Recommender systems suggest new items to users based on their characteristics and previous behavior. Despite the support that they can bring to financial decision making, their application to banking data is an underexplored field. We build recommenders for private and retail banking use cases. The vision is to enhance the quality of financial advice and make it accessible to a wider client base. In the retail banking use case, where clients typically hold few products, we obtained the most promising results with Demographic Recommender Systems using client features. In the private banking use case, where clients typically invest in multiple securities, we found the Collaborative Filtering approach, based on user-product interactions, to be particularly suitable.