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All Journal International Journal of Advanced Science Computing and Engineering
Zulfikri, Fahmi
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Predicting Peer to Peer Lending Loan Risk Using Classification Approach Zulfikri, Fahmi; Tryanda, Dendy; Syarif, Allevia; Patria, Harry
International Journal of Advanced Science Computing and Engineering Vol. 3 No. 2 (2021)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (318.08 KB) | DOI: 10.62527/ijasce.3.2.57

Abstract

Technological innovations have affected all sectors of life, especially, the financial sector with the emergence of financial technology. One of them is marked by the emergence of Peer-to-Peer Lending ("P2P Lending). Credit Risk Management is essential to P2P Lending as it directly affects business results, therefore it is important for P2P Lending to predict borrowers with the highest probability to become good or bad loans based on their profile or characteristics. In the experiments, five classification algorithms are used, which are Gradient Boosted Trees, Naïve Bayes, Random Forest, Decision Tree and Logistic Regression. The result is two modelling performed well that is Random Forest with accuracy 93.38% and Decision Tree with 92.35%.
Co-Authors Apriani, Yosephin Kurjono, Kurjono Neti Budiwati Patria, Harry pratama, sony Rahmad, Abdul Sumartini Sumartini Syarif, Allevia Tryanda, Dendy