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Credit Risk Assessment in P2P Lending Using LightGBM and Particle Swarm Optimization
16 references·0 citing·43 second hop
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References

  • 2015Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research1,277 cited
  • 2003Credit rating analysis with support vector machines and neural networks: a market comparative study1,017 cited
  • 2014A data-driven approach to predict the success of bank telemarketing902 cited
  • 2000A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers744 cited
  • 2018Study on a prediction of P2P network loan default based on the machine learning LightGBM and XGboost algorithms according to different high dimensional data cleaning444 cited
  • 2015Risk assessment in social lending via random forests398 cited
  • 2015Determinants of Default in P2P Lending327 cited
  • 2015Instance-based credit risk assessment for investment decisions in P2P lending313 cited
  • 2016Market Mechanisms in Online Peer-to-Peer Lending275 cited
  • 2016The use of profit scoring as an alternative to credit scoring systems in peer-to-peer (P2P) lending253 cited
  • 2020A novel construction cost prediction model using hybrid natural and light gradient boosting226 cited
  • 2019LightGBM: an Effective Decision Tree Gradient Boosting Method to Predict Customer Loyalty in the Finance Industry221 cited
  • 2017Cost-sensitive boosted tree for loan evaluation in peer-to-peer lending189 cited
  • 2019

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A study on predicting loan default based on the random forest algorithm
187 cited
  • 2014Development and application of consumer credit scoring models using profit-based classification measures179 cited
  • 2014Credit risk assessment model for Jordanian commercial banks: Neural scoring approach150 cited
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