Senior ResearcherContact Ann-Charlotte
When companies transition to circular business models (especially functional sales models), risk assessments of business cases and payback times, as well as new types of collateral and residual value, have been identified as important challenges for financiers. We investigate how these challenges can be overcome with the help of AI and digitalisation, as well as how scaled-up circular models and data-driven value and risk assessment can change the roles within value chains & business ecosystems,
This project aims to reduce uncertainties regarding future value of products and thereby increase the willingness among financiers to be part of the development of new circular business models (CBMs).
Research shows that there is a strong relationship between CO2 reduction potential and circular resource effective systems for production and consumption. Manufacturing firms transitioning to circular business models are thus one important part of the solution of the climate crisis, and the financial sector is a key enabler to make the shift happen. Our own research as well as earlier literature have highlighted the need to reduce risk in CBMs, and the specific aspect of understanding future residual value of used products - as collateral and balance sheet items. We will collect open data from online marketplaces and use machine
learning technology to predict residual value of 3-4 product categories.
This will result in an AI model and application prototype. Together with our bank project partners we will explore how to best disseminate the use of the model, as well as how a scaled-up shift to CBMs would affect the business ecosystem of financiers (banks and insurance companies), and enabling technology firms.
This research is ground-breaking and has the potential to both substantially add knowledge to the intersection of circular economy and digitalisation, and to set an industry standard for how to use AI to reduce risk in financing of CBMs.
Region Stockholm, Västra Götaland Region