Deep Learning models are currently being introduced into business processes to support decision-making in insurance companies. At the same time model risk is recognized as an increasingly relevant field within the management of operational rlsk that tries to mitigate the risk of poor business decisions because of flawed models or inappropriate model use. ln this paper we try to determlne how Deep Learning models are different from established actuarial models currently in use in insurance companies and how these differences might necessitate changes in the model risk management framework. We analyse operatlonal risk in the development and implementation of Deep Learning models using examples from pricing and mortality forecasting to illustrate specific model risks and controls to mitigate those risks. We discuss changes ln model governance and the role that model risk managers could play in providing assurance on the appropria te use of Deep Learnlng models.