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Invite colleaguesMinimise model risk management oversight for cyber security solutions
Abstract
Adoption of artificial intelligence (AI) and machine learning (ML)-powered cyber security tools and models by financial institutions has received considerable attention in the model risk management community. In parallel, developing trustworthy AI that is more explainable, fair, robust, private and transparent has also received considerable research and regulatory attention. Appropriate governing of cyber security models is inevitable. The prevailing thought at present is to have the model risk management function to oversee the development, implementation and use of such cyber security tools and models. This study first demonstrates two primary challenges of executing this oversight and then offers a few practical suggestions to ensure a reasonable application.
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Author's Biography
Liming Brotcke PhD leads the Consumer and Commercial Banking Business Line Risk Data Science function at Ally Financial after heading the Model Validation Group for three and a half years. Prior to joining Ally, she worked at the Federal Reserve Bank of Chicago focusing on Large Institution Supervision Coordinating Committee (LISCC) bank supervision and regulation pertaining to credit risk, model risk, current expected credit loss (CECL) implementation, and artificial intelligence (AI)/machine learning (ML) practice. Liming has extensive industry experience in financial model development, implementation, performance monitoring and validation from working at Ally, Citi Group and Discover Financial Services.