Making Sweden´s consumer credit market sustainable
Reference number | |
Coordinator | Kungliga Tekniska Högskolan - Sustainable Finance Lab |
Funding from Vinnova | SEK 5 000 000 |
Project duration | March 2022 - December 2025 |
Status | Ongoing |
Venture | Financial Market Research |
Call | Research on Financial markets 2022-2024 |
Purpose and goal
** Denna text är maskinöversatt ** This project will study market failures in the consumer credit market and propose measures. We will study behaviors that lead borrowers to take on too much debt, or choose credit products that are very expensive, or other decisions that seem to counteract the borrower´s interest. We will study the supply side of the consumer credit market to see if and how, for example, product design and marketing play into bad borrower decisions. We will study the underlying causes of failures in the consumer credit market.
Expected effects and result
This project will identify the behavioral traits that lead borrowers to take on too much debt, or to choose credit products that are very costly, or other outcomes that appear to work against the interest of the borrower. The project will also bring empirical methods of behavioral finance to study credit market outcomes in Sweden. The project will also investigate the state of the Swedish consumer credit market as an ecosystem to support an analysis of the socio-economic costs and potential benefits of reform that can strengthen the sustainability of the market.
Planned approach and implementation
The research undertaken with this project will be mainly empirical. This means that a significant effort will be required to gather and/or access the data needed for the research. We will obtain data from field data obtained from our industry partners who gather data on their customers, field data from government agencies, many of which have developed data services for research (e.g. Kronofogden’s API), and data obtained from controlled experiments and survey and interview data. Our main effort will be to study the data using machine learning and econometric methods.