Question 1

What are the best machine learning algorithms for credit scoring?

Accepted Answer

Papers in the repository, such as 'Benchmarking state-of-the-art classification algorithms for credit scoring,' compare methods like SVM, logistic regression, and ensemble techniques, often finding ensembles perform best but depend on data quality and context.

Question 2

How to handle sample selection bias in credit modeling?

Accepted Answer

The Sample Selection section includes resources on reject inference and instance sampling, discussing techniques to address bias from using only accepted applicants' data, though practical implementation details are sparse.

Question 3

Credit scoring XGBoost vs logistic regression: which is better?

Accepted Answer

Studies like 'A comparative study on base classifiers in ensemble methods' suggest XGBoost and other ensemble methods often outperform logistic regression in accuracy, but logistic regression remains popular for interpretability and regulatory compliance.

Question 4

How to make credit risk models explainable for regulators?

Accepted Answer

The Model Explainability section references papers from the Bank of England and others, offering frameworks to interpret ML models, but users must adapt these concepts to their specific tools and jurisdictions.

Question 5

Where can I find datasets for building credit scoring models?

Accepted Answer

The repository does not provide datasets; it focuses on academic resources, so users need to source data from public repositories like UCI or proprietary sources, which is a common challenge noted in the literature.

Question 6

What are the regulatory challenges for using machine learning in credit risk?

Accepted Answer

Resources like 'Regulatory learning: How to supervise machine learning models?' discuss tensions between dynamic ML models and static regulatory frameworks, highlighting compliance hurdles around explainability and bias.

Awesome Credit Modeling

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