BCBS published a high-level summary of an industry workshop on the governance and oversight of artificial intelligence and machine learning in financial services. The Supervision and Implementation Group (SIG) of BCBS held this workshop on October 03, 2019. With regard to unique risks posted by artificial intelligence and machine learning, participants discussed how best to deal with ethics and bias, given that these issues have broader scope than that which is within the remit of prudential authorities. It was discussed that, while existing governance frameworks and policies are generally considered to apply to artificial intelligence and machine learning models, there may need to be further refinements.
The workshop, which was chaired by Mr. Arthur Yuen, Deputy Chief Executive of HKMA. primarily covered the use of artificial intelligence and machine learning in the banking sector. The SIG met with senior representatives from internationally active banks and associated industry associations, consulting and technology firms and other public authorities to discuss how banks are leveraging artificial intelligence and machine learning technologies to better manage risk or offer new and innovative financial services; also discussed were the associated challenges related to risk governance, data management, and engagement of third-party service providers. The SIG also discussed how bank supervisors and other official sector bodies are approaching the risks arising from the adoption of financial technology.
A key observation is that, in the last 12 to 18 months, the use of artificial intelligence and machine learning in risk assessments and analytics has increased across a range of functions, including credit analytics, fraud and anti-money laundering detection, and regulatory compliance. Nevertheless, ongoing challenges discussed at the workshop include "black box" risk and explainability of machine learning models, including bias, ethics, transparency, and data privacy requirement, and data management. There was a recognition that, while artificial intelligence and machine learning models have characteristics that are common to other financial and regulatory models used by banks, artificial intelligence and machine learning models may amplify traditional model risks. One such subset of risks highlighted by participants are those related to data, including the quantity and quality of vast data sets, data access, and engagement with third parties that use or store data.
Additionally, discussions focused on the role of model risk management functions in overseeing artificial intelligence and machine learning validation and the challenges with having the right skill sets and expertise to address risks specific to artificial intelligence and machine learning. Supervisors also provided commentary on how they are considering the supervision and oversight of artificial intelligence and machine learning models. The SIG agreed that ongoing supervisory engagement with stakeholders and information sharing were beneficial and future outreach will be considered.
Related Link: Press Release
Keywords: International, Banking, Artificial Intelligence, Machine Learning, Workshop, Regtech, Fintech, Governance, Third-Party Service Providers, Big Data, BCBS
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