Modeling Corporate Credit Climate Risk
Amid discussion by regulators and bankers of climate risk metrics, researchers propose a bottom-up approach for corporate credit climate stress.
Browsing: Model risk
ABA Urges Additional Clarity on Model Risk Management for BSA/AML Compliance
The American Bankers Association filed comments today urging the federal banking agencies and the Financial Crimes Enforcement Network to provide additional guidance on how the 2011 “Supervisory Guidance on Model Risk Management,” or MRMG, works in conjunction with Bank Secrecy Act/anti-money laundering and Office of Foreign Assets Control compliance.
Banking Agencies Provide Guidance on Model Risk Management
The federal banking agencies and the Financial Crimes Enforcement Network today issued a statement on the use of the “Supervisory Guidance on Model Risk Management” to comply with Bank Secrecy Act/anti-money laundering rules.
COVID-19: A Positive Disruptor to Expected Credit Loss Models
The current murky picture delivered by traditional data has forced an increased focus on new methods that better capture credit risk.
Regulators Discuss Trends in BSA/AML Compliance Risks
When conducting recent examinations for Bank Secrecy Act/anti-money laundering compliance, regulatory officials flagged deficiencies in risk assessments, a need for more maturity in compliance systems and processes and data integrity issues, particularly as a result of mergers, as areas of concern during a panel discussion at the American Bankers Association/American Bar Association Financial Crimes Enforcement Conference in Washington, D.C., today.
GAO: Fed’s Large Bank Guidance Constitutes a Rule
The Government Accountability Office this week found that the two of the Federal Reserve’s guidance documents on large bank supervision—which were issued in 2012 and 2014 by the Fed’s Large Institution Supervision Coordinating Committee—are considered rules for the purposes of the Congressional Review Act.
Who Owns Model Risk in an AI World?
Regulatory guidance wants models that can be challenged. But when artificial intelligence turns a model into a black box, how can bankers manage model risk?