The DOI matches its Crossref record; 18 of the 19 references it cites were found in a registry or an index.
SHAP and LIME: An Evaluation of Discriminative Power in Credit Risk
Open accessAbstract
In credit risk estimation, the most important element is obtaining a probability of default as close as possible to the effective risk. This effort quickly prompted new, powerful algorithms that reach a far higher accuracy, but at the cost of losing intelligibility, such as Gradient Boosting or ensemble methods. These models are usually referred to as “black-boxes”, implying that you know the inputs and the output, but there is little way to understand what is going on under the hood. As a response to that, we have seen several different Explainable AI models flourish in recent years, with the aim of letting the user see why the black-box gave a certain output. In this context, we evaluate two very popular eXplainable AI (XAI) models in their ability to discriminate observations into groups, through the application of both unsupervised and predictive modeling to the weights these XAI models assign to features locally. The evaluation is carried out on real Small and Medium Enterprises data, obtained from official italian repositories, and may form the basis for the employment of such XAI models for post-processing features extraction.
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STREAM: A data-driven framework for physiological state monitoring in ICU patients
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Metadata
- Authors
- Gramegna, A., & Giudici, P.
- Affiliations
- University of Pavia (IT)
- Corresponding author
- Gramegna, Alex
- ORCID
- Giudici, Paolo
- Venue
- Frontiers in Artificial Intelligence
- Publisher
- Frontiers Media SA
- Published
- 2021-09-17
- Volume
- 4
- Pages
- 752558-752558
- ISSN
- 2624-8212 (ISSN-L)
- PMC
- PMC8484963
- OpenAlex
- W3200880768
- Cited by
- 306
- References
- 19
- Citations by year
- 2026: 57 · 2025: 87 · 2024: 89 · 2023: 46 · 2022: 26 · 2021: 1
- FWCI
- 14.31
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Artificial Intelligence (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Business, Management and Accounting › Accounting › Financial Distress and Bankruptcy Prediction
- Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
- Keywords
- SHAP, lime, explainable AI, credit risk, probability of default, model interpretability, black-box models, feature attribution, small and medium-sized enterprises, gradient boosting, ensemble methods
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