Description: In order for an organization to benefit from artificial intelligence/machine learning (AI/ML) models, it requires the involvement of different business areas. During this webcast, professionals from data analytics, audit and risk areas will address challenges and provide solutions for working together to develop, implement, use and risk manage AI/ML tools. Throughout the discussion, the presenters will discuss: • Defining and exploring the challenges and objectives between data scientists, domain experts and control functions • Risk management – model risks and other risks • End-to-end model validation • Model performance vs. interpretability/understandability trade-offs • What to audit – auditor’s challenge and solutions

Description: One important lesson learnt from the recent financial crisis was that overreliance on financial models could lead to unforeseen and harmful consequences. Model risk management (MRM) is becoming a core and mandatory component in enterprise risk management for many financial institutions, especially Fed/OCC CCAR-compliant firms. In recent years, insurance companies are also being required to have model risk management programs by Solvency II, ORSA and other supervisory guidance. MRM is not just model validation. It consists of governance and policies regarding a model's life cycle including data, model development, model test and implementation, model validation, model monitoring and documentation. It also provides guidelines and standards for risk assessment and model inventory management. The presenters will discuss current and best practice MRM, approach for model validation and auditing, as well as topics for technical issues such as types of model risks. It will also discuss the differences between Fed/OCC standards and Solvency II standards. At the conclusion of the session, attendees will be able to: Describe best practice in model risk management; Explain requirements and standards; and Develop model validation procedure.