Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research

Random Forests

Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538

LIBSVM: A library for support vector machines

Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918

Data Mining: Practical Machine Learning Tools and Techniques

Witten, I. H., Frank, E., & Hall, M. A. (2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,543

Reinforcement Learning: An Introduction

Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,268

UCI Machine Learning Repository

Asuncion, A. (2007) · Medical Entomology and Zoology · Cited by 24,190

The Elements of Statistical Learning

Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162

An introduction to ROC analysis

Fawcett, T. (2006) · Pattern Recognition Letters · doi:10.1016/j.patrec.2005.10.010 · Cited by 21,879

The WEKA data mining software: an update

Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1656274.1656278 · Cited by 17,750

Bagging predictors

Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325

Extreme learning machine: Theory and applications

Huang, G.-B., Zhu, Q.-Y., & Siew, C.-K. (2006) · Neurocomputing · doi:10.1016/j.neucom.2005.12.126 · Cited by 13,285

Sovereign Debt Markets in Light of the Shadow Economy

Markellos, R. N., Psychoyios, D., & SCHNEIDER, F. G. (2012) · European Journal of Operational Research · doi:10.1016/j.ejor.2015.12.039 · Cited by 26

Estimation Procedures of Using Five Alternative Machine Learning Methods for Predicting Credit Card Default

Teng, H.-W., & Lee, M. (2019) · Review of Pacific Basin Financial Markets and Policies · doi:10.1142/s0219091519500218 · Cited by 25

A comparison of classification methods across different data complexity scenarios and datasets

Scholz, M., & Wimmer, T. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.114217 · Cited by 25

Biogeography based optimization for mining rules to assess credit risk

Giri, P. K., De, S. S., Dehuri, S., & Cho, S. (2021) · Intelligent systems in accounting, finance and management/Intelligent systems in accounting, finance & management · doi:10.1002/isaf.1486 · Cited by 25

Comparing Performance of Machine Learning Algorithms for Default Risk Prediction in Peer to Peer Lending

Aleksandrova, Y. (2021) · TEM Journal · doi:10.18421/tem101-16 · Cited by 25

Bayesian Hyperparameter Optimization and Ensemble Learning for Machine Learning Models on Software Effort Estimation

Marco, R., Ahmad, S. S. S., & Ahmad, S. (2022) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2022.0130351 · Cited by 25

Predicting Delinquency on Mortgage Loans: An Exhaustive Parametric Comparison of Machine Learning Techniques

Ali, S. E. A., Rizvi, S. S. H., Lai, F., Ali, R. F., & Jan, A. A. (2021) · International Journal of Industrial Engineering and Management · doi:10.24867/ijiem-2021-1-272 · Cited by 26

Intelligent credit scoring using deep learning methods

Gicić, A., Đonko, D., & Subaşı, A. (2023) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.7637 · Cited by 25

Financial risk management on a neutral atom quantum processor

Leclerc, L., Ortiz-Gutiérrez, L., Grijalva, S., Albrecht, B., Cline, J., Elfving, V. E., Signoles, A., Henriet, L., Bimbo, G. D., Sheikh, U. A., et al. (2023) · Physical Review Research · doi:10.1103/physrevresearch.5.043117 · Cited by 42

Performance Comparison of Machine Learning Platforms

Roy, A., Qureshi, S., Pande, K., Nair, D., Gairola, K., Jain, P., Singh, S., Sharma, K., Jagadale, A., Lin, Y.-Y., et al. (2019) · INFORMS journal on computing · doi:10.1287/ijoc.2018.0825 · Cited by 27

Predicting firm failure in the software industry

Roumani, Y. F., Nwankpa, J. K., & Tanniru, M. R. (2019) · Artificial Intelligence Review · doi:10.1007/s10462-019-09789-2 · Cited by 25

A novel framework for enhancing transparency in credit scoring: Leveraging Shapley values for interpretable credit scorecards

Hlongwane, R., Ramaboa, K. K. K. M., & Mongwe, W. T. (2024) · PLoS ONE · doi:10.1371/journal.pone.0308718 · Cited by 33

Applications of Artificial Intelligence for Optimizing Banking and Financial Operations: A Systematic Literature Review and Future Research Agenda

Singh, R. K., Mishra, R., Kumar, S., & Bag, S. (2025) · Journal of Economic Surveys · doi:10.1111/joes.12693 · Cited by 33

My Advisor, Her AI, and Me: Evidence from a Field Experiment on Human–AI Collaboration and Investment Decisions

Yang, L., Bauer, K., Li, X., & Hinz, O. F. (2025) · Management Science · doi:10.1287/mnsc.2022.03918 · Cited by 39

A Novel GSCI-Based Ensemble Approach for Credit Scoring

Chen, X., Li, S., Xu, X., Meng, F., & Cao, W. (2020) · IEEE Access · doi:10.1109/access.2020.3043937 · Cited by 24

Estimating Corporate Bankruptcy Forecasting Models by Maximizing Discriminatory Power

Charalambous, C., Martzoukos, S. H. S., & Taoushianis, Z. (2020) · Review of Quantitative Finance and Accounting · doi:10.1007/s11156-021-00995-0 · Cited by 24

Know Where to Invest: Platform Risk Evaluation in Online Lending

Wang, Z., Jiang, C., & Zhao, H. (2021) · Information Systems Research · doi:10.1287/isre.2021.1083 · Cited by 24

A comparative study of combining tree‐based feature selection methods and classifiers in personal loan default prediction

Guo, W., & Zhou, Z. Z. (2022) · Journal of Forecasting · doi:10.1002/for.2856 · Cited by 27

Improving the predictive accuracy of the cross-selling of consumer loans using deep learning networks

Boustani, N., Emrouznejad, A., Gholami, R., Despić, O., & Ioannou, A. (2023) · Annals of Operations Research · doi:10.1007/s10479-023-05209-5 · Cited by 28

A kernel-free quadratic surface support vector machine for semi-supervised learning

Yan, X., Bai, Y., Fang, S., & Luo, J. (2016) · Journal of the Operational Research Society · doi:10.1057/jors.2015.89 · Cited by 24

Comparative study of individual and ensemble methods of classification for credit scoring

Singh, P. (2017) · International Conference on Inventive Computing and Informatics (ICICI) · doi:10.1109/icici.2017.8365282 · Cited by 24

A Hybrid Credit Scoring Model Using Neural Networks and Logistic Regression

Munkhdalai, L., Lee, J. Y., & Ryu, K. H. (2019) · Smart innovation, systems and technologies · doi:10.1007/978-981-13-9714-1_27 · Cited by 26

Predicting donation behavior: Acquisition modeling in the nonprofit sector using Facebook data

Schetgen, L., Bogaert, M., & den Poel, D. V. (2020) · Decision Support Systems · doi:10.1016/j.dss.2020.113446 · Cited by 23

A class of categorization methods for credit scoring models

Silva, D. M. B., Pereira, G. H. A., & Magalhães, T. M. (2021) · European Journal of Operational Research · doi:10.1016/j.ejor.2021.04.029 · Cited by 23

Ensemble Learning Methods for Shear Strength Prediction of Fly Ash-Amended Soils with Lignin Reinforcement

Chen, W., Qu, S., Lin, L., Luo, Q., & Wang, T. (2023) · Journal of Materials in Civil Engineering · doi:10.1061/(asce)mt.1943-5533.0004676 · Cited by 24

Responsible Credit Risk Assessment with Machine Learning and Knowledge Acquisition

Guan, C., Suryanto, H., Mahidadia, A., Bain, M., & Compton, P. (2023) · Human-Centric Intelligent Systems · doi:10.1007/s44230-023-00035-1 · Cited by 23

Credit scoring prediction leveraging interpretable ensemble learning

Liu, Y., Huang, F., Ma, L., Zeng, Q., & Shi, J. (2023) · Journal of Forecasting · doi:10.1002/for.3033 · Cited by 27

Credit scoring model: A combination of genetic programming and deep learning

Tran, K. V., Dương, T. L., & Ho, Q. (2016) · Future Technologies Conference · doi:10.1109/ftc.2016.7821603 · Cited by 23

Advanced Neural Network Approach, Its Explanation with LIME for Credit Scoring Application

Munkhdalai, L., Wang, L., Park, H. W., & Ryu, K. H. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-14802-7_35 · Cited by 26

Credit Risk Assessment Model for Small and Micro-Enterprises: The Case of Lithuania

Kanapickienė, R., & Špicas, R. (2019) · Risks · doi:10.3390/risks7020067 · Cited by 23

Hybrid Harmony Search–Artificial Intelligence Models in Credit Scoring

Goh, R. Y., Lee, L. S., Seow, H., & Gopal, K. (2020) · Entropy · doi:10.3390/e22090989 · Cited by 22

Employing Explainable AI to Optimize the Return Target Function of a Loan Portfolio

Gramespacher, T., & Posth, J.-A. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.693022 · Cited by 31

Credit offering strategy and dynamic pricing in the presence of consumer strategic behavior

Zha, Y., Wang, Y., Li, Q., & Yao, W. (2022) · European Journal of Operational Research · doi:10.1016/j.ejor.2022.03.023 · Cited by 22

A Deep Learning Approach for Credit Scoring Using Feature Embedded Transformer

Wang, C., & Xiao, Z. (2022) · Applied Sciences · doi:10.3390/app122110995 · Cited by 29

Prediction of Maximum Tunnel Uplift Caused by Overlying Excavation Using XGBoost Algorithm with Bayesian Optimization

Zhao, H., Wang, Y., Li, X., Guo, P., & Lin, H. (2023) · Applied Sciences · doi:10.3390/app13179726 · Cited by 22

Interactive dynamic diffusion graph convolutional network for traffic flow prediction

Zhang, S., Yu, W., & Zhang, W. (2024) · Information Sciences · doi:10.1016/j.ins.2024.120938 · Cited by 22

Big Data, Artificial Intelligence and Machine Learning: A Transformative Symbiosis in Favour of Financial TechnologyPreprint

Stasinakis, C., & Sermpinis, G. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3663062 · Cited by 22

Comparison of the impact of dimensionality reduction and data splitting on classification performance in credit risk assessment

Bulut, C., & Arslan, E. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10904-1 · Cited by 21

A zero-inflated non default rate regression model for credit scoring data

Louzada, F., Moreira, F., & Júnior, M. R. D. O. (2017) · Communication in Statistics- Theory and Methods · doi:10.1080/03610926.2017.1346803 · Cited by 21

A Wavelet-based Data Analysis to Credit Scoring

Saia, R., Carta, S., & Fenu, G. (2018) · IEEE International Conference on Digital Signal Processing (DSP) · doi:10.1145/3193025.3193039 · Cited by 21

Websites’ data: a new asset for enhancing credit risk modeling

Crosato, L., Domènech, J., & Liberati, C. (2023) · Annals of Operations Research · doi:10.1007/s10479-023-05306-5 · Cited by 21

Deep reinforcement learning based on balanced stratified prioritized experience replay for customer credit scoring in peer-to-peer lending

Wang, Y., Jia, Y., Fan, S., & Xiao, J. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-023-10697-9 · Cited by 21

The best of two worlds: Balancing model strength and comprehensibility in business failure prediction using spline-rule ensembles

Bock, K. W. D. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.07.036 · Cited by 21

Credit Risk Prediction Using Artificial Neural Network Algorithm

Gupta, D. K., & Goyal, S. (2018) · International Journal of Modern Education and Computer Science · doi:10.5815/ijmecs.2018.05.02 · Cited by 21

Robust identification of email tracking: A machine learning approach

Haupt, J., Bender, B., Fabian, B., & Lessmann, S. (2018) · European Journal of Operational Research · doi:10.1016/j.ejor.2018.05.018 · Cited by 21

The Value of Open Banking Data for Application Credit Scoring: Case Study of a Norwegian Bank

Hjelkrem, L. O., de Lange, P. E., & Nesset, E. (2022) · Journal of risk and financial management · doi:10.3390/jrfm15120597 · Cited by 30

Comparing minds and machines: implications for financial stability

Buckmann, M., Haldane, A., & Hüser, A. (2021) · Oxford Review of Economic Policy · doi:10.1093/oxrep/grab017 · Cited by 24

Feature contribution alignment with expert knowledge for artificial intelligence credit scoring

Qadi, A. E., Trocan, M., Díaz-Rodríguez, N., & Frossard, T. (2022) · Signal Image and Video Processing · doi:10.1007/s11760-022-02239-7 · Cited by 20

Internet Financial Credit Scoring Models Based on Deep Forest and Resampling Methods

Zhong, Y. G., & Wang, H. (2023) · IEEE Access · doi:10.1109/access.2023.3239889 · Cited by 20

A recent review on optimisation methods applied to credit scoring models

Kamimura, E. S., Pinto, A. R. F., & Nagano, M. S. (2023) · Journal of Economics Finance and Administrative Science · doi:10.1108/jefas-09-2021-0193 · Cited by 20

Three-stage research framework to assess and predict the financial risk of SMEs based on hybrid method

Xiao, J., Zhang, W., Jiang, X., Yu, L., & Wang, S. (2023) · Decision Support Systems · doi:10.1016/j.dss.2023.114090 · Cited by 20

Investigating the beneficial impact of segmentation-based modelling for credit scoring

Idbenjra, K., Coussement, K., & Caigny, A. D. (2024) · Decision Support Systems · doi:10.1016/j.dss.2024.114170 · Cited by 20

Credit risk assessment using the factorization machine model with feature interactions

Quan, J., & Sun, X. (2024) · Humanities and Social Sciences Communications · doi:10.1057/s41599-024-02700-7 · Cited by 20

Credit scoring: Does XGboost outperform logistic regression?A test on Italian SMEs

Zedda, S. (2024) · Research in International Business and Finance · doi:10.1016/j.ribaf.2024.102397 · Cited by 20

XAIRF-WFP: a novel XAI-based random forest classifier for advanced email spam detection

Bouke, M. A., Alramli, O. I., & Abdullah, A. (2024) · International Journal of Information Security · doi:10.1007/s10207-024-00920-1 · Cited by 23

A systematic credit scoring model based on heterogeneous classifier ensembles

Alaraj, M., & Abbod, M. (2015) · International Conference on Innovations in Intelligent Systems and Applications (INISTA) · doi:10.1109/inista.2015.7276736 · Cited by 20

Decoding Human Behavior with Big Data? Critical, Constructive Input from the Decision Sciences

Katsikopoulos, K. V., & Canellas, M. C. (2022) · AI Magazine · doi:10.1002/aaai.12034 · Cited by 22

Investigating the Performance of Smote for Class Imbalanced Learning: A Case Study of Credit Scoring Datasets

Anis, M., & Ali, M. (2017) · European Scientific Journal ESJ · doi:10.19044/esj.2017.v13n33p340 · Cited by 20

A mixed solution-based high agreement filtering method for class noise detection in binary classification

Samami, M., Akbari, E., Abdar, M., Pławiak, P., Nematzadeh, H., Basiri, M. E., & Makarenkov, V. (2020) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2020.124219 · Cited by 19

A Multi-State Approach to Modelling Intermediate Events and Multiple Mortgage Loan Outcomes

Chamboko, R., & Bravo, J. M. (2020) · Risks · doi:10.3390/risks8020064 · Cited by 19

Profit-Based Model Selection for Customer Retention Using Individual Customer Lifetime Values

Óskarsdóttir, M., Baesens, B., & Vanthienen, J. (2018) · Big Data · doi:10.1089/big.2018.0015 · Cited by 19

An empirical examination of UTAUT model and social network analysis

Xu, Z., Li, Y., & Hao, L. (2019) · Library Hi Tech · doi:10.1108/lht-11-2018-0175 · Cited by 19

A comparison analysis for credit scoring using bagging ensembles

Luo, C. (2018) · Expert Systems · doi:10.1111/exsy.12297 · Cited by 19

Comparing Two Novel Hybrid MRDM Approaches to Consumer Credit Scoring Under Uncertainty and Fuzzy Judgments

Shen, K.-Y., Sakai, H., & Tzeng, G. (2018) · International Journal of Fuzzy Systems · doi:10.1007/s40815-018-0525-0 · Cited by 19

Smart Financial Management System Based on Data Ming and Man‐Machine Management

Lai, M. (2022) · Wireless Communications and Mobile Computing · doi:10.1155/2022/2717982 · Cited by 18

Deep learning meets decision trees: An application of a heterogeneous deep forest approach in credit scoring for online consumer lending

Xia, Y., Guo, X., Li, Y., He, L., & Chen, X. (2022) · Journal of Forecasting · doi:10.1002/for.2891 · Cited by 18

Assessment of liquefaction-induced lateral spread using soft computing approaches

Chen, Z., Chen, Y., Zhang, Y., Liu, X., Xiao, P., & Samui, P. (2022) · Gondwana Research · doi:10.1016/j.gr.2022.08.006 · Cited by 18

Depicting Risk Profile over Time: A Novel Multiperiod Loan Default Prediction Approach

Wang, Z., Jiang, C., & Zhao, H. (2023) · MIS Quarterly · doi:10.25300/misq/2022/17491 · Cited by 18

Geographically Weighted Logistic Regression Applied to Credit Scoring Models

Albuquerque, P. H. M., Medina, F. A. S., & da Silva, A. R. (2017) · Revista Contabilidade & Finanças · doi:10.1590/1808-057x201703760 · Cited by 18

An experimental comparison of classification techniques in debt recoveries scoring: Evidence from South Africa's unsecured lending market

Mushava, J., & Murray, M. P. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.02.030 · Cited by 18

A novel multi-objective particle swarm optimization for comprehensible credit scoring

Guo, Y. F., He, J., Xu, L., & Liu, W. (2018) · Soft Computing · doi:10.1007/s00500-018-3509-y · Cited by 18

Debt rating model based on default identification

Chi, G., & Meng, B. (2018) · Management Decision · doi:10.1108/md-11-2017-1109 · Cited by 18

A Partially Interpretable Adaptive Softmax Regression for Credit Scoring

Munkhdalai, L., Ryu, K. H., Namsrai, O.-E., & Theera‐Umpon, N. (2021) · Applied Sciences · doi:10.3390/app11073227 · Cited by 21

Forecasting financial distress in listed Chinese construction firms: leveraging ensemble learning and non-financial variables

Wang, J., Li, M., Moorhead, M., & Skitmore, M. (2024) · Construction Management and Economics · doi:10.1080/01446193.2024.2403553 · Cited by 19

An Ensemble Classifier Model to Predict Credit Scoring - Comparative Analysis

Parvin, A. S., & Saleena, B. (2020) · IEEE International Symposium on Smart Electronic Systems (iSES) · doi:10.1109/ises50453.2020.00017 · Cited by 20

Meta-Learning Approaches for Recovery Rate Prediction

Roccazzella, F., Gambetti, P., & Vrins, F. D. (2022) · Risks · doi:10.3390/risks10060124 · Cited by 17

Impact of macroeconomic indicators on bankruptcy prediction models: Case of the Portuguese construction sector

Sousa, A., Braga, A. C., & Cunha, J. (2022) · Quantitative Finance and Economics · doi:10.3934/qfe.2022018 · Cited by 17

Cost of Explainability in AI: An Example with Credit Scoring Models

Dessain, J., Bentaleb, N., & Vinas, F. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-44064-9_26 · Cited by 17

A spatial–temporal graph-based AI model for truck loan default prediction using large-scale GPS trajectory data

Chen, L., Ma, S., Li, C., Yang, Y., Wei, W., & Cui, R. (2024) · Transportation Research Part E Logistics and Transportation Review · doi:10.1016/j.tre.2024.103445 · Cited by 20

Modeling credit risk in credit unions using survival analysis

Hassan, M. K., Brodmann, J., Rayfield, B., & Huda, M. (2018) · International Journal of Bank Marketing · doi:10.1108/ijbm-05-2017-0091 · Cited by 24

Would two-stage scoring models alleviate bank exposure to bad debt?

Abdou, H. A., Mitra, S., Fry, J. M., & Elamer, A. A. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.03.028 · Cited by 17

Comparison of Prediction Models Applied in Economic Recession and Expansion

Čámská, D., & Klecka, J. (2020) · Journal of risk and financial management · doi:10.3390/jrfm13030052 · Cited by 17

Credit scoring model based on a novel group feature selection method: The case of Chinese small-sized manufacturing enterprises

Zhang, Z., Chi, G., Colombage, S. R. N., & Zhou, Y. N. (2021) · Journal of the Operational Research Society · doi:10.1080/01605682.2021.1880295 · Cited by 17

The non-linear nature of the cost of comprehensibility

Goethals, S., Martens, D., & Evgeniou, T. (2022) · Journal Of Big Data · doi:10.1186/s40537-022-00579-2 · Cited by 23

The Probability of Default Under IFRS 9: Multi-period Estimation and Macroeconomic Forecast

Vaněk, T., & Hampel, D. (2017) · Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis · doi:10.11118/actaun201765020759 · Cited by 17

An uncertainty-oriented cost-sensitive credit scoring framework with multi-objective feature selection

Wu, Y., Huang, W. W., Tian, Y., Zhu, Q., & Yu, L. (2022) · Electronic Commerce Research and Applications · doi:10.1016/j.elerap.2022.101155 · Cited by 16

A framework of dynamic selection method for user classification in touch-based continuous mobile device authentication

Zaidi, A. Z., Chong, C. Y., Parthiban, R., & Sadiq, A. S. (2022) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2022.103217 · Cited by 17

Machine learning approaches to predict the apparent rate constants for aqueous organic compounds by ferrate

Zheng, S., Guo, W., Lü, H., Si, Q., Liu, B., Wang, H., Zhao, Q., Jia, W., & Yu, T. (2022) · Journal of Environmental Management · doi:10.1016/j.jenvman.2022.116904 · Cited by 16

Alternative Scoring Factors using Non-Financial Data for Credit Decisions in Agricultural Microfinance

Simumba, N., Okami, S., Kodaka, A., & Kohtake, N. (2018) · IEEE International Symposium on Systems Engineering · doi:10.1109/syseng.2018.8544442 · Cited by 16

Banks to Basics! Why Banking Regulation Should Focus on Equity

Durand, P., & Quang, G. L. (2020) · European Journal of Operational Research · doi:10.1016/j.ejor.2021.10.057 · Cited by 16

Cost-sensitive multiple-instance learning method with dynamic transactional data for personal credit scoring

Zhang, W., Xu, W. X., Hao, H., & Zhu, D. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113489 · Cited by 16

Ten-year evolution on credit risk research: a systematic literature review approach and discussion

Assef, F. M., & Steiner, M. T. A. (2020) · Ingeniería e Investigación · doi:10.15446/ing.investig.v40n2.78649 · Cited by 16

Two‐stage hybrid learning techniques for bankruptcy prediction*

Tsai, C. (2020) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.11482 · Cited by 18

Multicriteria interpretability driven deep learning

Repetto, M. (2022) · Annals of Operations Research · doi:10.1007/s10479-022-04692-6 · Cited by 16

A novel extreme gradient boosting algorithm based model for predicting the scour risk around bridge piers: application to French railway bridges

Wang, T., Reiffsteck, P., Chevalier, C., Zhu, Z., Chen, C.-W., & Schmidt, F. (2022) · European Journal of Environmental and Civil engineering · doi:10.1080/19648189.2022.2072957 · Cited by 17

On Class Imbalance Correction for Classification Algorithms in Credit Scoring

Bischl, B., Kühn, T., & Szepannek, G. (2016) · Operations research proceedings · doi:10.1007/978-3-319-28697-6_6 · Cited by 16

An Entropy Based Algorithm for Credit Scoring

Saia, R., & Carta, S. (2016) · Lecture notes in business information processing · doi:10.1007/978-3-319-49944-4_20 · Cited by 16

Dynamic ensemble selection based on hesitant fuzzy multiple criteria decision making

Elmi, J., & Eftekhari, M. (2020) · Soft Computing · doi:10.1007/s00500-020-04668-3 · Cited by 16

Predicting Employee Turnover: Scoping and Benchmarking the State-of-the-Art

Vos, S. D., Bockel-Rickermann, C., Belle, J. V., & Verbeke, W. (2024) · Business & Information Systems Engineering · doi:10.1007/s12599-024-00898-z · Cited by 21

Credit scoring model for fintech lending: An integration of large language models and FocalPoly loss

Xia, Y., Han, Z., Li, Y., & He, L. (2024) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2024.07.005 · Cited by 19

Benchmarking state-of-the-art classification algorithms for credit scoring

Baesens, B., Van Gestel, T., Viaene, S., Stepanova, M., Suykens, J., & Vanthienen, J. (2003) · Journal of the Operational Research Society · doi:10.1057/palgrave.jors.2601545 · Cited by 904

Benchmarking state-of-the-art classification techniques for credit scoring

Martens, D., Baesens, B., Gestel, T. V., & Vanthienen, J. (2005)

A comparative study of state of the art classification algorithms for credit scoring

Baesens, B., Viaene, S., & Vanthienen, J. (2001) · ePrints Soton (University of Southampton)

Comparative study of individual and ensemble methods of classification for credit scoring

Singh, P. (2017) · International Conference on Inventive Computing and Informatics (ICICI) · doi:10.1109/icici.2017.8365282 · Cited by 24

Development of Credit Scoring Model to Determine the Creditworthiness of Borrowers

Ali, A., & Rafi, M. (2014) · Journal of Independent Studies and Research - Computing · doi:10.31645/2014.12.2.2