An Investigation In To the Credit Risk Conundrum of Relending In P2p

Abstract

In recent years, online Peer-to-Peer (P2P) lending market is rapidly expanding. This research paper aims to analyze the credit risk involved in P2P lending system. The P2P system allows investors to get significantly higher come back on asset as compared to bank deposit, but it comes with a risk of the loan and interest not being repaid. A loan is considered "good" if it's repaid with interest and on time. However, P2P lending platforms guide to elevated danger, amid which elevated credit threat: not possessed by the lenders, and complete threats: owing to the elevated interconnectedness amid borrowers. This identifies for latest and further precise credit threat representation to guard customers and safeguard economic constancy. In this manuscript a method is proposed to improve credit risk exactness of P2P podiums by leveraging topological data entrenched into comparison systems, resulting from borrowers' economic data. The algorithms are optimized to favour the potential good loans whilst identifying defaults or risky credits.