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Network Based Scoring Models to Improve Credit Risk Management in Peer to Peer Lending Platforms.

Paolo Giudici1, Branka Hadji-Misheva2, Alessandro Spelta1

  • 1Department of Economics and Management, Fintech Laboratory, University of Pavia, Pavia, Italy.

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Summary
This summary is machine-generated.

FinTech peer-to-peer lending offers benefits but increases credit risk. This study enhances credit scoring models by using borrower network topology to improve risk prediction and financial stability.

Keywords:
contagioncredit riskcredit scoringnetwork modelspeer to peer lending

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Area of Science:

  • Financial Technology (FinTech)
  • Credit Risk Modeling
  • Network Science

Background:

  • The rise of FinTech has transformed financial intermediation, particularly in credit services.
  • Peer-to-peer (P2P) lending platforms offer advantages like speed and cost reduction but introduce higher credit and systemic risks.
  • Existing credit risk models may not fully capture the complexities and interconnectedness inherent in P2P lending.

Purpose of the Study:

  • To enhance the accuracy of credit risk assessment in FinTech P2P lending.
  • To develop improved credit scoring models by incorporating network topology.
  • To mitigate risks and promote financial stability within the P2P lending ecosystem.

Main Methods:

  • Leveraging topological information from borrower similarity networks derived from financial data.
  • Utilizing topological coefficients (e.g., borrower importance, community structures) as explanatory variables.
  • Integrating these topological features into traditional credit scoring models.

Main Results:

  • Demonstrated improved predictive performance of credit scoring models.
  • Showcased the value of network topology in identifying and quantifying credit risks.
  • Provided evidence for enhanced accuracy in assessing borrower creditworthiness.

Conclusions:

  • Topological analysis of borrower networks offers a novel and effective approach to credit risk modeling in FinTech.
  • Incorporating network structure enhances the predictive power of credit scoring, benefiting both lenders and financial stability.
  • This methodology provides a more robust framework for managing risks associated with P2P lending platforms.