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A Novel Reject Inference Model Using Outlier Detection and Gradient Boosting Technique in Peer-to-Peer Lending
16 references·7 citing·38 second hop
A Novel Reject Inference Model Using Outlier Detection and Gradient Boosting Technique in Peer-to-Peer Lending (2019)A Novel Reject Inference Model Using Outl…Study on a prediction of P2P network loan default based on the machine learning LightGBM and XGboost algorithms according to different high dimensional data cleaning (2018)Study on a prediction of P2P …Information: Hard and Soft (2018)Information: Hard and SoftThe information value of online social networks: Lessons from peer-to-peer lending (2017)The information value of onli…Cost-sensitive boosted tree for loan evaluation in peer-to-peer lending (2017)Cost-sensitive boosted tree f…A novel heterogeneous ensemble credit scoring model based on bstacking approach (2017)A novel heterogeneous ensembl…A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring (2017)A boosted decision tree appro…From Fintech to Finlife: the case of Fintech Development in China (2016)From Fintech to Finlife: the …Risk assessment in social lending via random forests (2015)Risk assessment in social len…Instance-based credit risk assessment for investment decisions in P2P lending (2015)Instance-based credit risk as…Determinants of Default in P2P Lending (2015)Determinants of Default in P2…Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research (2015)Benchmarking state-of-the-art…Evaluating credit risk and loan performance in online Peer-to-Peer (P2P) lending (2014)Evaluating credit risk and lo…Development and application of consumer credit scoring models using profit-based classification measures (2014)Development and application o…Credit scoring using the clustered support vector machine (2014)Credit scoring using the clus…Benchmarking state-of-the-art classification algorithms for credit scoring (2003)Benchmarking state-of-the-art…Credit Rationing in Markets with Imperfect Information (1981)Credit Rationing in Markets w…Louvain community-based label assignment for reject inference in peer-to-peer lending (2025)Louvain community-based label…Fighting sampling bias: A framework for training and evaluating credit scoring models (2025)Fighting sampling bias: A fra…Success Factors Influencing Peer-to-Peer Lending to Support Financial Innovation (2023)Success Factors Influencing P…P2P Lending Default Prediction Based on AI and Statistical Models (2022)P2P Lending Default Predictio…A Framework of Global Credit-Scoring Modeling Using Outlier Detection and Machine Learning in a P2P Lending Platform (2022)A Framework of Global Credit-…Risk-return modelling in the p2p lending market: Trends, gaps, recommendations and future directions (2021)Risk-return modelling in the …Forecasting loss given default for peer-to-peer loans via heterogeneous stacking ensemble approach (2021)Forecasting loss given defaul…Increases in risk and in risk aversion (1974)Risk Sharing and Incentives in the Principal and Agent Relationship (1979)Corporate financing and investment decisions when firms have information that investors do not have (1984)The Benefits of Lending Relationships: Evidence from Small Business Data (1994)Financial Dependence and Growth (1996)The Nature of the Firm (1937)The Costs and Benefits of Ownership: A Theory of Vertical and Lateral Integration (1986)Financial Integration and Firm Performance: Evidence from Foreign Bank Entry in Emerging Markets (2007)Fintech: what’s old, what’s new? 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References

  • 1981Credit Rationing in Markets with Imperfect Information12,762 cited
  • 2015Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research1,277 cited
  • 2003Benchmarking state-of-the-art classification algorithms for credit scoring903 cited
  • 2018Information: Hard and Soft787 cited
  • 2017A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring770 cited
  • 2014Evaluating credit risk and loan performance in online Peer-to-Peer (P2P) lending495 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
  • 2014Credit scoring using the clustered support vector machine269 cited
  • 2017The information value of online social networks: Lessons from peer-to-peer lending233 cited
  • 2017A novel heterogeneous ensemble credit scoring model based on bstacking approach214 cited
  • 2016From Fintech to Finlife: the case of Fintech Development in China201 cited

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  • 2017Cost-sensitive boosted tree for loan evaluation in peer-to-peer lending189 cited
  • 2014Development and application of consumer credit scoring models using profit-based classification measures179 cited
  • Cited by

    • 2021Forecasting loss given default for peer-to-peer loans via heterogeneous stacking ensemble approach42 cited
    • 2021Risk-return modelling in the p2p lending market: Trends, gaps, recommendations and future directions33 cited
    • 2022P2P Lending Default Prediction Based on AI and Statistical Models31 cited
    • 2022A Framework of Global Credit-Scoring Modeling Using Outlier Detection and Machine Learning in a P2P Lending Platform21 cited
    • 2025Louvain community-based label assignment for reject inference in peer-to-peer lending16 cited
    • 2025Fighting sampling bias: A framework for training and evaluating credit scoring models15 cited
    • 2023Success Factors Influencing Peer-to-Peer Lending to Support Financial Innovation12 cited