BCBS to Intensify Supervisory Focus on Machine Learning Models
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
Previous Article
SNB Publishes Release 5.12 of Form on Counterparty Solvency RiskRelated Articles
CFPB Finalizes Rule on Small Business Lending Data Collection
The Consumer Financial Protection Bureau (CFPB) published a final rule that sets out data collection requirements on small business lending, under section 1071 of the Dodd-Frank Act.
BCBS to Consult on Pillar 3 Climate Risk Disclosures by End of 2023
The Bank for International Settlements (BIS) published a summary of the recent Basel Committee (BCBS) meetings.
FINMA Approves Merger of Credit Suisse and UBS
The Swiss Financial Market Supervisory Authority (FINMA) has approved the takeover of Credit Suisse by UBS.
BOE Sets Out Its Thinking on Regulatory Capital and Climate Risks
The Bank of England (BOE) published a working paper that aims to understand the climate-related disclosures of UK financial institutions.
US Congress Report Examines Data Privacy and Cybersecurity Regulations
The U.S. Congressional Research Service published a report on banking, data privacy, and cybersecurity regulation.
OSFI Finalizes on Climate Risk Guideline, Issues Other Updates
The Office of the Superintendent of Financial Institutions (OSFI) is seeking comments, until May 31, 2023, on the draft guideline on culture and behavior risk, with final guideline expected by the end of 2023.
EU to Conduct One-Off Scenario Analysis to Assess Transition Risk
The European authorities recently made multiple announcements that impact the banking sector.
APRA Assesses Macro-Prudential Policy Settings, Issues Other Updates
The Australian Prudential Regulation Authority (APRA) published an information paper that assesses its macro-prudential policy settings aimed at promoting stability at a systemic level.
BIS Paper Examines Impact of Greenhouse Gas Emissions on Lending
BIS issued a paper that investigates the effect of the greenhouse gas, or GHG, emissions of firms on bank loans using bank–firm matched data of Japanese listed firms from 2006 to 2018.
HMT Mulls Alignment of Ring-Fencing and Resolution Regimes for Banks
The HM Treasury (HMT) is seeking evidence, until May 07, 2023, on practicalities of aligning the ring-fencing and the banking resolution regimes for banks.