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Prediction of Default Probability of Credit-Card Bills
4 references·1 citing·21 second hop
Prediction of Default Probability of Credit-Card Bills (2019)Prediction of Default Probability of Cred…Benefits of relationship banking: Evidence from consumer credit markets (2018)Benefits of relationship bank…Prediction of Credit-Card Defaulters: A Comparative Study on Performance of Classifiers (2016)Prediction of Credit-Card Def…A new Mixture model for the estimation of credit card Exposure at Default (2015)A new Mixture model for the e…The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients (2008)The comparisons of data minin…Predicting Credit Risk for Unsecured Lending: A Machine Learning Approach (2021)Predicting Credit Risk for Un…A Guide to Modern Econometrics (2000)Testing for Serial Correlation in Linear Panel-data Models (2003)Empirical Analysis of Corporate Credit Lines (2009)Exposure at default models with and without the credit conversion factor (2016)What factors are influencing credit card customer’s default behavior in China? A study based on survival analysis (2019)Deep Neural Networks for Behavioral Credit Rating (2020)Statistical Classification Methods in Consumer Credit Scoring: A Review (1997)Benchmarking state-of-the-art classification algorithms for credit scoring (2003)Machine Learning in Banking Risk Management: A Literature Review (2019)A Review on Fairness in Machine Learning (2022)Bias and Unfairness in Machine Learning Models: A Systematic Review on Datasets, Tools, Fairness Metrics, and Identification and Mitigation Methods (2023)Insiders and Outsiders: The Choice between Informed and Arm's-Length Debt (1992)The Benefits of Lending Relationships: Evidence from Small Business Data (1994)Relationship Lending and Lines of Credit in Small Firm Finance (1995)Market-Based Loss Mitigation Practices for Troubled Mortgages Following the Financial Crisis (2010)Key Factors Determining the Expected Benefit of Customers When Using Bank Cards: An Analysis on Millennials and Generation Z in Romania (2019)Impact of marketplace lending on consumers’ future borrowing capacities and borrowing outcomes (2021)Neural networks for credit risk evaluation: Investigation of different neural models and learning schemes (2010)Credit Risk Analysis Using Machine and Deep Learning Models (2018)Machine learning techniques for credit risk evaluation: a systematic literature review (2020)Classification of Credit Card Default Clients Using LS-SVM Ensemble (2018)
this work references cited by second hop

References

  • 2008The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients881 cited
  • 2018Benefits of relationship banking: Evidence from consumer credit markets105 cited
  • 2015A new Mixture model for the estimation of credit card Exposure at Default35 cited
  • 2016Prediction of Credit-Card Defaulters: A Comparative Study on Performance of Classifiers24 cited

Cited by

  • 2021Predicting Credit Risk for Unsecured Lending: A Machine Learning Approach11 cited

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