Leveraging Federated Learning for Unsecured Loan Risk Assessment on Decentralized Finance Lending Platforms

Qian’ang Mao1; Sheng Wan2; Daning Hu2; Jiaqi Yan1; Jin Hu2; Xuan Yang3

10.1109/ICDMW60847.2023.00092

2023

23rd IEEE International Conference on Data Mining (IEEE ICDM)

2375-9232

979-8-3503-8165-8

2023 IEEE International Conference on Data Mining Workshops (ICDMW)

663-670

4-4 Dec. 2023

Shanghai, China

10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA

IEEE COMPUTER SOC

This study proposes a novel privacy-preserving unsecured loan risk assessment system that allows decentralized finance (DeFi) lending platforms to offer loans without collateral. This system leverages federated learning methods to train risk assessment models using both off-chain and on-chain data sources, to more accurately evaluate borrower default risk for unsecured loans. Moreover, this system is built on a trusted execution environment (TEE) with program-level isolation, which provides a secure and efficient solution for DeFi platforms to offer unsecured loans. The effectiveness of this platform is validated through a set of simulation experiments. These experiments underscore the capability of the federated learning models to accurately assess borrower default risk while preserving stringent data privacy standards. The unique and innovative system design we proposed offers significant advancements for DeFi lending platforms. These improvements have the potential to greatly enhance DeFi platforms' inclusiveness by offering unsecured loans while maintaining efficiency, and security.

federated learning blockchain unsecured lending credit scoring

其他

英语

CPCI-S

Computer Science

Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Computer Science, Theory & Methods

WOS:001164077500084

IEEE

1.Nanjing University, Nanjing, China
2.Southern University of Science and Technology, Shenzhen, China
3.University of Zurich, Zurich, Switzerland

Qian’ang Mao,Sheng Wan,Daning Hu,et al. Leveraging Federated Learning for Unsecured Loan Risk Assessment on Decentralized Finance Lending Platforms[C]. 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA:IEEE COMPUTER SOC,2023:663-670.