This training program will detail why SR Letter 11-7 has become the gold standard for model risk management, with its principle being adopted not only by banks but also virtually all U.S. financial institutions.

Why Should You Attend:

Financial institutions rely on models and quantitative analysis to perform key activities such as investment valuations, risk management, and liquidity and capital management. A sound management of the risks inherent in the use of mathematical models is imperative to reduce the risk of reliance on flawed or misapplied models. SR Letter 11-7, issued jointly by the Federal Reserve and OCC, provides comprehensive guidance for banks on effective model risk management.

This webinar will offer attendees an understanding of the background behind SR Letter 11-7. It will further discuss the purpose and scope behind the supervisory guidance on model risk management.

Areas Covered in the Webinar:

• Background and Model Risk
• Purpose and Scope of the Guidance
• Model Risk Management
• Model Development, Implementation, and Use
• Model Validation
• Governance, Policies, and Controls
• Roles and Responsibilities
• Spreadsheet Controls
• Effective Challenge

Who Will Benefit:

• Asset managers
• Broker/dealers
• Supervisors
• Financial officers and controllers
• Risk officers
• Operational risk managers
• Board members
• General (legal) counsel
• Internal and external auditors
• Information technology managers
• Regulators who supervise or examine banks or financial institutions
• CFOs
• COOs
• CROs
• CIOs
• CPAs
• CBAs
• CFEs

Topic Background:

In April 2011, federal bank regulators published SR Letter 11-7, entitled “Supervisory Guidance on Model Risk Management.” SR Letter 11-7 outlines the regulatory expectations with respect to the risk management processes and internal controls that banks must implement to ensure that models are developed, implemented and used in a well-controlled manner. The guidance also describes the roles of the various areas involved in the management and oversight of models, including the need for independent model validation and effective challenge.

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