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As the COVID-19 pandemic adversely affects the financial markets, a better understanding of the lending dynamics of a successful marketplace is necessary under the conditions of financial distress. Using the loan book database of Mintos (Latvia) and employing logit regression method, we provide evidence of the pandemic-induced exposure to default risk in the marketplace lending market. Our analysis indicates that the probability of default increases from 0.056 in the pre-pandemic period to 0.079 in the post-pandemic period. COVID-19 pandemic has a significant impact on default risk during May and June of 2020. We also find that the magnitude of the impact of COVID-19 risk is higher for borrowers with lower credit ratings and in countries with low levels of FinTech adoption. Our main findings are robust to sample selection bias allowing for a better understanding of and quantifying risks related to FinTech loans during the pandemic and periods of overall economic distress.

We aggregate the United States (US) state-level data with LendingClub's loan book covering the period from 2008 to 2019. LendingClub is a FinTech lending company that provides loans through a technology-driven platform. It was one of the pioneering and leading US peer-to-peer (P2P) lending platforms. Our dataset consists of over two million observations (N=2,703,430) with diverse loan, borrowers and state-specific features. We provide the description of variables, descriptive statistics, and STATA code with the full dataset. The US possesses significant cross-state variation in terms of economic and demographic characteristics while having risk-sharing policies at the federal level to protect states' creditworthiness. This unique feature of our combined database creates an ideal opportunity to explore the P2P lending market within the context of macroeconomic variables. As the dataset covers a 12-year period for all US states, it enables further cross-sectional and longitudinal analyses of the FinTech lending market.

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