Corpus ID: 233231639

Enabling Machine Learning Algorithms for Credit Scoring - Explainable Artificial Intelligence (XAI) methods for clear understanding complex predictive models

@article{Biecek2021EnablingML,
  title={Enabling Machine Learning Algorithms for Credit Scoring - Explainable Artificial Intelligence (XAI) methods for clear understanding complex predictive models},
  author={Przemysław Biecek and Marcin Chlebus and Janusz Gajda and Alicja Gosiewska and Anna Kozak and Dominik Ogonowski and Jakub Sztachelski and Piotr Wojewnik},
  journal={ArXiv},
  year={2021},
  volume={abs/2104.06735}
}
Rapid development of advanced modelling techniques gives an opportunity to develop tools that are more and more accurate. However as usually, everything comes with a price and in this case, the price to pay is to loose interpretability of a model while gaining on its accuracy and precision. For managers to control and effectively manage credit risk and for regulators to be convinced with model quality the price to pay is too high. So, it prevents them from using advanced models due to the lack… Expand
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References

SHOWING 1-10 OF 35 REFERENCES
Comprehensible credit scoring models using rule extraction from support vector machines
TLDR
This paper provides an overview of the recently proposed rule extraction techniques for SVMs and introduces two others taken from the artificial neural networks domain, being Trepan and G-REX, which rank at the top of comprehensible classification techniques. Expand
An experimental comparison of classification algorithms for imbalanced credit scoring data sets
TLDR
The results from this empirical study indicate that the random forest and gradient boosting classifiers perform very well in a credit scoring context and are able to cope comparatively well with pronounced class imbalances in these data sets. Expand
A comparison of neural networks and linear scoring models in the credit union environment
Abstract The purpose of the present paper is to explore the ability of neural networks such as multilayer perceptrons and modular neural networks, and traditional techniques such as linearExpand
DALEX: Explainers for Complex Predictive Models in R
  • P. Biecek
  • Computer Science
  • J. Mach. Learn. Res.
  • 2018
TLDR
A consistent collection of explainers for predictive models, a.k.a. black boxes, based on a uniform standardized grammar of model exploration which may be easily extended. Expand
Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research
TLDR
The study of Baesens et al. (2003) is updated and several novel classification algorithms to the state-of-the-art in credit scoring are compared, providing an independent assessment of recent scoring methods and offering a new baseline to which future approaches can be compared. Expand
Explaining Explanations: An Approach to Evaluating Interpretability of Machine Learning
TLDR
The definition of explainability is provided and how it can be used to classify existing literature is shown and discussed to create best practices and identify open challenges in explanatory artificial intelligence. Expand
A k-nearest-neighbour classifier for assessing consumer credit risk
SUMMARY The last 30 years have seen the development of credit scoring techniques for assessing the creditworthiness of consumer loan applicants. Traditional credit scoring methodology has involvedExpand
A Unified Approach to Interpreting Model Predictions
TLDR
A unified framework for interpreting predictions, SHAP (SHapley Additive exPlanations), which unifies six existing methods and presents new methods that show improved computational performance and/or better consistency with human intuition than previous approaches. Expand
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
TLDR
LIME is proposed, a novel explanation technique that explains the predictions of any classifier in an interpretable and faithful manner, by learning aninterpretable model locally varound the prediction. Expand
Machine Learning in Banking Risk Management: A Literature Review
There is an increasing influence of machine learning in business applications, with many solutions already implemented and many more being explored. Since the global financial crisis, risk managementExpand
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