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- W2907181002 abstract "Peer-to-Peer lending refers to online marketplaces where lenders lend to individuals or small businesses. This rapidly expanding new source of credit eliminates the traditional intermediary of financial services and reduces financial exclusion by allowing more people to borrow and lend. At the same time, the replacement of financial intermediaries by individuals poses new challenges, since both the borrowers and the lenders are human beings who are prone to a variety of behavioral biases. In the current work, we survey the growing literature on P2P lending and identify typical cognitive biases that may affect borrowers and lenders' financial decisions. Specifically, we review two core biases studied in the traditional behavioral literature and discuss their implementations in the context of P2P lending: Familiarity Bias as well as Stereotypes and Representativeness. Then, as a case in point we focus on Debt Account Aversion (DAA) which describes individuals' tendency to consistently paid off small debts first to reduce the nominal number of debts, although at the same time they had larger debts with higher interest rates. We conduct an online experiment to explore the effects of the P2P context on the magnitude of this DAA. Our main finding shows that when electing to repay a portfolio of P2P and bank debts (compared to paying multiple bank debts), borrowers exhibit a lower level of rational behavior and are more prone to Debt Account Aversion. Understanding the behavioral patterns of both lenders and borrowers in P2P lending will enable financial institutions and policy makers to devise tools and procedures that can ameliorate this new credit market, and guide individuals toward more effective management of their personal assets and obligations." @default.
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- W2907181002 date "2019-04-23" @default.
- W2907181002 modified "2023-09-26" @default.
- W2907181002 title "Behavioral Biases in Peer-to-Peer (P2P) Lending" @default.
- W2907181002 doi "https://doi.org/10.1142/9789813279469_0011" @default.
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