Benchmarking Gradient Boosting and Explainable AI for Credit Default Prediction on Imbalanced Financial Data: A Leakage-Safe Multi-Seed Evaluation with SHAP and LIME
- DOI
- 10.21203/rs.3.rs-10794000/v1
- Published
- 2026-09-03
- Container
- Not recorded
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
Credibility signals
uncertain Score 60/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
Show all credibility signals
- supportingDOI registered: A matching record was returned by Crossref.
- supportingDOI resolves: A matching record was returned by Crossref.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredOpen access status: Not checked or no result supplied; no credibility inference made.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- cautionPublication version: Identified as a preprint; peer review and later versions may change the record.
- supportingMetadata completeness: All 6 scored descriptive metadata groups are present.
Cite this work
BibTeX
@article{allodium:10.21203/rs.3.rs-10794000/v1,
title = {Benchmarking Gradient Boosting and Explainable AI for Credit Default Prediction on Imbalanced Financial Data: A Leakage-Safe Multi-Seed Evaluation with SHAP and LIME},
author = {Manpreet Singh and Rohith Reddy Bellibatlu and Yash Jajoo and Muhammad Zeeshan and Rathan Ramachandra and Akshatha Srikantha and Rahul Joshi},
year = {2026},
doi = {10.21203/rs.3.rs-10794000/v1},
url = {https://doi.org/10.21203/rs.3.rs-10794000/v1}
}RIS
TY - JOUR TI - Benchmarking Gradient Boosting and Explainable AI for Credit Default Prediction on Imbalanced Financial Data: A Leakage-Safe Multi-Seed Evaluation with SHAP and LIME AU - Manpreet Singh AU - Rohith Reddy Bellibatlu AU - Yash Jajoo AU - Muhammad Zeeshan AU - Rathan Ramachandra AU - Akshatha Srikantha AU - Rahul Joshi PY - 2026 DO - 10.21203/rs.3.rs-10794000/v1 UR - https://doi.org/10.21203/rs.3.rs-10794000/v1 ER -
APA
Singh, M., Bellibatlu, R. R., Jajoo, Y., Zeeshan, M., Ramachandra, R., Srikantha, A., & Joshi, R. (2026). Benchmarking Gradient Boosting and Explainable AI for Credit Default Prediction on Imbalanced Financial Data: A Leakage-Safe Multi-Seed Evaluation with SHAP and LIME. https://doi.org/10.21203/rs.3.rs-10794000/v1
Source records
- crossref · retrieved 2026-11-08T18:14:54.665Z