Author(s): Nguyen Thi Tuong Anh, Pham Thi My Hanh, Vu Thi Le Thu
The Covid 19 Pandemic has rapidly accelerated the structural shift toward fully digital solutions, hence uplifting demand for virtual financial services such as digital lending services. This paper aims to predict the probability of loan defaults in the US Peer-to-Peer lending market through utilizing the dataset of Lending Club, the biggest US P2P lending platform, which covers 1,137,850 loans within the period from Q1 2017 to Q3 2020. A logistic regression model is developed to consolidate strong evidence of Borrowers characteristics, Loan characteristics and Credit Characteristics on the likelihood of defaulted loans. More importantly, the impact of Covid-19 pandemic presence is found visible for the performance of the defaulted loans in this P2P market