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P2P Lending Default Prediction Based on AI and Statistical Models.

Po-Chang Ko1,2, Ping-Chen Lin2,3, Hoang-Thu Do1,4

  • 1Department of Intelligent Commerce, National Kaohsiung University of Science and Technology, Kaohsiung 82445, Taiwan.

Entropy (Basel, Switzerland)
|June 24, 2022
PubMed
Summary
This summary is machine-generated.

This study developed advanced prediction models to reduce risks in peer-to-peer (P2P) lending. The LightGBM model demonstrated superior accuracy, significantly improving platform revenue and mitigating default risks.

Keywords:
AI modelP2P lending default predictiondata processingstatistical model

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Area of Science:

  • Financial Technology (Fintech)
  • Data Science
  • Machine Learning

Background:

  • Peer-to-peer (P2P) lending has grown rapidly due to Fintech and big data, outpacing regulatory development.
  • This regulatory lag creates significant risks, including default and information asymmetry, on P2P platforms.
  • Existing risk mitigation strategies require enhancement to address the complexities of modern P2P lending.

Purpose of the Study:

  • To propose and evaluate advanced prediction models for mitigating default and information asymmetry risks in P2P lending.
  • To compare the performance of statistical and artificial intelligence (AI) models in classifying loan statuses.
  • To identify the most effective model for enhancing operational efficiency and profitability on P2P lending platforms.

Main Methods:

  • Data pre-processing of Lending Club customer data from 2018 Q3 to 2019 Q2.
  • Implementation and comparison of statistical models (Logistic Regression, Bayesian Classifier, LDA) and AI models (Decision Tree, Random Forest, LightGBM, ANN, CNN).
  • Evaluation using metrics like confusion matrix, AUC-ROC curve, KS chart, and Student's t-test.

Main Results:

  • The LightGBM model achieved the highest performance, outperforming other models by 2.91% in accuracy.
  • This improved accuracy translated to a projected revenue increase of nearly USD 24 million for Lending Club.
  • Statistical analysis confirmed the significant performance differences between the evaluated models.

Conclusions:

  • The LightGBM model offers a robust solution for enhancing risk management and profitability in P2P lending.
  • AI-driven prediction models are crucial for navigating the complexities and risks inherent in unregulated Fintech environments.
  • The findings provide valuable insights for P2P platforms seeking to optimize lending decisions and minimize financial exposure.