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- W3124133053 abstract "Peer-to-Peer (P2P) fintech platforms allow cost reduction and service improvement in credit lending. However, these improvements may come at the price of a worse credit risk measurement, and this can hamper lenders and endanger the stability of a financial system. We approach the problem of credit risk for Peer-to-Peer (P2P) systems by presenting a latent factor-based classification technique to divide the population into major network communities in order to estimate a more efficient logistic model. Given a number of attributes that capture firm performances in a financial system, we adopt a latent position model which allow us to distinguish between communities of connected and not-connected firms based on the spatial position of the latent factors. We show through empirical illustration that incorporating the latent factor-based classification of firms is particularly suitable as it improves the predictive performance of P2P scoring models." @default.
- W3124133053 created "2021-02-01" @default.
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- W3124133053 date "2019-01-01" @default.
- W3124133053 modified "2023-09-23" @default.
- W3124133053 title "Latent Factor Models for Credit Scoring in P2P Systems" @default.
- W3124133053 doi "https://doi.org/10.2139/ssrn.3325231" @default.
- W3124133053 hasPublicationYear "2019" @default.
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