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Using Bayesian networks to perform reject inference
7 references·4 citing·38 second hop
Using Bayesian networks to perform reject inference (2019)Using Bayesian networks to perform reject…Reject inference in credit scoring using Semi-supervised Support Vector Machines (2017)Reject inference in credit sc…Intelligent Credit Scoring: Building and Implementing Better Credit Risk Scorecards (2016)Intelligent Credit Scoring: B…Reject inference, augmentation, and sample selection (2007)Reject inference, augmentatio…Recent developments in consumer credit risk assessment (2007)Recent developments in consum…Does reject inference really improve the performance of application scoring models? (2004)Does reject inference really …Sample selection bias in credit scoring models (2003)Sample selection bias in cred…Does reject inference really improve the performance of application scoring models? (2003)Does reject inference really …Credit risk evaluation: a comprehensive study (2022)Credit risk evaluation: a com…Assessing and predicting small industrial enterprises’ credit ratings: A fuzzy decision-making approach (2022)Assessing and predicting smal…A Framework of Global Credit-Scoring Modeling Using Outlier Detection and Machine Learning in a P2P Lending Platform (2022)A Framework of Global Credit-…Reject inference methods in credit scoring (2021)Reject inference methods in c…The demand for deductibles in private health insurance (1981)An econometric analysis of the bank credit scoring problem (1989)Good practice in retail credit scorecard assessment (2005)Genetic programming for credit scoring: The case of Egyptian public sector banks (2009)The Pricing of Options and Corporate Liabilities (1973)Statistical Classification Methods in Consumer Credit Scoring: A Review (1997)Neural network credit scoring models (2000)CREDIT SCORING, STATISTICAL TECHNIQUES AND EVALUATION CRITERIA: A REVIEW OF THE LITERATURE (2011)Machine Learning in Financial Crisis Prediction: A Survey (2011)A review of trade credit literature: Opportunities for research in operations (2013)Credit Scoring and Its Applications (2002)Enterprise risk management: small business scorecard analysis (2009)Credit scoring for microfinance: is it worth it? (2011)Credit Scoring and Its Applications (2017)Statistical Challenges of Administrative and Transaction Data (2018)Bankruptcy prediction in banks and firms via statistical and intelligent techniques – A review (2006)Trust and Credit: The Role of Appearance in Peer-to-peer Lending (2012)Screening Peers Softly: Inferring the Quality of Small Borrowers (2015)An artificial intelligence system for predicting customer default in e-commerce (2018)Predicting loan default in peer‐to‐peer lending using narrative data (2019)Three-stage reject inference learning framework for credit scoring using unsupervised transfer learning and three-way decision theory (2020)Multiple Attribute Decision Making: Methods and Applications (2011)Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research (2015)Best-worst multi-criteria decision-making method: Some properties and a linear model (2015)A novel q-rung orthopair fuzzy best-worst method, Shannon entropy and MARCOS method for mobile medical app service quality evaluation (2024)SME default prediction: A systematic methods evaluation (2024)A study on credit scoring modeling with different feature selection and machine learning approaches (2020)FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY (1968)Credit risk measurement: Developments over the last 20 years (1997)Credit scoring with a data mining approach based on support vector machines (2006)Transforming credit risk assessment: A systematic review of AI and machine learning applications (2025)Bankruptcy prediction of small and medium enterprises using a flexible binary generalized extreme value model (2015)Deep generative models for reject inference in credit scoring (2020)Consumer credit risk assessment: A review from the state-of-the-art classification algorithms, data traits, and learning methods (2023)How Costly is Noise? Data and Disparities in Consumer Credit (2021)Advancing credit risk modelling with Machine Learning: A comprehensive review of the state-of-the-art (2024)Algorithmic self-referentiality: How machine learning pushes calculative practices to assess themselves (2024)A new approach for reject inference in credit scoring using kernel-free fuzzy quadratic surface support vector machines (2018)
this work references cited by second hop

References

  • 2007Recent developments in consumer credit risk assessment472 cited
  • 2017Reject inference in credit scoring using Semi-supervised Support Vector Machines132 cited
  • 2003Sample selection bias in credit scoring models130 cited
  • 2003Does reject inference really improve the performance of application scoring models?88 cited
  • 2007Reject inference, augmentation, and sample selection81 cited
  • 2004Does reject inference really improve the performance of application scoring models?70 cited
  • 2016Intelligent Credit Scoring: Building and Implementing Better Credit Risk Scorecards51 cited

Cited by

  • 2022Assessing and predicting small industrial enterprises’ credit ratings: A fuzzy decision-making approach35 cited
  • 2022Credit risk evaluation: a comprehensive study35 cited
  • 2022A Framework of Global Credit-Scoring Modeling Using Outlier Detection and Machine Learning in a P2P Lending Platform21 cited
  • 2021Reject inference methods in credit scoring19 cited

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