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Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research
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“Many benchmarking studies have illustrated that complex analytical techniques only provide marginal performance gains on structured, tabular data sets as frequently encountered in common classification tasks such as fraud detection, credit scoring and marketing analytics (Baesens et al., 2003; Lessmann et al., 2015).”
From Data engineering for fraud detection (2021) · cites this paper as Lessmann et al., 2015
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“This result is unsurprising and is fully consistent with the results of benchmarking experiments performed across various business domains and reported in the literature [2, 17].”
From Why you should stop predicting customer churn and start using uplift models (2021) · cites this paper as [2]
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“One of the explanations for their popularity for time series forecasting (cf. other advanced techniques such as bagged or boosted decision trees), is that they are better-suited to handle continuous covariates (Lessmann, Baesens, Seow, & Thomas, 2015), which occur frequently in financial time series forecasting (e.g., prices, price differences or technical indicators).”
From Bridging the divide in financial market forecasting: machine learners vs. financial economists (2016) · cites this paper as Lessmann, Baesens, Seow, & Thomas, 2015
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“We would also like to note that these complex models not always significantly outperform simple analytical models such as logistic regression (Baesens et al., 2003; Lessmann et al., 2015)”
From Data engineering for fraud detection (2021) · cites this paper as Lessmann et al., 2015
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Metadata
- Authors
- Lessmann, S., Baesens, B., Seow, H.-V., & Thomas, L. C.
- Affiliations
- Humboldt-Universität zu Berlin (DE)
- University of Southampton (GB)
- University of Nottingham Malaysia Campus (MY)
- Corresponding author
- Lessmann, Stefan
- Venue
- European Journal of Operational Research
- Publisher
- Elsevier BV
- Published
- 2015-11
- Volume
- 247
- Issue
- 1
- Pages
- 124-136
- ISSN
- 0377-2217 (ISSN-L)
- OpenAlex
- W2131816657
- Cited by
- 1,284
- References
- 170
- Citations by year
- 2026: 218 · 2025: 193 · 2024: 109 · 2023: 129 · 2022: 121 · 2021: 134 · 2020: 109 · 2019: 112 · 2018: 73 · 2017: 48 · 2016: 29 · 2015: 7
- FWCI
- 230.52
- Type
- journal article
- Language
- en
- Open access
- green
- lirias.kuleuven.be
- Licence
- Locations
- European Journal of Operational Research (publishedVersion)
- ePrints Soton (University of Southampton) (acceptedVersion)
- Lirias (submittedVersion, other-oa)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Accounting › Financial Distress and Bankruptcy Prediction
- Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
- Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
- Keywords
- credit scoring, machine learning classifiers, benchmarking, classifier comparison, credit risk assessment
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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