The potential of artificial intelligence and machine learning (ML) to deliver value to financial institutions has created something of a gold rush in adopting this methodology for applications. More specifically, organisations are turning to ML models as an alternative to traditional models to gain faster, more accurate and insightful predictions and classifications in their risk management and financial management business decisions.
Because they are more complex and less transparent than traditional models, ML models pose a unique set of challenges to model risk management and model validation. Join our webinar to learn about the challenges and benefits of incorporating machine learning models into your risk management program.
· Discuss practical applications of machine learning models at financial services institutions
· Define the unique set of challenges associated with validating machine learning models
· Develop customized methods for validating ML models within your institution based on industry best practices