<![CDATA[Purpose: Credit risk is one of the most fundamental risks that P2P lending platforms have. The magnitude of information asymmetry, consumer behavior, and the unequal distribution of financial literacy make credit risk in P2P lending more vulnerable in several parts of Indonesia. The purpose of this study was to determine the domicile of the borrower on the credit risk in P2P lending Methodology: We use time series data from January 2018-December 2021 for analysis. Vector Error Correction Model (VECM) is used to analyze the data. Findings: The results show that borrowers domiciled outside Java influence the credit default significantly positively, while borrowers domiciled in Java influence credit default significantly negatively. Moreover, interest rate influences positively significant on P2P lending default, while inflation influences positively on P2P lending default. Novelty: this paper is the first paper to analyze the P2P credit default in Indonesia using time series analysis. ]]>
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