Koch, ChristianLinnik, BenjaminPelzel, FrankSultanow,EldarWelter, SebastianCox, Sean2021-12-142021-12-142021978-3-88579-708-1https://dl.gi.de/handle/20.500.12116/37610Chatbots have the potential to significantly increase the efficiency of banks and public institutions. Both sectors, however, are subject to special regulations and restrictions in areas such as information security and data protection. The policies of these organizations therefore, in some cases, reject the use of cloud and proprietary products because in their view they lack transparency. As a result, the implementation of chatbots in banks and public institutions often focuses on open-source and on-premises solutions; however, there are hardly any scientific guidelines on how to implement these systems. Our paper aims to close this research gap. The article proposes a reference architecture for chatbots in banks and public institutions that are a.) based on open-source software and b.) are hosted on-premises. The framework is validated by case studies at TeamBank AG and the German Federal Employment Agency. Even if our architecture is designed for these specific industries, it may also add value in other sectors – as chatbots are expected to become increasingly important for the practical application of artificial intelligence in enterprises.enChatbotsMachine Learning ArchitectureEnterprise ArchitectureReference ArchitectureCapability MapBankingPublic Sector10.18420/informatik2021-1061617-5468