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Peer-to-peer lending default prediction model: a credit scoring application with social media data

Author

Listed:
  • Taufik Faturohman
  • Sudarso Kaderi Wiryono
  • Hasna Laila Nabila Khilfah
  • Allesandra Andri
  • Muhammad Abdullah Hamzah
  • Okta Saputra
  • Gun Gun Indrayana

Abstract

This study applied social media data to build credit scoring models in reducing non-performing loans in a peer-to-peer (P2P) lending portfolio. P2P lending can increase financial inclusion that has been known as one of the important factors in reducing poverty and enhancing prosperity. Discriminant analysis (DA), logistic regression (LR), neural network (NN), and support vector machine (SVM) were employed to compare effectiveness of the models. Results show that NN and SVM provide better outputs in predicting the default prediction model. Furthermore, social media data comprehensively increase the accuracy of lending default predictions.

Suggested Citation

  • Taufik Faturohman & Sudarso Kaderi Wiryono & Hasna Laila Nabila Khilfah & Allesandra Andri & Muhammad Abdullah Hamzah & Okta Saputra & Gun Gun Indrayana, 2024. "Peer-to-peer lending default prediction model: a credit scoring application with social media data," International Journal of Monetary Economics and Finance, Inderscience Enterprises Ltd, vol. 17(2/3), pages 189-200.
    • Handle: RePEc:ids:ijmefi:v:17:y:2024:i:2/3:p:189-200
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