Advances in Machine Learning for Credit Risk Assessment

The field of machine learning for credit risk assessment is moving towards a greater emphasis on data quality and robustness. Researchers are investigating the impact of data quality issues, such as missing values and noisy attributes, on the predictive accuracy of machine learning models. Additionally, there is a growing interest in developing methods that can handle small datasets and provide reliable probability estimates. Noteworthy papers include:

  • A study on the impact of data quality on machine learning models for credit risk assessment, which proposed a methodology for assessing the robustness of models to data degradation.
  • A paper on loss given default prediction, which demonstrated the effectiveness of information-theoretic approaches in handling mixture-contaminated training data.
  • A study on Bayesian transfer learning for small-data predictive analytics, which introduced a framework for achieving enterprise-level prediction accuracy with small datasets.

Sources

How Data Quality Affects Machine Learning Models for Credit Risk Assessment

Loss Given Default Prediction Under Measurement-Induced Mixture Distributions: An Information-Theoretic Approach

SmallML: Bayesian Transfer Learning for Small-Data Predictive Analytics

Are Foundation Models Useful for Bankruptcy Prediction?

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