Towards Designing Robo-Advisory to Promote Consensus Efficient Group Decision-Making in New Types of Economic Scenarios

Robo-advisors are a new type of FinTech increasingly used by millennials in place of traditional financial advice. Building on artificial intelligence, robo-advisors provide personalized asset and wealth management services. Their application and study have hitherto focused exclusively on individual advisory regarding asset management. We observe a pressing need to investigate robo- advisors’ application for complex artificial intelligence based recommendation tasks both, in context of group decision-making and in contexts beyond asset management, due to robo-advisors’ potential as a lever for integrating artificial intelligence in the entire decision-making process. Thus, we present a action design research in progress aimed at designing such a robo-advisor. More specifically, this study investigates whether and how robo-advisory promotes consensus-efficient group decision-making in new types of economic scenarios (after-sales). Based on a comprehensive problem formulation, we aim towards deriving a set of meta-requirements and design principles that are embodied in a preliminary prototypical instantiation of a robo-advisor

Harnessing the Business Potential of Self-Service Machine Learning for Forecasting Warranty Costs – Insights from a Case Study in the Automotive Sector

New machine learning (ML) methods, such as AutoML, facilitate the modeling process and use of predictive models, promoting the democratization of ML. They open up the potential for domain experts to conduct Self-Service ML for the creation and operationalization of predictive models, to improve planning processes and forecasting accuracy. However, the departmental use of Self-Service ML technologies and systems remain under-investigated. For our research, we conducted a case study at an automobile OEM and focused on applications in the field of the operational forecasting and planning of warranty and goodwill costs. Following a case-study approach, our findings suggest that Self-Service ML can be realized but needs a thorough consideration of auditability, interpretability, and support for data provision and model operationalization. We condensed our findings to design requirements and decisions that are supposed to promote practical Self-Service ML implementations and provide a starting point for further research and designing such systems