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Internet Financial Credit Scoring Models Based on Deep Forest and Resampling Methods
16 references·0 citing·45 second hop
Internet Financial Credit Scoring Models Based on Deep Forest and Resampling Methods (2023)Internet Financial Credit Scoring Models …Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects (2021)Machine learning for credit s…Artificial neural network technique for improving prediction of credit card default: A stacked sparse autoencoder approach (2021)Artificial neural network tec…A new deep learning ensemble credit risk evaluation model with an improved synthetic minority oversampling technique (2020)A new deep learning ensemble …Cost-sensitive semi-supervised selective ensemble model for customer credit scoring (2019)Cost-sensitive semi-supervise…A hybrid deep learning model for consumer credit scoring (2018)A hybrid deep learning model …The information value of online social networks: Lessons from peer-to-peer lending (2017)The information value of onli…A deep learning approach for credit scoring using credit default swaps (2016)A deep learning approach for …How low can you go? — Overcoming the inability of lenders to set proper interest rates on unsecured peer-to-peer lending markets (2015)How low can you go? — Overcom…Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research (2015)Benchmarking state-of-the-art…An empirical evaluation of the performance of binary classifiers in the prediction of credit ratings changes (2015)An empirical evaluation of th…An Overview of Personal Credit Scoring: Techniques and Future Work (2012)An Overview of Personal Credi…Credit scoring with a data mining approach based on support vector machines (2006)Credit scoring with a data mi…Mining the customer credit using classification and regression tree and multivariate adaptive regression splines (2004)Mining the customer credit us…A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers (2000)A survey of credit and behavi…Construction of a k-nearest-neighbour credit-scoring system (1997)Construction of a k-nearest-n…A comparison of neural networks and linear scoring models in the credit union environment (1996)A comparison of neural networ…Credit Rationing in Markets with Imperfect Information (1981)Social Capital: Its Origins and Applications in Modern Sociology (1998)The Digitization of Word of Mouth: Promise and Challenges of Online Feedback Mechanisms (2003)The value of big data for credit scoring: Enhancing financial inclusion using mobile phone data and social network analytics (2018)Peer to Peer (P2P) Lending Problems and Potential Solutions: A Systematic Literature Review (2019)FinTech, Lending and Payment Innovation: A Review (2020)Limited-dependent and qualitative variables in econometrics (1983)A Survey of Corporate Governance (1997)Stated choice methods analysis and applications (2000)A multicriteria outranking approach for modeling corporate credit ratings: An application of the Electre Tri-nC method (2018)Do Traditional Financial Distress Prediction Models Predict the Early Warning Signs of Financial Distress? (2019)Artificial intelligence in Finance: a comprehensive review through bibliometric and content analysis (2024)The Pricing of Options and Corporate Liabilities (1973)Managerial Applications of Neural Networks: The Case of Bank Failure Predictions (1992)Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience) (1994)Recent developments in consumer credit risk assessment (2007)A comparative assessment of ensemble learning for credit scoring (2010)A Stochastic Approximation Method (1951)CREDIT SCORING, STATISTICAL TECHNIQUES AND EVALUATION CRITERIA: A REVIEW OF THE LITERATURE (2011)Neural network credit scoring models (2000)Credit rating analysis with support vector machines and neural networks: a market comparative study (2003)A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems (2010)A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring (2017)Machine behaviour (2019)Support vector machines for credit scoring and discovery of significant features (2008)Two-stage consumer credit risk modelling using heterogeneous ensemble learning (2019)How are reinforcement learning and deep learning algorithms used for big data based decision making in financial industries–A review and research agenda (2022)Machine learning-driven credit risk: a systemic review (2022)Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models (2018)A machine learning approach combining expert knowledge with genetic algorithms in feature selection for credit risk assessment (2021)Generación de series de tiempo financieras sintéticas para "data augmentation" usando redes neuronales generativas adversarias (GAN) (2021)A novel ensemble feature selection method by integrating multiple ranking information combined with an SVM ensemble model for enterprise credit risk prediction in the supply chain (2022)A two-stage hybrid credit risk prediction model based on XGBoost and graph-based deep neural network (2022)FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY (1968)Comparing Predictive Accuracy (1994)Comparing Predictive Accuracy (1995)Credit default prediction from user-generated text in peer-to-peer lending using deep learning (2021)Big data, artificial intelligence and machine learning: A transformative symbiosis in favour of financial technology (2022)A Discriminant Analysis of Predictors of Business Failure (1972)Forecasting with artificial neural networks: (1998)Computer assisted customer churn management: State-of-the-art and future trends (2005)An experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring (2008)Machine Learning in Financial Crisis Prediction: A Survey (2011)'It's Reducing a Human Being to a Percentage' (2018)Machine Learning and Natural Language Processing in Mental Health: Systematic Review (2020)
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References

  • 2015Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research1,277 cited
  • 2006Credit scoring with a data mining approach based on support vector machines805 cited
  • 2000A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers744 cited
  • 1996A comparison of neural networks and linear scoring models in the credit union environment522 cited
  • 2021Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects438 cited
  • 2004Mining the customer credit using classification and regression tree and multivariate adaptive regression splines378 cited
  • 2020A new deep learning ensemble credit risk evaluation model with an improved synthetic minority oversampling technique234 cited
  • 2017The information value of online social networks: Lessons from peer-to-peer lending233 cited
  • 2016A deep learning approach for credit scoring using credit default swaps196 cited
  • 2015An empirical evaluation of the performance of binary classifiers in the prediction of credit ratings changes169 cited
  • 2015How low can you go? — Overcoming the inability of lenders to set proper interest rates on unsecured peer-to-peer lending markets96 cited
  • 1997Construction of a k-nearest-neighbour credit-scoring system96 cited
  • 2019Cost-sensitive semi-supervised selective ensemble model for customer credit scoring78 cited

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  • 2018A hybrid deep learning model for consumer credit scoring75 cited
  • 2021Artificial neural network technique for improving prediction of credit card default: A stacked sparse autoencoder approach70 cited
  • 2012An Overview of Personal Credit Scoring: Techniques and Future Work66 cited
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