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Interpretable Machine Learning Models for Credit Risk Assessment
5 references·0 citing·24 second hop
Interpretable Machine Learning Models for Credit Risk Assessment (2024)Interpretable Machine Learning Models for…Credit Risk Analysis using LightGBM and a comparative study of popular algorithms (2021)Credit Risk Analysis using Li…Credit Risk Scoring Analysis Based on Machine Learning Models (2019)Credit Risk Scoring Analysis …Credit Card Default Prediction using Machine Learning Techniques (2018)Credit Card Default Predictio…Classification of Credit Card Default Clients Using LS-SVM Ensemble (2018)Classification of Credit Card…Credit risk measurement: Developments over the last 20 years (1997)Credit risk measurement: Deve…FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY (1968)The Pricing of Options and Corporate Liabilities (1973)ON THE PRICING OF CORPORATE DEBT: THE RISK STRUCTURE OF INTEREST RATES* (1974)Bank Capital Regulation in Contemporary Banking Theory: A Review of the Literature (2001)Credit Risk in Two Institutional Regimes: Spanish Commercial and Savings Banks (2002)Safer ratios, riskier portfolios: Banks׳ response to government aid (2014)Economics:Principles in Action (1978)A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers (2000)The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients (2008)Personal credit default prediction fusion framework based on self-attention and cross-network algorithms (2024)Using data mining to improve assessment of credit worthiness via credit scoring models (2011)Defining architecture components of the Big Data Ecosystem (2014)A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring (2017)Predicting Credit Card Defaults with Machine Learning Algorithm Using Customer Database (2023)Credit card default prediction using ML and DL techniques (2024)Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience) (1994)Consumer credit-risk models via machine-learning algorithms (2010)Credit risk analysis using boosting methods (2023)Three-stage research framework to assess and predict the financial risk of SMEs based on hybrid method (2023)Recent developments in consumer credit risk assessment (2007)A data driven ensemble classifier for credit scoring analysis (2009)Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research (2015)RETRACTED ARTICLE: Explainable AI Model for Recognizing Financial Crisis Roots Based on Pigeon Optimization and Gradient Boosting Model (2023)Unveiling the nonlinear drivers of urban land resources on carbon emissions: The mediating role of industrial upgrading and technological innovation (2024)
this work references cited by second hop

References

  • 1997Credit risk measurement: Developments over the last 20 years836 cited
  • 2018Credit Card Default Prediction using Machine Learning Techniques42 cited
  • 2021Credit Risk Analysis using LightGBM and a comparative study of popular algorithms30 cited
  • 2019Credit Risk Scoring Analysis Based on Machine Learning Models21 cited
  • 2018Classification of Credit Card Default Clients Using LS-SVM Ensemble19 cited

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