An Explainable AI framework for credit evaluation and analysis
Open accessIntegrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
An XAI framework integrated with Random Forest algorithms offers clear explanations for loan decisions, enhancing transparency in the lending process.
“Nallakaruppan et al. [110] proposed an XAI framework integrated with Random Forest algorithms, offering clear explanations for loan decisions, thus enhancing transparency in the lending process.”
From Recent Emerging Techniques in Explainable Artificial Intelligence to Enhance the Interpretable and Understanding of AI Models for Human (2025) · cites this paper as [110]
An XAI framework using LIME and SHAP explainers provides local and global insights on various parameters, significantly improving the accuracy and reliability of loan acceptance predictions.
“This XAI framework utilizes LIME and SHAP explainers to provide local and global insights on various parameters, significantly improving the accuracy and reliability of loan acceptance predictions.”
From Recent Emerging Techniques in Explainable Artificial Intelligence to Enhance the Interpretable and Understanding of AI Models for Human (2025) · cites this paper as [110]
Cites
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555 · see citing works
Gauriat, C.-M., Pencolé, Y., Ribot, P., et al. (2024) · HAL · doi:10.4230/oasics.dx.2024.27 · Cited by 12,623 · see citing works
A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures
Yu, Y., Si, X., Hu, C., & Zhang, J. (2019) · Neural Computation · doi:10.1162/neco_a_01199 · Cited by 5,662 · see citing works
XAI—Explainable artificial intelligence
Gunning, D., Stefik, M., Choi, J., Miller, T., Stumpf, S., & Yang, G.-Z. (2019) · Science Robotics · doi:10.1126/scirobotics.aay7120 · Cited by 2,053 · see citing works
pdp: An R Package for Constructing Partial Dependence Plots
Brandon, M. G. (2017) · The R Journal · doi:10.32614/rj-2017-016 · Cited by 1,198 · see citing works
LaValley, M. P. (2008) · Circulation · doi:10.1161/circulationaha.106.682658 · Cited by 1,163 · see citing works
An experimental comparison of classification algorithms for imbalanced credit scoring data sets
Brown, I., & Mues, C. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.09.033 · Cited by 768 · see citing works
Using Neural Network Rule Extraction and Decision Tables for Credit-Risk Evaluation
Baesens, B., Setiono, R., Mues, C., & Vanthienen, J. (2003) · Management Science · doi:10.1287/mnsc.49.3.312.12739 · Cited by 536 · see citing works
A new fuzzy support vector machine to evaluate credit risk
Wang, Y., Wang, S., & Lai, K. K. (2005) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2005.859320 · Cited by 384 · see citing works
SHAP and LIME: An Evaluation of Discriminative Power in Credit Risk
Gramegna, A., & Giudici, P. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.752558 · Cited by 306 · see citing works
Explainable AI in Fintech Risk Management
Bussmann, N., Giudici, P., Marinelli, D., & Papenbrock, J. (2020) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2020.00026 · Cited by 276 · see citing works
A benchmark of machine learning approaches for credit score prediction
Moscato, V., Picariello, A., & Sperlí, G. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113986 · Cited by 274 · see citing works
Fernández, A., Herrera, F., Cordón, Ó., del Jesús, M. J., & Marcelloni, F. (2019) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2018.2881645 · Cited by 234 · see citing works
Shen, F., Zhao, X., Kou, G., & Alsaadi, F. E. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106852 · Cited by 234 · see citing works
IEEE 10th International Conference on Industrial Informatics
(2012) · doi:10.1109/indin19913.2012 · Cited by 229 · see citing works
A Study on Different Deep Learning Algorithms Used in Deep Neural Nets: MLP SOM and DBN
Naskath, J., Sivakamasundari, G., & Begum, A. A. S. (2022) · Wireless Personal Communications · doi:10.1007/s11277-022-10079-4 · Cited by 218 · see citing works
A deep learning approach for credit scoring using credit default swaps
Luo, C., Wu, D. D., & Wu, D. (2016) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2016.12.002 · Cited by 196 · see citing works
Explainability of a Machine Learning Granting Scoring Model in Peer-to-Peer Lending
Garzón, M. J. A., Arroyo, J., Caparrini, A., & Vargas, M. J. S. (2020) · IEEE Access · doi:10.1109/access.2020.2984412 · Cited by 181 · see citing works
Legal requirements on explainability in machine learning
Bibal, A., Lognoul, M., de Streel, A., & Frénay, B. (2021) · Artificial Intelligence and Law · doi:10.1007/s10506-020-09270-4 · Cited by 165 · see citing works
A DBN-based resampling SVM ensemble learning paradigm for credit classification with imbalanced data
Yu, L., Zhou, R., Tang, L., & Chen, R. (2018) · Applied Soft Computing · doi:10.1016/j.asoc.2018.04.049 · Cited by 161 · see citing works
Suthaharan, S. (2015) · CERN Document Server (European Organization for Nuclear Research) · Cited by 156 · see citing works
Credit risk assessment model for Jordanian commercial banks: Neural scoring approach
Bekhet, H. A., & Eletter, S. F. K. (2014) · Review of Development Finance · doi:10.1016/j.rdf.2014.03.002 · Cited by 151 · see citing works
García, V., Marqués, A. I., & Sánchez, J. S. (2018) · Information Fusion · doi:10.1016/j.inffus.2018.07.004 · Cited by 143 · see citing works
Empirical Inference: Festschrift in Honor of Vladimir N. Vapnik
Schlkopf, B., Luo, Z., & Vovk, V. (2014) · Cited by 86 · see citing works
Explainable AI in Credit Risk ManagementPreprint
Misheva, B. H., Hirsa, A., Osterrieder, J., Kulkarni, O., & Lin, S. F. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3795322 · Cited by 86 · see citing works
Explainable models of credit losses
Bastos, J. A., & Matos, S. M. (2021) · European Journal of Operational Research · doi:10.1016/j.ejor.2021.11.009 · Cited by 46 · see citing works
Explainable Machine Learning Models of Consumer Credit Risk
Davis, R., Lo, A. W., Mishra, S., Nourian, A., Singh, M. K., Wu, N., & Zhang, R. (2023) · The Journal of Financial Data Science · doi:10.3905/jfds.2023.1.141 · Cited by 43 · see citing works
A multicriteria decision support tool for modelling bank credit ratings
Gaganis, C., Papadimitri, P., & Tasiou, M. (2020) · Annals of Operations Research · doi:10.1007/s10479-020-03516-9 · Cited by 35 · see citing works
BACS: blockchain and AutoML-based technology for efficient credit scoring classification
Yang, F., Qiao, Y., Qi, Y., Bo, J., & Wang, X. (2022) · Annals of Operations Research · doi:10.1007/s10479-022-04531-8 · Cited by 35 · see citing works
Research on Credit Risk Evaluation of Commercial Banks Based on Artificial Neural Network Model
Hu, Y., & Su, J. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2022.01.148 · Cited by 31 · see citing works
Yu, L., & Zhang, X. (2020) · Finance research letters · doi:10.1016/j.frl.2020.101521 · Cited by 30 · see citing works
Yang, K., Hui, Y., & Lau, R. Y. K. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.116847 · Cited by 30 · see citing works
Explainable AI (XAI) Models Applied to Planning in Financial MarketsPreprint
Benhamou, E., Ohana, J., Saltiel, D., et al. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3862437 · Cited by 18 · see citing works
Xu, S.-J., & Zhang, M. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2022.11.253 · Cited by 8 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Mathew, D. E., Ebem, D. U., Ikegwu, A. C., Ukeoma, P. E., & Dibiaezue, N. F. (2025) · Neural Processing Letters · doi:10.1007/s11063-025-11732-2 · Cited by 168 · see citing works
- Authors
- Nallakaruppan, M. K., Balusamy, B., Shri, M. L., Malathi, V., & Bhattacharyya, S.
- Affiliations
- Vellore Institute of Technology University (IN)
- Sathyabama Institute of Science and Technology (IN)
- VSB - Technical University of Ostrava (CZ)
- Corresponding author
- Bhattacharyya, Siddhartha
- Venue
- Applied Soft Computing
- Publisher
- Elsevier BV
- Published
- 2024-03
- Volume
- 153
- Pages
- 111307
- ISSN
- 1568-4946 (ISSN-L)
- OpenAlex
- W4391364314
- Cited by
- 83
- References
- 36
- Citations by year
- 2026: 42 · 2025: 32 · 2024: 9
- FWCI
- 12.25
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Applied Soft Computing (publishedVersion)
- SSRN Electronic Journal (acceptedVersion)
- DSpace VŠB-TUO (VŠB-TUO) (submittedVersion)
- 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
- explainable AI, creditworthiness assessment, lending, random forest, lime, SHAP, non-fungible tokens, Industry 5.0, credit scoring
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 ›
Are you an author, or do you see a problem with this page? Request a review
