The Basel Committee on Banking Supervision (BCBS) published a newsletter that outlines the potential focus areas for the supervision of artificial intelligence and machine learning models.
During the internal discussions, BCBS has identified several areas for continued analysis by supervisors. Since, artificial intelligence-machine learning models are more complex and difficult to manage, banks are seeking to maintain a level of transparency in model design, operation, and interpretability of model outcomes commensurate with the risk of the banking activity being supported. In case of artificial intelligence-machine learning model being outsourced, banks still have to maintain the responsibility and accountability for appropriate due diligence and oversight. The artificial intelligence-machine learning model deployment often involves use of large data sets, interconnectivity with third parties, and the use of cloud technologies; it creates greater data governance challenges for banks to ensure data quality, relevance, security, and confidentiality, including cyber risk. Furthermore, artificial intelligence-machine learning models (as with traditional models) can reflect biases and inaccuracies in the data they are trained on and potentially result in unethical outcomes if not properly managed.
Given the challenges associated with an artificial intelligence-machine learning model, both supervisors and banks are assessing existing risk management and governance practices to determine whether roles and responsibilities for identifying and managing risks remain sufficient. BCBS is working to develop insights on the supervisory implications of the use of artificial intelligence-machine learning models and intends to focus on:
- the extent and degree to which the outcomes of models can be understood and explained
- artificial intelligence-machine learning model governance structures, including responsibilities and accountability for decisions driven by artificial intelligence-machine learning models
- the potential implications of broader usage of artificial intelligence-machine learning models for the resilience of individual banks and, more broadly, for financial stability
Related Link: BCBS Newsletter
Keywords: International, Banking, Artificial Intelligence, Machine Learning, Fintech, Model Explainability, Cloud Service Providers, Credit Risk, Regtech, Predictive Analytics, BCBS
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