Year: 2024
Type: conference-paper
Abstract: :This paper proposes a decentralized personal credit risk assessment scheme that combines the random forest algorithm and smart contract technology. Traditional credit risk assessment suffers from the problems of information opacity and assessment bias. To improve the accuracy and fairness of the assessment, the random forest model was embed in a smart contract to achieve automated and transparent credit risk assessment. Through experimental verification, Random Forest performs well in handling credit risk assessment in terms of accuracy, precision and recall. This scheme ensures the security and fairness of the assessment process while improving the precision of risk assessment.
Authors: Lei Huang, Chuan Li
Institution: Xi’an University of Posts and Telecommunications
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4411729992