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Explainable Machine Learning in Credit Risk Management

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Explainable AI in Fintech Risk Management

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Explainability of a Machine Learning Granting Scoring Model in Peer-to-Peer Lending

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Explainable artificial intelligence (XAI) in finance: a systematic literature review

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Expl(AI)ned: The Impact of Explainable Artificial Intelligence on Users’ Information Processing

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Post-hoc vs ante-hoc explanations: xAI design guidelines for data scientists

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Interpretable machine learning for imbalanced credit scoring datasets

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Recent Applications of Explainable AI (XAI): A Systematic Literature Review

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Applied machine learning as a driver for polymeric biomaterials design

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Machine learning explainability in nasopharyngeal cancer survival using LIME and SHAP

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An empirical study of handcrafted and dense feature extraction techniques for lung and colon cancer classification from histopathological images

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Reliable water quality prediction and parametric analysis using explainable AI models

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Beyond Predictive Learning Analytics Modelling and onto Explainable Artificial Intelligence with Prescriptive Analytics and ChatGPT

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Increasing transparency in machine learning through bootstrap simulation and shapely additive explanations

Huang, A. A., & Huang, S. Y. (2023) · PLoS ONE · doi:10.1371/journal.pone.0281922 · Cited by 109 · see citing works

Explainable AI for Credit Assessment in Banks

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Prediction of Customer Churn Behavior in the Telecommunication Industry Using Machine Learning Models

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Machine learning in marketing: Recent progress and future research directions

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An Explainable AI framework for credit evaluation and analysis

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Explaining Intrusion Detection-Based Convolutional Neural Networks Using Shapley Additive Explanations (SHAP)

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Explainable AI-based innovative hybrid ensemble model for intrusion detection

Ahmed, U., Jiangbin, Z., Almogren, A., Khan, S., Sadiq, M. T., Altameem, A., & Rehman, A. U. (2024) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-024-00712-x · Cited by 82 · see citing works

Credit Risk Assessment and Financial Decision Support Using Explainable Artificial Intelligence

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Review of Multi-Criteria Decision-Making Methods in Finance Using Explainable Artificial Intelligence

Černevičienė, J., & Kabašinskas, A. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.827584 · Cited by 84 · see citing works

The measurement and early warning of daily financial stability index based on XGBoost and SHAP: Evidence from China

Tan, B., Gan, Z., & Wu, Y. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120375 · Cited by 74 · see citing works

Model-agnostic explainable artificial intelligence methods in finance: a systematic review, recent developments, limitations, challenges and future directions

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Explanation matters: An experimental study on explainable AI

Hamm, P., Klesel, M., Coberger, P., & Wittmann, H. F. (2023) · Electronic Markets · doi:10.1007/s12525-023-00640-9 · Cited by 75 · see citing works

Machine learning-based predictions of fatigue life for multi-principal element alloys

Sai, N. J., Rathore, P., & Chauhan, A. (2022) · Scripta Materialia · doi:10.1016/j.scriptamat.2022.115214 · Cited by 67 · see citing works

SignExplainer: An Explainable AI-Enabled Framework for Sign Language Recognition With Ensemble Learning

Kothadiya, D., Bhatt, C., Rehman, A. U., Alamri, F. S., & Saba, T. (2023) · IEEE Access · doi:10.1109/access.2023.3274851 · Cited by 73 · see citing works

An interpretable machine learning prognostic system for risk stratification in oropharyngeal cancer

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Identifying cardiovascular disease risk in the U.S. population using environmental volatile organic compounds exposure: A machine learning predictive model based on the SHAP methodology

Fu, Q., Wu, Y., Zhu, M. S., Xia, Y., Yu, Q., Liu, Z., Ma, X., & Yang, R. (2024) · Ecotoxicology and Environmental Safety · doi:10.1016/j.ecoenv.2024.117210 · Cited by 63 · see citing works

Using Machine Learning Methods to Forecast Air Quality: A Case Study in Macao

Lei, T. M. T., Siu, S. W. I., Monjardino, J., Mendes, L., & Ferreira, F. C. (2022) · Atmosphere · doi:10.3390/atmos13091412 · Cited by 58 · see citing works

Interpretable and explainable predictive machine learning models for data-driven protein engineering

Medina-Ortiz, D., Khalifeh, A., Anvari-Kazemabad, H., & Davari, M. D. (2024) · Biotechnology Advances · doi:10.1016/j.biotechadv.2024.108495 · Cited by 54 · see citing works

Explainable artificial intelligence to improve the resilience of perishable product supply chains by leveraging customer characteristics

Jauhar, S. K., Harinath, S., Krishnaswamy, V., & Paul, S. K. (2024) · Annals of Operations Research · doi:10.1007/s10479-024-06348-z · Cited by 45 · see citing works

Enhancing transparency and fairness in automated credit decisions: an explainable novel hybrid machine learning approach

Nwafor, C. N., Nwafor, O., & Brahma, S. (2024) · Scientific Reports · doi:10.1038/s41598-024-75026-8 · Cited by 57 · see citing works

An Explainable Artificial Intelligence Framework for the Predictive Analysis of Hypo and Hyper Thyroidism Using Machine Learning Algorithms

Hossain, M. B., Shama, A., Adhikary, A., Raha, A. D., Uddin, K. M. A., Hossain, M. A., Islam, I., Murad, S. A., Munir, M. S., & Bairagi, A. K. (2023) · Human-Centric Intelligent Systems · doi:10.1007/s44230-023-00027-1 · Cited by 45 · see citing works

Predictive modeling based on artificial neural networks for membrane fouling in a large pilot-scale anaerobic membrane bioreactor for treating real municipal wastewater

Wang, T., & Li, Y. (2023) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2023.169164 · Cited by 41 · see citing works

C-SHAP: A Hybrid Method for Fast and Efficient Interpretability

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Explainable inflation forecasts by machine learning models

Aras, S., & Lisboa, P. J. G. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117982 · Cited by 44 · see citing works

Determinants of non-performing loans: An explainable ensemble and deep neural network approach

Nwafor, C. N., & Nwafor, O. (2023) · Finance research letters · doi:10.1016/j.frl.2023.104084 · Cited by 38 · see citing works

Shapley Feature Selection

Gramegna, A., & Giudici, P. (2022) · FinTech · doi:10.3390/fintech1010006 · Cited by 30 · see citing works

Systemic risk prediction using machine learning: Does network connectedness help prediction?

Wang, G., Chen, Y., Zhu, Y., & Xie, C. (2024) · International Review of Financial Analysis · doi:10.1016/j.irfa.2024.103147 · Cited by 31 · see citing works

Representation Learning with a Variational Autoencoder for Predicting Nitrogen Requirement in Rice

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A comprehensive systematic review of intrusion detection systems: emerging techniques, challenges, and future research directions

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From words to insights: Text analysis in business research

Herhausen, D., Ludwig, S., Abedin, E., Haque, N. U., & Jong, D. D. (2025) · Journal of Business Research · doi:10.1016/j.jbusres.2025.115491 · Cited by 34 · see citing works

An analytical approach to credit risk assessment using machine learning models

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Measuring responsible artificial intelligence (RAI) in banking: a valid and reliable instrument

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Enhancing mineral prospectivity mapping with geospatial artificial intelligence: A geographically neural network-weighted logistic regression approach

Wang, L., Yang, J., Wu, S., Hu, L., Ge, Y., & Du, Z. (2024) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2024.103746 · Cited by 24 · see citing works

Explainable Machine Learning Prediction for the Academic Performance of Deaf Scholars

Raji, N. R., Kumar, R. M. S., & Biji, C. L. (2024) · IEEE Access · doi:10.1109/access.2024.3363634 · Cited by 23 · see citing works

Enhancing Alzheimer's disease detection: An explainable machine learning approach with ensemble techniques

Mahamud, E., Assaduzzaman, M., Islam, J., Fahad, N., Hossen, M. J., & Ramanathan, T. T. (2025) · Intelligence-Based Medicine · doi:10.1016/j.ibmed.2025.100240 · Cited by 24 · see citing works

Enhancing ML-based anomaly detection in data management for security through integration of IoT, cloud, and edge computing

Baimukhanov, S., Ali, H., & Yazıcı, A. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128700 · Cited by 23 · see citing works

Stable local interpretable model-agnostic explanations based on a variational autoencoder

Xiang, X.-Y., Yu, H., Wang, Y., & Wang, G. (2023) · Applied intelligence (Boston) · doi:10.1007/s10489-023-04942-5 · Cited by 25 · see citing works

Evaluating Retinal Disease Diagnosis with an Interpretable Lightweight CNN Model Resistant to Adversarial Attacks

Bhandari, M., Shahi, T. B., & Neupane, A. (2023) · Journal of Imaging · doi:10.3390/jimaging9100219 · Cited by 22 · see citing works

Multiparametric MRI-Based Interpretable Radiomics Machine Learning Model Differentiates Medulloblastoma and Ependymoma in Children: A Two-Center Study

Yimit, Y., Yasin, P., Tuersun, A., Wang, J., Wang, X., Huang, C., Abudoubari, S., Chen, X., Ibrahim, I., Nijiati, P., et al. (2024) · Academic Radiology · doi:10.1016/j.acra.2024.02.040 · Cited by 22 · see citing works

A multi-objective optimization design to generate surrogate machine learning models in explainable artificial intelligence applications

Monteiro, W. R., & Reynoso-Meza, G. (2023) · EURO Journal on Decision Processes · doi:10.1016/j.ejdp.2023.100040 · Cited by 21 · see citing works

A systematic literature review on applications of explainable artificial intelligence in the financial sector

Çil, A. E., & Yıldız, K. (2025) · Internet of Things · doi:10.1016/j.iot.2025.101696 · Cited by 27 · see citing works

Deep learning based approaches to enhance energy efficiency in autonomous driving systems

Kwak, J., Lee, Y., Choi, M., & Lee, S. (2024) · Energy · doi:10.1016/j.energy.2024.132625 · Cited by 20 · see citing works

Unveiling Key Predictors for Early Heart Attack Detection using Machine Learning and Explainable AI Technique with LIME

Paudel, P., Karna, S. K., Saud, R., Regmi, L., Thapa, T. B., & Bhandari, M. (2023) · International Conference on Networking, Systems and Security (NSysS) · doi:10.1145/3629188.3629193 · Cited by 20 · see citing works

Development of a machine learning-based model for the prediction and progression of diabetic kidney disease: A single centred retrospective study

Nayak, S., Amin, A., Reghunath, S. R., Thunga, G., Acharya, U. D., Shivashankara, K. N., Attur, R. P., & Acharya, L. D. (2024) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2024.105546 · Cited by 22 · see citing works

Defect diagnosis in power transformer using explainable AI: A semi-supervised learning-based Partial discharge pattern approach

Basharan, V., & Manimala, R. (2025) · Electric Power Systems Research · doi:10.1016/j.epsr.2025.111860 · Cited by 19 · see citing works

Machine Learning-Based Predictions of Porosity during Cold Spray Deposition of High Entropy Alloy Coatings

Sharma, D., Boruah, D., Bakir, A. A., Ameen, A., & Paul, S. (2024) · Coatings · doi:10.3390/coatings14040404 · Cited by 19 · see citing works

Explainable machine learning to predict the cost of capital

Bussmann, N., Giudici, P., Tanda, A., & Yu, E. (2025) · Frontiers Artif. Intell. · doi:10.3389/frai.2025.1578190 · Cited by 16 · see citing works

The role of artificial intelligence algorithms in information systems research: a conceptual overview and avenues for research

Bendig, D., & Bräunche, A. (2024) · Management Review Quarterly · doi:10.1007/s11301-024-00451-y · Cited by 22 · see citing works

Explainable Artificial Intelligence in Deep Learning Neural Nets-Based Digital Images Analysis

Averkin, A. N., Volkov, E. N., & Yarushev, S. (2024) · Journal of Computer and Systems Sciences International · doi:10.1134/s1064230724700138 · Cited by 17 · see citing works

Towards clinical prediction with transparency: An explainable AI approach to survival modelling in residential aged care

Sušnjak, T., & Griffin, E. (2025) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2025.108653 · Cited by 17 · see citing works

Comparative analysis of boosting algorithms for predicting personal default

Nhat, N. M., & Ngo, D. (2025) · Cogent Economics & Finance · doi:10.1080/23322039.2025.2465971 · Cited by 17 · see citing works

From YouTube to the brain: Transfer learning can improve brain-imaging predictions with deep learning

Malik, N., & Bzdok, D. (2022) · Neural Networks · doi:10.1016/j.neunet.2022.06.014 · Cited by 17 · see citing works

Evaluation of Cost-Sensitive Learning Models in Forecasting Business Failure of Capital Market Firms

Peykani, P., Peymany, M., Tănăsescu, C. R., Sargolzaei, M., & Kamyabfar, H. (2025) · Mathematics · doi:10.3390/math13030368 · Cited by 18 · see citing works

Impulsivity Classification Using EEG Power and Explainable Machine Learning

Hüpen, P., Kumar, H., Shymanskaya, A., Swaminathan, R., & Habel, U. (2022) · International Journal of Neural Systems · doi:10.1142/s0129065723500065 · Cited by 16 · see citing works

Forecasting patient flows with pandemic induced concept drift using explainable machine learning

Sušnjak, T., & Maddigan, P. (2023) · EPJ Data Science · doi:10.1140/epjds/s13688-023-00387-5 · Cited by 16 · see citing works

Machine learning-based predictions of yield strength for neutron-irradiated ferritic/martensitic steels

Sai, N. J., Rathore, P., Sridharan, K., & Chauhan, A. (2023) · Fusion Engineering and Design · doi:10.1016/j.fusengdes.2023.113964 · Cited by 16 · see citing works

Understanding explainability and interpretability for risk science applications

Thekdi, S., & Aven, T. (2024) · Safety Science · doi:10.1016/j.ssci.2024.106566 · Cited by 16 · see citing works

Development and characterization of novel wood-based composite materials for solar-powered atmospheric water harvesting: A machine intelligence supported approach

Zhang, X., Xu, Y., Han, S., Weng, X., Che, W., Gu, K., Guan, Y., Yu, Y., Hao, D., & Hou, J. (2025) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2025.145061 · Cited by 15 · see citing works

Explainable AI in early autism detection: a literature review of interpretable machine learning approaches

Agrawal, R., & Agrawal, R. (2025) · Discover Mental Health · doi:10.1007/s44192-025-00232-3 · Cited by 20 · see citing works

Novel quantitative electroencephalogram feature image adapted for deep learning: Verification through classification of Alzheimer’s disease dementia

Jeong, T., Park, U., & Kang, S. W. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.1033379 · Cited by 15 · see citing works

Stress management with HRV following AI, semantic ontology, genetic algorithm and tree explainer

Chatterjee, A., Riegler, M. A., Ganesh, K., & Halvorsen, P. (2025) · Scientific Reports · doi:10.1038/s41598-025-87510-w · Cited by 15 · see citing works

A Fuzzy-XAI Framework for Customer Segmentation and Risk Detection: Integrating RFM, 2-Tuple Modeling, and Strategic Scoring

Díaz, G. M. (2025) · Mathematics · doi:10.3390/math13132141 · Cited by 15 · see citing works

Explainable Machine Learning for Phishing Site Detection: A High‐Efficiency Approach Using Boosting Models and SHAP

Uddin, K. M. M., Biswas, N., Rikta, S. T., Nur-A-Alam, M., & Mostafiz, R. (2025) · The Journal of Engineering · doi:10.1049/tje2.70110 · Cited by 14 · see citing works

Prediction of juvenile crime in Bangladesh due to drug addiction using machine learning and explainable AI techniques

Nesa, M., Shaha, T. R., & Yoon, Y. (2022) · Journal of Computational Social Science · doi:10.1007/s42001-022-00175-7 · Cited by 14 · see citing works

A machine learning-based model for predicting distant metastasis in patients with rectal cancer

Qiu, B., Shen, Z., Wu, S., Qin, X., Yang, D., & Wang, Q. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1235121 · Cited by 14 · see citing works

Considerations on the regulation of AI systems in the financial sector by the AI Act

Mazzini, G., & Bagni, F. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1277544 · Cited by 14 · see citing works

“Are Your Explanations Reliable?” Investigating the Stability of LIME in Explaining Text Classifiers by Marrying XAI and Adversarial Attack

Burger, C., Chen, L., & Le, T. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.792 · Cited by 15 · see citing works

Enhanced streamflow prediction using SWAT’s influential parameters: a comparative analysis of PCA-MLR and XGBoost models

Priya, R. Y., & Manjula, R. (2023) · Earth Science Informatics · doi:10.1007/s12145-023-01139-9 · Cited by 13 · see citing works

Analyzing the Impact of Storm ‘Daniel’ and Subsequent Flooding on Thessaly’s Soil Chemistry through Causal Inference

Iatrou, M., Tziouvalekas, M., Tsitouras, A., Evangelou, E., Noulas, C., Vlachostergios, D. Ν., Aschonitis, V. G., Arampatzis, G., Metaxa, I., Karydas, C. G., et al. (2024) · Agriculture · doi:10.3390/agriculture14040549 · Cited by 13 · see citing works

The determinants of voluntary disclosure: Integration of eXtreme gradient boost (XGBoost) and explainable artificial intelligence (XAI) techniques

Lu, Y.-H., & Lin, Y.-C. (2024) · International Review of Financial Analysis · doi:10.1016/j.irfa.2024.103577 · Cited by 17 · see citing works

Evaluation of Local Interpretable Model-Agnostic Explanation and Shapley Additive Explanation for Chronic Heart Disease Detection

Admassu, T. (2022) · Proceedings of Engineering and Technology Innovation · doi:10.46604/peti.2023.10101 · Cited by 13 · see citing works

Knowing (Not) to Know: Explainable Artificial Intelligence and Human Metacognition

von Zahn, M., Liebich, L., Jussupow, E., Hinz, O. F., & Bauer, K. (2025) · Information Systems Research · doi:10.1287/isre.2024.1431 · Cited by 14 · see citing works

Enhancing winter road maintenance with explainable AI: SHAP analysis for interpreting machine learning models in road friction estimation

Ding, X., & Kwon, T. J. (2024) · Canadian Journal of Civil Engineering · doi:10.1139/cjce-2023-0410 · Cited by 12 · see citing works

Exploring the interrelationships between composition, rheology, and compressive strength of self-compacting concrete: An exploration of explainable boosting algorithms

Wahab, S., Salami, B. A., AlAteah, A. H., Al-Tholaia, M. M. H., & Alahmari, T. S. (2024) · Case Studies in Construction Materials · doi:10.1016/j.cscm.2024.e03084 · Cited by 12 · see citing works

Personal bankruptcy prediction using machine learning techniques

Brygała, M., & Korol, T. (2024) · Economics and Business Review/The Poznań University of Economics Review · doi:10.18559/ebr.2024.2.1149 · Cited by 12 · see citing works

Development and application of explainable artificial intelligence using machine learning classification for long-term facial nerve function after vestibular schwannoma surgery

Przepiórka, Ł., Kujawski, S., Wójtowicz, K., Maj, E., Marchel, A., & Kunert, P. (2024) · Journal of Neuro-Oncology · doi:10.1007/s11060-024-04844-7 · Cited by 12 · see citing works

Explainability of convolutional neural networks for damage diagnosis using transmissibility functions

Parziale, M., Silva, P. H., Giglio, M., & Cadini, F. (2024) · Structures · doi:10.1016/j.istruc.2024.107583 · Cited by 13 · see citing works

Expl(Ai)Ned: The Impact of Explainable Artificial Intelligence on Cognitive ProcessesPreprint

Bauer, K., von Zahn, M., & Hinz, O. F. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3872711 · Cited by 12 · see citing works

Heart disease prediction with a feature-sensitized interpretable framework for the Internet of Medical Things sensors

Kailasanathan, N., Ezhilarasan, G., Selvarajan, S., Dhanaraj, R. K., Pamucar, D., & Shankar, N. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1612915 · Cited by 16 · see citing works

AI-enabled IoT Applications: Towards a Transparent Governance Framework

Fares, N. Y., Nedeljkovic, D., & Jammal, M. (2023) · IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT) · doi:10.1109/gcaiot61060.2023.10385106 · Cited by 11 · see citing works

Decoding the Recommender System: A Comprehensive Guide to Explainable AI in E-commerce

Sahu, G., & Gaur, L. (2024) · Studies in computational intelligence · doi:10.1007/978-3-031-55615-9_3 · Cited by 13 · see citing works

Explainable machine learning for financial risk management: two practical use cases

Famà, A., Myftiu, J., Pagnottoni, P., & Spelta, A. (2024) · Statistics · doi:10.1080/02331888.2024.2401078 · Cited by 16 · see citing works

Guiding computationally intensive theory development with explainable artificial intelligence: The case of shapley additive explanations

Stoffels, D., Faltermaier, S., Strunk, K. S., & Fiedler, M. (2024) · Journal of Information Technology · doi:10.1177/02683962241289597 · Cited by 11 · see citing works

Assessment of eco-driving strategies on carbon emissions for hybrid vehicles through portable emissions measurement systems

Li, B., Chen, Z., Zhu, X.-H., Zhang, Z., Peng, Z., Zhao, H., & He, H.-D. (2024) · Atmospheric Pollution Research · doi:10.1016/j.apr.2024.102365 · Cited by 11 · see citing works

Developing guidelines for functionally-grounded evaluation of explainable artificial intelligence using tabular data

Velmurugan, M., Ouyang, C., Xu, Y., Sindhgatta, R., Wickramanayake, B., & Moreira, C. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109772 · Cited by 11 · see citing works

TransCORALNet: A two-stream transformer CORAL networks for supply chain credit assessment cold start

Shi, J., Siebes, A. P. J. M., & Mehrkanoon, S. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.127581 · Cited by 11 · see citing works

MRI image based Ensemble Voting Classifier for Alzheimer's Disease Classification with Explainable AI Technique

Rashmi, U., Singh, T., & Ambesange, S. (2023) · IEEE International Conference for Convergence in Technology (I2CT) · doi:10.1109/i2ct57861.2023.10126269 · Cited by 11 · see citing works

Interpreting direct sales’ demand forecasts using SHAP values

Arboleda-Florez, M., & Castro-Zuluaga, C. (2023) · Production · doi:10.1590/0103-6513.20220035 · Cited by 12 · see citing works

Explainable prediction of node labels in multilayer networks: a case study of turnover prediction in organizations

Gadár, L., & Abonyi, J. (2024) · Scientific Reports · doi:10.1038/s41598-024-59690-4 · Cited by 10 · see citing works

Towards Interpretable Credit Risk Assessment: A Comparative Study of XAI Techniques with Regulatory Compliance

Chavare, C. R. D. C. D. (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.20258383

Explainable Credit Risk Assessment: Comparing Logistic Regression, XGBoost, and LightGBM with SHAP and LIME Analysis

Imanova, H. K. D. (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.20484846

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

Biecek, P., Chlebuś, M., Gajda, J., Gosiewska, A., Kozak, A., Ogonowski, D., Sztachelski, J., & Wojewnik, P. (2021) · RePEc: Research Papers in Economics · doi:10.48550/arxiv.2104.06735 · Cited by 10 · see citing works

Benchmarking Explainability Methods for SME Credit Risk Models in Emerging Markets

Chowdhury, S., & Das, A. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-32729-1_33

Explainable AI Using h2o AutoML and Robustness Check in Credit Risk Management

Sikha, S., & Vijayakumar, A. (2023) · International Conference on Intelligent Computing and Control for Engineering and Business Systems (ICCEBS) · doi:10.1109/iccebs58601.2023.10449100 · Cited by 2 · see citing works