be through existing risk or data management frameworks.
5.Openness and Transparency: FIs should be open and transparent about their use of AI/ML, consistent with legal and regulatory requirements. However, care should be taken to ensure that this transparency does not facilitate evasion of the industry’s financial crime capabilities, or breach reporting confidentiality requirements and/or other data protection obligations inadvertently.
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