A recent review on optimisation methods applied to credit scoring models


  • Elias Shohei Kamimura Production Engineering Department, University of Araraquara, Araraquara, Brazil
  • Anderson Rogerio Faia Pinto Production Engineering Department, University of Araraquara, Araraquara, Brazil
  • Marcelo Seido Nagano Production Engineering Department, Sao Carlos School of Engineering, University of Sao Paulo, Sao Carlos, Brazil


Credit scoring, Literature review, Risk management, Optimization methods



This paper aims to present a literature review of the most recent optimisation methods applied to Credit Scoring Models (CSMs).


The research methodology employed technical procedures based on bibliographic and exploratory analyses. A traditional investigation was carried out using the Scopus, ScienceDirect and Web of Science databases. The papers selection and classification took place in three steps considering only studies in English language and published in electronic journals (from 2008 to 2022). The investigation led up to the selection of 46 publications (10 presenting literature reviews and 36 proposing CSMs).


The findings showed that CSMs are usually formulated using Financial Analysis, Machine Learning, Statistical Techniques, Operational Research and Data Mining Algorithms. The main databases used by the researchers were banks and the University of California, Irvine. The analyses identified 48 methods used by CSMs, the main ones being: Logistic Regression (13%), Naive Bayes (10%) and Artificial Neural Networks (7%). The authors conclude that advances in credit score studies will require new hybrid approaches capable of integrating Big Data and Deep Learning algorithms into CSMs. These algorithms should have practical issues considered consider practical issues for improving the level of adaptation and performance demanded for the CSMs.

Practical implications

The results of this study might provide considerable practical implications for the application of CSMs. As it was aimed to demonstrate the application of optimisation methods, it is highly considerable that legal and ethical issues should be better adapted to CSMs. It is also suggested improvement of studies focused on micro and small companies for sales in instalment plans and commercial credit through the improvement or new CSMs.


The economic reality surrounding credit granting has made risk management a complex decision-making issue increasingly supported by CSMs. Therefore, this paper satisfies an important gap in the literature to present an analysis of recent advances in optimisation methods applied to CSMs. The main contribution of this paper consists of presenting the evolution of the state of the art and future trends in studies aimed at proposing better CSMs.

DOI: https://doi.org/10.1108/JEFAS-09-2021-0193


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Abdou, A.J. and Pointon, H.A. (2011), “Intelligent systems in accounting, finance and management”, Intelligent Systems in Accounting, Finance and Management, Vol. 16 Nos 1-2, pp. 21-31, available at: https://onlinelibrary.wiley.com/doi/10.1002/isaf.325

Altman, E.I. (1968), “American finance association”, The Journal of Finance, Vol. 29 No. 1, pp. 312-312, available at: https://onlinelibrary.wiley.com/doi/10.1111/j.1540-6261.1968.tb00843.x

Andriosopoulos, D., Doumpos, M., Pardalos, P.M. and Zopounidis, C. (2019), “Computational approaches and data analytics in financial services: a literature review”, Journal of the Operational Research Society, Vol. 70 No. 10, pp. 1581-1599, Taylor & Francis, available at: https://www.tandfonline.com/doi/full/10.1080/01605682.2019.1595193

Antonakis, A.C. and Sfakianakis, M.E. (2009), “Assessing Naïve Bayes as a method for screening credit applicants”, Journal of Applied Statistics, Vol. 36 No. 5, pp. 537-545, doi: 10.1080/02664760802554263.

Ashofteh, A. and Bravo, J.M. (2021), “A conservative approach for online credit scoring”, Expert Systems with Applications, Vol. 176, 114835, doi: 10.1016/j.eswa.2021.114835.

Baesens, B., Setiono, R., Mues, C. and Vanthienen, J. (2003), “Using neural network rule extraction and decision tables for credit-risk evaluation”, Management Science, Vol. 49 No. 3, pp. 312-329, doi: 10.1287/mnsc.49.3.312.12739.

Bastani, K., Asgari, E. and Namavari, H. (2019), “Wide and deep learning for peer-to-peer lending”, Expert Systems with Applications, Vol. 134, pp. 209-224, Elsevier, doi: 10.1016/j.eswa.2019.05.042.

Bravo, C., Maldonado, S. and Weber, R. (2013), “Granting and managing loans for micro-entrepreneurs: new developments and practical experiences”, European Journal of Operational Research, Vol. 227 No. 2, pp. 358-366, doi: 10.1016/j.ejor.2012.10.040.

Breeden, J.L. (2021), “Survey of machine learning in credit risk”, SSRN Electronic Journal, Vol. 17 No. 3, pp. 1-60, doi: 10.2139/ssrn.3616342.

Capon, N. (1982), “Credit scoring systems: a critical analysis”, Journal of Marketing, Vol. 46, pp. 82-91, No. Spring, doi: 10.2307/3203343.

Carta, S., Ferreira, A., Reforgiato Recupero, D. and Saia, R. (2021), “Credit scoring by leveraging an ensemble stochastic criterion in a transformed feature space”, Progress in Artificial Intelligence, Vol. 10 No. 4, pp. 417-432, doi: 10.1007/s13748-021-00246-2.

Chen, N., Ribeiro, B. and Chen, A. (2016), “Financial credit risk assessment: a recent review”, Artificial Intelligence Review, Vol. 45 No. 1, pp. 1-23, doi: 10.1007/s10462-015-9434-x.

Çiǧşar, B. and Ünal, D. (2019), “Comparison of data mining classification algorithms determining the default risk”, Scientific Programming, Vol. 2019 No. 8706505, pp. 1-9, doi: 10.1155/2019/8706505.

Dastile, X. and Celik, T. (2021), “Making deep learning-based predictions for credit scoring explainable”, IEEE Access, Vol. 9, pp. 50426-50440, doi: 10.1109/ACCESS.2021.3068854.

Djeundje, V.B., Crook, J., Calabrese, R. and Hamid, M. (2021), “Enhancing credit scoring with alternative data”, Expert Systems with Applications, Vol. 163, 113766, doi: 10.1016/j.eswa.2020.113766.

Donthu, N., Kumar, S., Mukherjee, D., Pandey, N. and Lim, W.M. (2021), “How to conduct a bibliometric analysis: an overview and guidelines”, Journal of Business Research, Vol. 133, pp. 285-296, doi: 10.1016/j.jbusres.2021.04.070.

Doumpos, M., Lemonakis, C., Niklis, D. and Zopounidis, C. (2018), Analytical Techniques in the Assessment of Credit Risk: an Overview of Methodologies and Applications, 1st ed., Springer, Switzerland, AG.

Durand, D. (1941), Risk Elements in Consumer Instalment Financing, National Bureau of Economy Research, Cambridge, MA, No. dura41-1, pp. 189-201, available at: https://www.nber.org/books-and-chapters/risk-elements-consumer-instalment-financing

Eisenbeis, R.A. (1978), “Problems in applying discriminant analysis in credit scoring models”, Journal of Banking and Finance, Vol. 2 No. 3, pp. 205-219, doi: 10.1016/0378-4266(78)90012-2.

Finlay, S. (2009), “Are we modelling the right thing? The impact of incorrect problem specification in credit scoring”, Expert Systems with Applications, Vol. 36 No. 5, pp. 9065-9071, doi: 10.1016/j.eswa.2008.12.016.

Finlay, S. (2010), “Credit scoring for profitability objectives”, European Journal of Operational Research, Vol. 202 No. 2, pp. 528-537, doi: 10.1016/j.ejor.2009.05.025.

Goh, R.Y. and Lee, L.S. (2019), “Credit scoring: a review on support vector machines and metaheuristic approaches”, Advances in Operations Research, Vol. 2019 No. 8706505, pp. 1-31, doi: 10.1155/2019/1974794.

Gunnarsson, B.R., vanden Broucke, S., Baesens, B., Óskarsdóttir, M. and Lemahieu, W. (2021), “Deep learning for credit scoring: do or don't?”, European Journal of Operational Research, Vol. 295 No. 1, pp. 292-305, doi: 10.1016/j.ejor.2021.03.006.

Ince, H. and Aktan, B. (2009), “A comparison of data mining techniques for credit scoring in banking: a managerial perspective”, Journal of Business Economics and Management, Vol. 10 No. 3, pp. 233-240, doi: 10.3846/1611-1699.2009.10.233-240.

Kang, Y., Jia, N., Cui, R. and Deng, J. (2021), “A graph-based semi-supervised reject inference framework considering imbalanced data distribution for consumer credit scoring”, Applied Soft Computing, Vol. 105, 107259, doi: 10.1016/j.asoc.2021.107259.

Kozeny, V. (2015), “Genetic algorithms for credit scoring: alternative fitness function performance comparison”, Expert Systems with Applications, Vol. 42 No. 6, pp. 2998-3004, doi: 10.1016/j.eswa.2014.11.028.

Kozodoi, N., Jacob, J. and Lessmann, S. (2022), “Fairness in credit scoring: assessment, implementation and profit implications”, European Journal of Operational Research, Vol. 297 No. 3, pp. 1083-1094, doi: 10.1016/j.ejor.2021.06.023.

Kozodoi, N., Lessmann, S., Papakonstantinou, K., Gatsoulis, Y. and Baesens, B. (2019), “A multi-objective approach for profit-driven feature selection in credit scoring”, Decision Support Systems, Vol. 120, pp. 106-117, doi: 10.1016/j.dss.2019.03.011.

Krichene, A. (2017), “Using a naive Bayesian classifier methodology for loan risk assessment: evidence from a Tunisian commercial bank”, Journal of Economics, Finance and Administrative Science, Vol. 22 No. 42, pp. 3-24, doi: 10.1108/JEFAS-02-2017-0039.

Kruppa, J., Schwarz, A., Arminger, G. and Ziegler, A. (2013), “Consumer credit risk: individual probability estimates using machine learning”, Expert Systems with Applications, Vol. 40 No. 13, pp. 5125-5131, doi: 10.1016/j.eswa.2013.03.019.

Laborda, J. and Ryoo, S. (2021), “Feature selection in a credit scoring model”, Mathematics, Vol. 9 No. 7, pp. 1-22, doi: 10.3390/math9070746.

Leonard, K.J. (1992), “Credit-scoring models for the evaluation of small-business loan applications”, IMA Journal of Management Mathematics, Vol. 4 No. 1, pp. 89-95, doi: 10.1093/imaman/4.1.89.

Lessmann, S., Baesens, B., Seow, H.V. and Thomas, L.C. (2015), “Benchmarking state-of-the-art classification algorithms for credit scoring: an update of research”, European Journal of Operational Research, Vol. 247 No. 1, pp. 124-136, doi: 10.1016/j.ejor.2015.05.030.

Li, Y. and Chen, W. (2020), “A comparative performance assessment of ensemble learning for credit scoring”, Mathematics, Vol. 8 No. 10, pp. 1-19, doi: 10.3390/math8101756.

Li, Z., Zhang, J., Yao, X. and Kou, G. (2021), “How to identify early defaults in online lending: a cost-sensitive multi-layer learning framework”, Knowledge-Based Systems, Vol. 221, 106963, doi: 10.1016/j.knosys.2021.106963.

Lim, W.M., Kumar, S. and Ali, F. (2022), “Advancing knowledge through literature reviews: ‘what’, ‘why’, and ‘how to contribute’”, The Service Industries Journal, Vol. 42 Nos 7-8, pp. 481-513, doi: 10.1080/02642069.2022.2047941.

Lin, W.Y., Hu, Y.H. and Tsai, C.F. (2012), “Machine learning in financial crisis prediction: a survey”, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), Vol. 42 No. 4, pp. 421-436, doi: 10.1109/TSMCC.2011.2170420.

Liu, J. and Bo, S. (2011), “Naive Bayesian classifier based on genetic simulated annealing algorithm”, Procedia Engineering, Vol. 23, pp. 504-509, doi: 10.1016/j.proeng.2011.11.2538.

Louzada, F., Ara, A. and Fernandes, G.B. (2016), “Classification methods applied to credit scoring: systematic review and overall comparison”, Surveys in Operations Research and Management Science, Vol. 21 No. 2, pp. 117-134, doi: 10.1016/j.sorms.2016.10.001.

Maldonado, S., Bravo, C., López, J. and Pérez, J. (2017), “Integrated framework for profit-based feature selection and SVM classification in credit scoring”, Decision Support Systems, Vol. 104, pp. 113-121, doi: 10.1016/j.dss.2017.10.007.

Marqués, A.I., García, V. and Sánchez, J.S. (2013), “A literature review on the application of evolutionary computing to credit scoring”, Journal of the Operational Research Society, Vol. 64 No. 9, pp. 1384-1399, doi: 10.1057/jors.2012.145.

Nalić, J. and Martinovic, G. (2020), “Building a credit scoring model based on data mining approaches”, International Journal of Software Engineering and Knowledge Engineering, Vol. 30 No. 2, pp. 147-169, doi: 10.1142/S0218194020500072.

Onay, C. and Ozturk, E. (2018), “A review of credit scoring research in the age of big data”, Journal of Financial Regulation and Compliance, Vol. 32 No. 10, pp. 91-100, doi: 10.1108/JFRC-06-2017-0054.

Orgler, Y.E. (1970), “Scoring model for commercial loans”, Journal of Money, Credit and Banking, Vol. 2 No. 4, pp. 435-445, doi: 10.2307/1991095.

Paul, J. and Criado, A.R. (2020), “The art of writing literature review: what do we know and what do we need to know?”, International Business Review, Vol. 29 No. 4, 101717, doi: 10.1016/j.ibusrev.2020.101717.

Řezáč, M. (2015), “ESIS2: information value estimator for credit scoring models”, Computational Economics, Vol. 45 No. 2, pp. 303-322, doi: 10.1007/s10614-014-9424-0.

Roa, L., Correa-Bahnsen, A., Suarez, G., Cortés-Tejada, F., Luque, M.A. and Bravo, C. (2021), “Super-app behavioral patterns in credit risk models: financial, statistical and regulatory implications”, Expert Systems with Applications, Vol. 169, 114486, doi: 10.1016/j.eswa.2020.114486.

Roy, P.K. and Shaw, K. (2021a), “A credit scoring model for SMEs using AHP and TOPSIS”, International Journal of Finance and Economics, No. December 2020, pp. 1-20, doi: 10.1002/ijfe.2425.

Roy, P.K. and Shaw, K. (2021b), “A multicriteria credit scoring model for SMEs using hybrid BWM and TOPSIS”, Financial Innovation, Vol. 7 No. 1, pp. 1-27, doi: 10.1186/s40854-021-00295-5.

Roy, P.K. and Shaw, K. (2022), “Modelling a sustainable credit score system (SCSS) using BWM and fuzzy TOPSIS”, International Journal of Sustainable Development and World Ecology, Vol. 29 No. 3, pp. 195-208, doi: 10.1080/13504509.2021.1935360.

Salcedo, N.U. (2021a), “Editorial: review and roadmap from the last 10 years (2010-2020)”, Journal of Economics, Finance and Administrative Science, Vol. 26 No. 51, pp. 2-6, doi: 10.1108/JEFAS-06-2021-271.

Salcedo, N.U. (2021b), “Editorial: an upcoming 30th anniversary encouraging the papers' publication”, Journal of Economics, Finance and Administrative Science, Vol. 26 No. 52, pp. 178-181, doi: 10.1108/JEFAS-11-2021-329.

Sariannidis, N., Papadakis, S., Garefalakis, A., Lemonakis, C. and Kyriaki-Argyro, T. (2020), “Default avoidance on credit card portfolios using accounting, demographical and exploratory factors: decision making based on machine learning (ML) techniques”, Annals of Operations Research, Vol. 294 Nos 1-2, pp. 715-739, Springer US, doi: 10.1007/s10479-019-03188-0.

Serrano-Cinca, C. and Gutiérrez-Nieto, B. (2016), “The use of profit scoring as an alternative to credit scoring systems in peer-to-peer (P2P) lending”, Decision Support Systems, Vol. 89, pp. 113-122, doi: 10.1016/j.dss.2016.06.014.

Sinha, A.P. and Zhao, H. (2008), “Incorporating domain knowledge into data mining classifiers: an application in indirect lending”, Decision Support Systems, Vol. 46 No. 1, pp. 287-299, doi: 10.1016/j.dss.2008.06.013.

Šušteršič, M., Mramor, D. and Zupan, J. (2009), “Consumer credit scoring models with limited data”, Expert Systems with Applications, Vol. 36 No. 3, pp. 4736-4744, PART 1, doi: 10.1016/j.eswa.2008.06.016.

Trivedi, S.K. (2020), “A study on credit scoring modeling with different feature selection and machine learning approaches”, Technology in Society, Vol. 63 No. 2017, 101413, doi: 10.1016/j.techsoc.2020.101413.

Verbraken, T., Bravo, C., Weber, R. and Baesens, B. (2014), “Development and application of consumer credit scoring models using profit-based classification measures”, European Journal of Operational Research, Vol. 238 No. 2, pp. 505-513, doi: 10.1016/j.ejor.2014.04.001.

Vukovic, S., Delibasic, B., Uzelac, A. and Suknovic, M. (2012), “A case-based reasoning model that uses preference theory functions for credit scoring”, Expert Systems with Applications, Vol. 39 No. 9, pp. 8389-8395, doi: 10.1016/j.eswa.2012.01.181.

Watson, R., T. and Webster, J. (2020), “Analysing the past to prepare for the future: writing a literature review a roadmap for release 2.0”, Journal of Decision Systems, Vol. 29, pp. 129-147, doi: 10.1080/12460125.2020.1798591.

Xia, Y., Li, Y., He, L., Xu, Y. and Meng, Y. (2021), “Incorporating multilevel macroeconomic variables into credit scoring for online consumer lending”, Electronic Commerce Research and Applications, Vol. 49 No. 9, 101095, doi: 10.1016/j.elerap.2021.101095.




How to Cite

Kamimura, E. S., Faia Pinto, . A. R., & Nagano, M. S. (2023). A recent review on optimisation methods applied to credit scoring models. Journal of Economics, Finance and Administrative Science, 28(56), 352–371. Retrieved from https://revistas.esan.edu.pe/index.php/jefas/article/view/691