Abstract
This research will impact banking financing and credit risk scoring, as well as private investments, business angels, crowdfunding, and others. Innovative research would use alternative data for financial risk assessment and include greenwashing evaluation and non compliant tax behavior as risky behaviors. Also, in the current landscape characterized by the rapid development of the open-banking sector and the use of blockchain for financial transactions, current research has the potential to partner with financial institutions and fintech businesses for the exploitation of results. Results will be disseminated through conferences and high-ranking journals. We will use alternative data available on internet to feed Machine Learning models for fraud detection and to develop a model for credit scoring. The model developed will use alternative and traditional public data available on the internet in order to predict the credit risk of default based on a complex fraud model including also intention to tax evasion and greenwashing practices as proxies. We will also collect psychometric data through interviews, experiments and focus groups. Text and image data will be analysed using machine learning. This type of data will be gathered with the approval of the participant.
Role UT
The University of Twente is a coordinator in the project. The team from UT includes the following individuals:
