Modelo interpretable de aprendizaje de máquina para la predicción de calificaciones crediticias corporativas

ilustraciones, gráficas, tablas

Autores:: Rodríguez Fonseca, Miguel Ángel

Tipo de recurso:

Fecha de publicación:: 2022

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

id	UNACIONAL2_466cdcd318da8f5860f4b7bfda373803
oai_identifier_str	oai:repositorio.unal.edu.co:unal/81669
network_acronym_str	UNACIONAL2
network_name_str	Universidad Nacional de Colombia
repository_id_str
dc.title.spa.fl_str_mv	Modelo interpretable de aprendizaje de máquina para la predicción de calificaciones crediticias corporativas
dc.title.translated.eng.fl_str_mv	Interpretable machine learning model for corporate credit ratings forecasting
title	Modelo interpretable de aprendizaje de máquina para la predicción de calificaciones crediticias corporativas
spellingShingle	Modelo interpretable de aprendizaje de máquina para la predicción de calificaciones crediticias corporativas 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería Machine learning Aprendizaje automático (Inteligencia artificial) Credit policy Consumer, credit Política crediticia Crédito al consumidor Aprendizaje de máquina automático Interpretabilidad Riesgo de crédito Inteligencia artificial Calificaciones crediticias corporativas LIME AutoML Interpretability Corporate credit rating Credit risk Machine learning
title_short	Modelo interpretable de aprendizaje de máquina para la predicción de calificaciones crediticias corporativas
title_full	Modelo interpretable de aprendizaje de máquina para la predicción de calificaciones crediticias corporativas
title_fullStr	Modelo interpretable de aprendizaje de máquina para la predicción de calificaciones crediticias corporativas
title_full_unstemmed	Modelo interpretable de aprendizaje de máquina para la predicción de calificaciones crediticias corporativas
title_sort	Modelo interpretable de aprendizaje de máquina para la predicción de calificaciones crediticias corporativas
dc.creator.fl_str_mv	Rodríguez Fonseca, Miguel Ángel
dc.contributor.advisor.spa.fl_str_mv	Hernández Pérez, Germán Jairo
dc.contributor.author.spa.fl_str_mv	Rodríguez Fonseca, Miguel Ángel
dc.subject.ddc.spa.fl_str_mv	620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería
topic	620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería Machine learning Aprendizaje automático (Inteligencia artificial) Credit policy Consumer, credit Política crediticia Crédito al consumidor Aprendizaje de máquina automático Interpretabilidad Riesgo de crédito Inteligencia artificial Calificaciones crediticias corporativas LIME AutoML Interpretability Corporate credit rating Credit risk Machine learning
dc.subject.lemb.eng.fl_str_mv	Machine learning Aprendizaje automático (Inteligencia artificial) Credit policy Consumer, credit
dc.subject.lemb.spa.fl_str_mv	Política crediticia Crédito al consumidor
dc.subject.proposal.spa.fl_str_mv	Aprendizaje de máquina automático Interpretabilidad Riesgo de crédito Inteligencia artificial Calificaciones crediticias corporativas
dc.subject.proposal.eng.fl_str_mv	LIME AutoML Interpretability Corporate credit rating Credit risk Machine learning
description	ilustraciones, gráficas, tablas
publishDate	2022
dc.date.accessioned.none.fl_str_mv	2022-06-29T18:44:58Z
dc.date.available.none.fl_str_mv	2022-06-29T18:44:58Z
dc.date.issued.none.fl_str_mv	2022
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv	Text
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TM
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/81669
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/81669 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	spa
language	spa
dc.relation.references.spa.fl_str_mv	[Abdou et al., 2017] Abdou, H. A., Abdallah, W. M., Mulkeen, J., Ntim, C. G., and Wang, Y. (2017). Prediction of financial strength ratings using machine learning and conventional techniques. Investment Management and Financial Innovations, 14:194–211. [Abedin et al., 2018] Abedin, M. Z., Guotai, C., Colombage, S., and Fahmida-E-Moula (2018). Credit default prediction using a support vector machine and a probabilistic neural network. Journal of Credit Risk, 14:1–27. Cited By :3 Export Date: 26 September 2019. [AghaeiRad et al., 2017] AghaeiRad, A., Chen, N., and Ribeiro, B. (2017). Improve credit scoring using transfer of learned knowledge from self-organizing map. Neural Computing and Applications, 28:1329–1342. Cited By :4¡br/¿Export Date: 26 September 2019. [Alexandros, 2017] Alexandros, G. (2017). “ forecasting models using machine learning ( ml ) techniques on banks ’ credit rating .”. [Alonso and Carbo, 2020] Alonso, A. and Carbo, J. M. (2020). Machine learning in credit risk: Measuring the dilemma between prediction and supervisory cost. SSRN Electronic Journal. [Alvim et al., 2010] Alvim, L. G. M., Santos, C. N. D., and Milidi ́u, R. L. (2010). Daily vo- lume forecasting using high frequency predictors. pages 248–254. Cited By :5¡br/¿Export Date: 26 September 2019. [Attigeri et al., 2017] Attigeri, G. V., Pai, M. M. M., and Pai, R. M. (2017). Credit risk assessment using machine learning algorithms. Advanced Science Letters, 23:3649–3653. [Ayouche et al., 2017] Ayouche, S., Aboulaich, R., and Ellaia, R. (2017). Partnership credit scoring classification probem: A neural network approach. International Journal of Applied Engineering Research, 12:693–704. [Baltaev and Chavdarov, 2014] Baltaev, A. and Chavdarov, I. (2014). Predicting corporate defaults: Evaluating moody ’ s credit rating institute. [Bandyopadhyay, 2006] Bandyopadhyay, A. (2006). Predicting probability of default of in- dian corporate bonds: logistic and z-score model approaches. Journal of Risk Finance, 7:255–272. [Bao et al., 2019] Bao, W., Lianju, N., and Yue, K. (2019). Integration of unsupervised and supervised machine learning algorithms for credit risk assessment. Expert Systems with Applications, 128:301–315. [Bhaduri, 2009] Bhaduri, A. (2009). Credit scoring using artificial immune system algo- rithms: A comparative study. pages 1540–1543. [Bhattacharjee et al., 2017] Bhattacharjee, B., Sridhar, A., and Shafi, M. (2017). An artifi- cial neural network-based ensemble model for credit risk assessment amp; deployment as a graphical user interface. International Journal of Data Mining, Modelling and Manage- ment, 9:122–141. [Birla et al., 2016] Birla, S., Kohli, K., and Dutta, A. (2016). Machine learning on imbalan- ced data in credit risk. [Bussmann et al., 2021] Bussmann, N., Giudici, P., Marinelli, D., and Papenbrock, J. (2021). Explainable machine learning in credit risk management. Computational Economics, 57:203–216. [Carvalho et al., 2019] Carvalho, D. V., Pereira, E. M., and Cardoso, J. S. (2019). Machine learning interpretability: A survey on methods and metrics. Electronics 2019, Vol. 8, Page 832, 8:832. [Chauhan et al., 2020] Chauhan, K., Jani, S., Thakkar, D., Dave, R., Bhatia, J., Tanwar, S., and Obaidat, M. S. (2020). Automated machine learning: The new wave of machine lear- ning. 2nd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2020 - Conference Proceedings, pages 205–212. [Chen et al., 2011] Chen, Q., Xue, H.-F., and Yan, L. (2011). Credit risk assessment based on potential support vector machine. volume 1, pages 97–101. [Chen et al., 2009] Chen, W., Ma, C., and Ma, L. (2009). Mining the customer credit using hybrid support vector machine technique. Expert Systems with Applications, 36:7611–7616. [Chi et al., 2019] Chi, G., Uddin, M. S., Abedin, M. Z., and Yuan, K. (2019). Hybrid model for credit risk prediction: An application of neural network approaches. International Journal on Artificial Intelligence Tools, 28. [Chuang and Huang, 2011] Chuang, C.-L. and Huang, S.-T. (2011). A hybrid neural network approach for credit scoring. Expert Systems, 28:185–196. [Cimpoeru, 2011] Cimpoeru, S. S. (2011). Neural networks and their application in cre- dit risk assessment. evidence from the romanian market. Technological and Economic Development of Economy, 17:519–534. [Correa and Gonzalez, 2011] Correa, A. B. and Gonzalez, A. M. (2011). Evolutionary algo- rithms for selecting the architecture of a mlp neural network: A credit scoring case. pages 725–732. [Danenas and Garsva, 2009] Danenas, P. and Garsva, G. (2009). Support vector machines and their application in credit risk evaluation process. Transformations in Business and Economics, 8:46–58. [Danenas and Garsva, 2012] Danenas, P. and Garsva, G. (2012). Pso-based linear svm clas- sifier selection for credit risk evaluation modeling process. volume 1 DISI, pages 338–341. [Danenas et al., 2011] Danenas, P., Garsva, G., and Gudas, S. (2011). Credit risk evaluation model development using support vector based classifiers. volume 4, pages 1699–1707. [Dima and Vasilache, 2009] Dima, A. M. and Vasilache, S. (2009). Ann model for corporate credit risk assessment. pages 94–98. [Du, 2003] Du, Y. (2003). Predicting credit rating and credit rating changes : A new ap- proach. Working Paper. [Elshawi et al., 2019] Elshawi, R., Maher, M., and Sakr, S. (2019). Automated machine learning: State-of-the-art and open challenges. [Fang and Huang, 2011] Fang, K. and Huang, H. (2011). Variable selection for credit risk model using data mining technique. Journal of Computers, 6:1868–1874. [Fenerich et al., 2018] Fenerich, A. T., Steiner, M. T. A., Neto, P. J. S., Tochetto, E. A., Tsutsumi, D. P., Assef, F. M., and Santos, B. S. D. (2018). Use of machine learning tech- niques in bank credit risk analysis. volume 2018-December. Fitch, 2021] Fitch (2021). Rating definitions. [Gilpin et al., 2019] Gilpin, L. H., Bau, D., Yuan, B. Z., Bajwa, A., Specter, M., and Kagal, L. (2019). Explaining explanations: An overview of interpretability of machine learning. Proceedings - 2018 IEEE 5th International Conference on Data Science and Advanced Analytics, DSAA 2018, pages 80–89. [Golbayani et al., 2020a] Golbayani, P., Florescu, I., and Chatterjee, R. (2020a). A compa- rative study of forecasting corporate credit ratings using neural networks, support vector machines, and decision trees. arXiv. [Golbayani et al., 2020b] Golbayani, P., Wang, D., and Florescu, I. (2020b). Application of deep neural networks to assess corporate credit rating. pages 1–18. [Groth and Muntermann, 2011] Groth, S. S. and Muntermann, J. (2011). An intraday mar- ket risk management approach based on textual analysis. Decision Support Systems, 50:680–691. [Guo et al., 2012] Guo, X., Zhu, Z., and Shi, J. (2012). A corporate credit rating model using support vector domain combined with fuzzy clustering algorithm. Mathematical Problems in Engineering, 2012. [Hajek, 2010] Hajek, P. (2010). Credit Rating Modelling by Neural Networks. [Hajek et al., 2014] Hajek, P., Olej, V., and Myskova, R. (2014). Forecasting corporate financial performance using sentiment in annual reports for stakeholders’ decision-making. Technological and Economic Development of Economy, 20:721–738. [Hajek et al., 2017] Hajek, P., Olej, V., and Prochazka, O. (2017). Predicting corporate credit ratings using content analysis of annual reports –a na ̈ıve bayesian network approach. Lecture Notes in Business Information Processing, 276:47–61. [Herv ́e and Erwan, 2020] Herv ́e, P. and Erwan, R. (2020). Explain artificial intelligence for credit risk management. [Hilscher et al., 2010] Hilscher, J., Bruche, M., Campbell, J., Cecchetti, S., Jarrow, R., Le- baron, B., Ramadorai, T., Shleifer, A., Singhal, M., Stein, J., Szilagyi, J., and Verdelhan, A. (2010). Credit ratings and credit risk. [Hsieh and Hung, 2010] Hsieh, N.-C. and Hung, L.-P. (2010). A data driven ensemble clas- sifier for credit scoring analysis. Expert Systems with Applications, 37:534–545. [Hu, 2009] Hu, J. (2009). Personal credit rating using artificial intelligence technology for the national student loans. pages 103–106. [Huang, 2012] Huang, C.-F. (2012). A hybrid stock selection model using genetic algorithms and support vector regression. Applied Soft Computing Journal, 12:807–818. Cited By :107 Export Date: 26 September 2019. [Huang et al., 2020] Huang, J., Chai, J., and Cho, S. (2020). Deep learning in finance and banking: A literature review and classification. Frontiers of Business Research in China, 14. [Huang, 2009] Huang, S.-C. (2009). Integrating nonlinear graph based dimensionality reduc- tion schemes with svms for credit rating forecasting. Expert Systems with Applications, 36:7515–7518. [Huang et al., 2010] Huang, W., Zhu, X., and Su, Q. (2010). Research on application of personal credit scoring based on bp-logistic hybrid algorithm. volume 4, pages V4735– V4739. [Huang et al., 2004] Huang, Z., Chen, H., Hsu, C. J., Chen, W. H., and Wu, S. (2004). Credit rating analysis with support vector machines and neural networks: A market comparative study. Decision Support Systems, 37:543–558. [H ́ajek and Olej, 2014] H ́ajek, P. and Olej, V. (2014). Predicting firms’ credit ratings using ensembles of artificial immune systems and machine learning – an over-sampling approach. IFIP Advances in Information and Communication Technology, 436:29–38. [Jagric et al., 2011] Jagric, V., Kracun, D., and Jagric, T. (2011). Does non-linearity matter in retail credit risk modeling? Finance a Uver - Czech Journal of Economics and Finance, 61:384–402. [Khashman, 2009] Khashman, A. (2009). A neural network model for credit risk evaluation. International Journal of Neural Systems, 19:285–294. [Khashman, 2010] Khashman, A. (2010). Neural networks for credit risk evaluation: Investi- gation of different neural models and learning schemes. Expert Systems with Applications, 37:6233–6239. [Khashman, 2011] Khashman, A. (2011). Credit risk evaluation using neural networks: Emo- tional versus conventional models. Applied Soft Computing Journal, 11:5477–5484. [Khemakhem and Boujelbene, 2018] Khemakhem, S. and Boujelbene, Y. (2018). Predicting credit risk on the basis of financial and non-financial variables and data mining. Review of Accounting and Finance, 17:316–340. [Kim and Ahn, 2012] Kim, K.-J. and Ahn, H. (2012). A corporate credit rating model using multi-class support vector machines with an ordinal pairwise partitioning approach. Com- puters and Operations Research, 39:1800–1811. [Kotsiantis et al., 2009] Kotsiantis, S. B., Kanellopoulos, D., Karioti, V., and Tampakas, V. (2009). An ontology-based portal for credit risk analysis. pages 165–169. [Kulkarni and Dhage, 2019] Kulkarni, S. V. and Dhage, S. N. (2019). Advanced credit score calculation using social media and machine learning. Journal of Intelligent and Fuzzy Systems, 36:2373–2380. [Kvamme et al., 2018] Kvamme, H., Sellereite, N., Aas, K., and Sjursen, S. (2018). Predic- ting mortgage default using convolutional neural networks. Expert Systems with Applica- tions, 102:207–217. [Ledell and Poirier, 2020] Ledell, E. and Poirier, S. (2020). H2o automl: Scalable automatic machine learning. [Lei et al., 2019] Lei, K., Xie, Y., Zhong, S., Dai, J., Yang, M., and Shen, Y. (2019). Ge- nerative adversarial fusion network for class imbalance credit scoring. Neural Computing and Applications. [Leo et al., 2019] Leo, M., Sharma, S., and Maddulety, K. (2019). Machine learning in ban- king risk management: A literature review. Risks, 7. [Letizia and Lillo, 2019] Letizia, E. and Lillo, F. (2019). Corporate payments networks and credit risk rating. EPJ Data Science, 8. [Li et al., 2017] Li, Z., Tian, Y., Li, K., Zhou, F., and Yang, W. (2017). Reject inference in credit scoring using semi-supervised support vector machines. Expert Systems with Applications, 74:105–114. [Lin and Zhang, 2012] Lin, Y. and Zhang, Y. (2012). Credit risk assessment based on neural network. pages 402–404. [Linardatos et al., 2020] Linardatos, P., Papastefanopoulos, V., and Kotsiantis, S. (2020). Explainable ai: A review of machine learning interpretability methods. Entropy 2021, Vol. 23, Page 18, 23:18. [Liu et al., 2010] Liu, X., Fu, H., and Lin, W. (2010). A modified support vector machine model for credit scoring. International Journal of Computational Intelligence Systems, 3:797–804. [Liu et al., 2009] Liu, Y., Cai, Q., Luo, Y., Qian, J., and Ye, F. (2009). Artificial neu- ral networks for corporation credit rating analysis. volume 1, pages 81–84. [Lu and Cao, 2009] Lu, H.-K. and Cao, J. (2009). Personal credit scoring model based on integration of rough set and ga-neural network. Nanjing Li Gong Daxue Xuebao/Journal of Nanjing University of Science and Technology, 33:194–198. [Luo et al., 2017] Luo, C., Wu, D., and Wu, D. (2017). A deep learning approach for credit scoring using credit default swaps. Engineering Applications of Artificial Intelligence, 65:465–470. [Malik and Hermawan, 2018] Malik, R. F. and Hermawan (2018). Credit scoring using cart algorithm and binary particle swarm optimization. International Journal of Electrical and Computer Engineering, 8:5425–5431. [Mall, 2018] Mall, S. (2018). An empirical study on credit risk management: The case of nonbanking financial companies. Journal of Credit Risk, 14:49–66. [Metz and Cantor, 2006] Metz, A. and Cantor, R. (2006). Moody’s credit rating prediction model. Moody’s Investors Service, pages 1–20. [Molnar, 2022] Molnar, C. (2022). Interpretable Machine Learning. 2 edition. [Moonasar, 2007] Moonasar, V. (2007). Credit risk analysis using credit risk analysis using artificial intelligence : Evidence from a leading south african banking institution. Metho- dology. [Moscatelli et al., 2020] Moscatelli, M., Parlapiano, F., Narizzano, S., and Viggiano, G. (2020). Corporate default forecasting with machine learning. Expert Systems with Appli- cations, 161. [Nanni and Lumini, 2009] Nanni, L. and Lumini, A. (2009). An experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring. Expert Systems with Applications, 36:3028–3033. [Olson et al., 2016] Olson, R. S., Bartley, N., Urbanowicz, R. J., and Moore, J. H. (2016). Evaluation of a tree-based pipeline optimization tool for automating data science. GECCO 2016 - Proceedings of the 2016 Genetic and Evolutionary Computation Conference, pages 485–492. [Paper et al., 2017] Paper, F. I. E. T., Carlenius, B., Døvik, E. K., Kolberg, J. K., and Aanes, B. (2017). Corporate credit rating using deep learning with genetic algorithms norwegian school of economics. pages 1–68. [Patron et al., 2019] Patron, G., Leon, D., Lopez, E., and Hernandez, G. (2019). An inter- pretable automated machine learning credit risk model. [Pazhoheshfara et al., 2010] Pazhoheshfara, P., Azadeh, A., and Saberia, M. (2010). Impro- ving accuracy of artificial neural networks for credit scoring models using voting algorithm. [Petropoulos et al., 2018] Petropoulos, A., Siakoulis, V., Stavroulakis, E., and Klamargias, A. (2018). A robust machine learning approach for credit risk analysis of large loan level datasets using deep learning and extreme gradient boosting. The use of big data analytics and artificial intelligence in central banking, 50:30–31. [Ping, 2009] Ping, Y. (2009). Hybrid classifier using neighborhood rough set and svm for credit scoring. pages 138–142. Cited By :21 Export Date: 26 September 2019. [Ping and Yongheng, 2011] Ping, Y. and Yongheng, L. (2011). Neighborhood rough set and svm based hybrid credit scoring classifier. Expert Systems with Applications, 38:11300– 11304. [Provenzano et al., 2020] Provenzano, A. R., Trifir`o, D., Jean, N., Pera, G. L., Spadaccino, M., Massaron, L., and Nordio, C. (2020). An artificial intelligence approach to shadow rating. SSRN Electronic Journal, pages 1–18. [Rahayu et al., 2010] Rahayu, S. P., Zain, J. M., Embong, A., and Purnami, S. W. (2010). Credit risk classification using kernel logistic regression with optimal parameter. pages 602–605. Export Date: 26 September 2019. [Ribeiro and Chen, 2017] Ribeiro, B. and Chen, N. (2017). Financial credit risk assess- ment via learning-based hashing. Intelligent Decision Technologies, 11:177–186. [Rom, 2009] Rom, M. C. (2009). The credit rating agencies and the subprime mess: Greedy, ignorant, and stressed? [Rudin, 2019] Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. [Ryser and Denzler, 2009] Ryser, M. and Denzler, S. (2009). Selecting credit rating mo- dels: A cross-validation-based comparison of discriminatory power. Financial Markets and Portfolio Management, 23:187–203. [Saitoh, 2016] Saitoh, F. (2016). Predictive modeling of corporate credit ratings using a semi-supervised random forest regression. volume 2016-December, pages 429–433. [Salehi and Mansoury, 2011] Salehi, M. and Mansoury, A. (2011). An evaluation of iranian banking system credit risk: Neural network and logistic regression approach. International Journal of Physical Sciences, 6:6082–6090. [Santos, 2007] Santos, K. (2007). Corporate credit ratings: a quick guide. 44:45–49. [Selau and Ribeiro, 2011] Selau, L. P. R. and Ribeiro, J. L. D. (2011). A systematic approach to construct credit risk forecast models. Pesquisa Operacional, 31:41–56. [Sitt and Wu, 2011] Sitt, M. and Wu, T. (2011). Evaluation of credit risk. pages 1–5. [SP, 2021] SP (2021). Guide to credit rating essentials what are credit ratings and how do they work? [Studer et al., 2021] Studer, S., Bui, T. B., Drescher, C., Hanuschkin, A., Winkler, L., Peters, S., and M ̈uller, K.-R. (2021). Towards crisp-ml(q): A machine learning process model with quality assurance methodology. Machine Learning and Knowledge Extraction 2021, Vol. 3, Pages 392-413, 3:392–413. [Truong et al., 2019] Truong, A., Walters, A., Goodsitt, J., Hines, K., Bruss, B., and Farivar, R. (2019). Towards automated machine learning: Evaluation and comparison of automl approaches and tools. [Tsai and Chen, 2010] Tsai, C.-F. and Chen, M.-L. (2010). Credit rating by hybrid machine learning techniques. Applied Soft Computing Journal, 10:374–380. [Turkson et al., 2016] Turkson, R. E., Baagyere, E. Y., and Wenya, G. E. (2016). A ma- chine learning approach for predicting bank credit worthiness. pages 81–87. [Twala, 2009] Twala, B. (2009). Combining classifiers for credit risk prediction. Journal of Systems Science and Systems Engineering, 18:292–311. [Vahid and Ahmadi, 2016] Vahid, P. R. and Ahmadi, A. (2016). Modeling corporate custo- mers’ credit risk considering the ensemble approaches in multiclass classification: Evidence from iranian corporate credits. Journal of Credit Risk, 12:71–95. [Vidovic and Yue, 2020] Vidovic, L. and Yue, L. (2020). Machine learning and credit risk modelling machine learning in finance. [Wang et al., 2012] Wang, J., Hedar, A.-R., Wang, S., and Ma, J. (2012). Rough set and scatter search metaheuristic based feature selection for credit scoring. Expert Systems with Applications, 39:6123–6128. [Wu, 2010] Wu, Y. (2010). Research of credit sale risk evaluation based on bp neural network. [Yao, 2009] Yao, P. (2009). Credit scoring using ensemble machine learning. volume 3, pages 244–246. [Yao et al., 2018] Yao, Q., Wang, M., Chen, Y., Dai, W., Li, Y.-F., Tu, W.-W., Yang, Q., and Yu, Y. (2018). Taking human out of learning applications: A survey on automated machine learning. [Ye et al., 2008] Ye, Y., Liu, S., and Li, J. (2008). A multiclass machine learning approach to credit rating prediction. Proceedings - International Symposium on Information Pro- cessing, ISIP 2008 and International Pacific Workshop on Web Mining and Web-Based Application, WMWA 2008, pages 57–61. [Yi, 2009] Yi, J. (2009). Credit scoring model based on the decision tree and the simulated annealing algorithm. volume 4, pages 18–22. [Zhang et al., 2009] Zhang, C., Li, Y., Liu, M., and Liu, Z. (2009). An enhanced approach for investment risk forecasting of electric power projects. volume 1, pages 29–32. [Zhao and Chen, 2009] Zhao, S.-F. and Chen, L.-C. (2009). The bp neural networks appli- cations in bank credit risk management system. pages 527–532. [Zhu et al., 2010] Zhu, C., Zhan, Y., and Jia, S. (2010). Research on bp neural network evaluation model of credit risk of bank clients.
dc.rights.coar.fl_str_mv	http://purl.org/coar/access_right/c_abf2
dc.rights.license.spa.fl_str_mv	Atribución-NoComercial 4.0 Internacional
dc.rights.uri.spa.fl_str_mv	http://creativecommons.org/licenses/by-nc/4.0/
dc.rights.accessrights.spa.fl_str_mv	info:eu-repo/semantics/openAccess
rights_invalid_str_mv	Atribución-NoComercial 4.0 Internacional http://creativecommons.org/licenses/by-nc/4.0/ http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv	openAccess
dc.format.extent.spa.fl_str_mv	xi, 61 páginas
dc.format.mimetype.spa.fl_str_mv	application/pdf
dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Bogotá - Ingeniería - Maestría en Ingeniería - Ingeniería de Sistemas y Computación
dc.publisher.department.spa.fl_str_mv	Departamento de Ingeniería de Sistemas e Industrial
dc.publisher.faculty.spa.fl_str_mv	Facultad de Ingeniería
dc.publisher.place.spa.fl_str_mv	Bogotá, Colombia
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Bogotá
institution	Universidad Nacional de Colombia
bitstream.url.fl_str_mv	https://repositorio.unal.edu.co/bitstream/unal/81669/3/1031151220.2022.pdf https://repositorio.unal.edu.co/bitstream/unal/81669/4/license.txt https://repositorio.unal.edu.co/bitstream/unal/81669/5/1031151220.2022.pdf.jpg
bitstream.checksum.fl_str_mv	0e24f2f68f971b5d796bcf2966cb157f 8153f7789df02f0a4c9e079953658ab2 0b59ebeebfa668eb7134d5453a3fc97e
bitstream.checksumAlgorithm.fl_str_mv	MD5 MD5 MD5
repository.name.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
repository.mail.fl_str_mv	repositorio_nal@unal.edu.co
_version_	1814089526385049600
spelling	Atribución-NoComercial 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Hernández Pérez, Germán Jairo66571677097cf76f6d8382e3c0191758Rodríguez Fonseca, Miguel Ángelb398561b1f9ad6e1254284f53797a5d72022-06-29T18:44:58Z2022-06-29T18:44:58Z2022https://repositorio.unal.edu.co/handle/unal/81669Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, gráficas, tablasLas calificaciones crediticias corporativas son uno de los indicadores financieros de mayor relevancia en el análisis de riesgo crediticio. Estas son generadas por diversas agencias calificadoras las cuales basan sus metodologías en el uso de diversas variables financieras de cada compañía y su impacto en el mercado es de tal importancia que su mala gestión puede desencadenar grandes crisis financieras como la ocurrida en 2008. Junto a esto la gran importancia que tienen los modelos internos de calculo de riesgo de crédito y las regulaciones y acuerdos internacionales buscan que se tenga un nivel de explicabilidad de los métodos empleados al realizar esta gestión de riesgo. En este trabajo se propone el uso de Aprendizaje de Máquina automático (AutoML) como herramienta para la generación de un modelo de aprendizaje de máquina que realice una predicción de las calificaciones crediticias corporativas haciendo uso de datos provenientes de hojas de balance, estados financieros e información descriptiva de la compañía. Adicionalmente, como aporte principal de este trabajo se realizó la inclusión, dentro del AutoML, del nivel de interpretabilidad de cada modelo como un segundo objetivo a optimizar, permitiendo la generación de modelos que puedan explicar de mejor manera sus resultados. (Texto tomado de la fuente).Corporate credit ratings are one of the most relevant financial indicators in credit risk analysis. These are generated by different rating agencies which base their methodologies on the use of various financial variables of each company and the experience of their analysts; their impact on the market is of such importance that their mismanagement can trigger major financial crises such as the one that occurred in 2008. Along with this, the great importance of internal models for calculating credit risk and international regulations and agreements seek to have a level of explainability of the methods used to perform this risk management. This paper proposes the use of Automatic Machine Learning (AutoML) as a tool for the generation of a machine learning model that performs a prediction of corporate credit ratings using data from balance sheets, financial statements and descriptive information of the company. Additionally, the main contribution of this work was the inclusion within AutoML of the level of interpretability of each model as a second objective to be optimized, allowing the generation of models that can better explain their results.Incluye anexosMaestríaMagíster en Ingeniería - Ingeniería de Sistemas y ComputaciónSistemas inteligentesxi, 61 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ingeniería - Maestría en Ingeniería - Ingeniería de Sistemas y ComputaciónDepartamento de Ingeniería de Sistemas e IndustrialFacultad de IngenieríaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingenieríaMachine learningAprendizaje automático (Inteligencia artificial)Credit policyConsumer, creditPolítica crediticiaCrédito al consumidorAprendizaje de máquina automáticoInterpretabilidadRiesgo de créditoInteligencia artificialCalificaciones crediticias corporativasLIMEAutoMLInterpretabilityCorporate credit ratingCredit riskMachine learningModelo interpretable de aprendizaje de máquina para la predicción de calificaciones crediticias corporativasInterpretable machine learning model for corporate credit ratings forecastingTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TM[Abdou et al., 2017] Abdou, H. A., Abdallah, W. M., Mulkeen, J., Ntim, C. G., and Wang, Y. (2017). Prediction of financial strength ratings using machine learning and conventional techniques. Investment Management and Financial Innovations, 14:194–211.[Abedin et al., 2018] Abedin, M. Z., Guotai, C., Colombage, S., and Fahmida-E-Moula (2018). Credit default prediction using a support vector machine and a probabilistic neural network. Journal of Credit Risk, 14:1–27. Cited By :3 Export Date: 26 September 2019.[AghaeiRad et al., 2017] AghaeiRad, A., Chen, N., and Ribeiro, B. (2017). Improve credit scoring using transfer of learned knowledge from self-organizing map. Neural Computing and Applications, 28:1329–1342. Cited By :4¡br/¿Export Date: 26 September 2019.[Alexandros, 2017] Alexandros, G. (2017). “ forecasting models using machine learning ( ml ) techniques on banks ’ credit rating .”.[Alonso and Carbo, 2020] Alonso, A. and Carbo, J. M. (2020). Machine learning in credit risk: Measuring the dilemma between prediction and supervisory cost. SSRN Electronic Journal.[Alvim et al., 2010] Alvim, L. G. M., Santos, C. N. D., and Milidi ́u, R. L. (2010). Daily vo- lume forecasting using high frequency predictors. pages 248–254. Cited By :5¡br/¿Export Date: 26 September 2019.[Attigeri et al., 2017] Attigeri, G. V., Pai, M. M. M., and Pai, R. M. (2017). Credit risk assessment using machine learning algorithms. Advanced Science Letters, 23:3649–3653.[Ayouche et al., 2017] Ayouche, S., Aboulaich, R., and Ellaia, R. (2017). Partnership credit scoring classification probem: A neural network approach. International Journal of Applied Engineering Research, 12:693–704.[Baltaev and Chavdarov, 2014] Baltaev, A. and Chavdarov, I. (2014). Predicting corporate defaults: Evaluating moody ’ s credit rating institute.[Bandyopadhyay, 2006] Bandyopadhyay, A. (2006). Predicting probability of default of in- dian corporate bonds: logistic and z-score model approaches. Journal of Risk Finance, 7:255–272.[Bao et al., 2019] Bao, W., Lianju, N., and Yue, K. (2019). Integration of unsupervised and supervised machine learning algorithms for credit risk assessment. Expert Systems with Applications, 128:301–315.[Bhaduri, 2009] Bhaduri, A. (2009). Credit scoring using artificial immune system algo- rithms: A comparative study. pages 1540–1543.[Bhattacharjee et al., 2017] Bhattacharjee, B., Sridhar, A., and Shafi, M. (2017). An artifi- cial neural network-based ensemble model for credit risk assessment amp; deployment as a graphical user interface. International Journal of Data Mining, Modelling and Manage- ment, 9:122–141.[Birla et al., 2016] Birla, S., Kohli, K., and Dutta, A. (2016). Machine learning on imbalan- ced data in credit risk.[Bussmann et al., 2021] Bussmann, N., Giudici, P., Marinelli, D., and Papenbrock, J. (2021). Explainable machine learning in credit risk management. Computational Economics, 57:203–216.[Carvalho et al., 2019] Carvalho, D. V., Pereira, E. M., and Cardoso, J. S. (2019). Machine learning interpretability: A survey on methods and metrics. Electronics 2019, Vol. 8, Page 832, 8:832.[Chauhan et al., 2020] Chauhan, K., Jani, S., Thakkar, D., Dave, R., Bhatia, J., Tanwar, S., and Obaidat, M. S. (2020). Automated machine learning: The new wave of machine lear- ning. 2nd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2020 - Conference Proceedings, pages 205–212.[Chen et al., 2011] Chen, Q., Xue, H.-F., and Yan, L. (2011). Credit risk assessment based on potential support vector machine. volume 1, pages 97–101.[Chen et al., 2009] Chen, W., Ma, C., and Ma, L. (2009). Mining the customer credit using hybrid support vector machine technique. Expert Systems with Applications, 36:7611–7616.[Chi et al., 2019] Chi, G., Uddin, M. S., Abedin, M. Z., and Yuan, K. (2019). Hybrid model for credit risk prediction: An application of neural network approaches. International Journal on Artificial Intelligence Tools, 28.[Chuang and Huang, 2011] Chuang, C.-L. and Huang, S.-T. (2011). A hybrid neural network approach for credit scoring. Expert Systems, 28:185–196.[Cimpoeru, 2011] Cimpoeru, S. S. (2011). Neural networks and their application in cre- dit risk assessment. evidence from the romanian market. Technological and Economic Development of Economy, 17:519–534.[Correa and Gonzalez, 2011] Correa, A. B. and Gonzalez, A. M. (2011). Evolutionary algo- rithms for selecting the architecture of a mlp neural network: A credit scoring case. pages 725–732.[Danenas and Garsva, 2009] Danenas, P. and Garsva, G. (2009). Support vector machines and their application in credit risk evaluation process. Transformations in Business and Economics, 8:46–58.[Danenas and Garsva, 2012] Danenas, P. and Garsva, G. (2012). Pso-based linear svm clas- sifier selection for credit risk evaluation modeling process. volume 1 DISI, pages 338–341.[Danenas et al., 2011] Danenas, P., Garsva, G., and Gudas, S. (2011). Credit risk evaluation model development using support vector based classifiers. volume 4, pages 1699–1707.[Dima and Vasilache, 2009] Dima, A. M. and Vasilache, S. (2009). Ann model for corporate credit risk assessment. pages 94–98.[Du, 2003] Du, Y. (2003). Predicting credit rating and credit rating changes : A new ap- proach. Working Paper.[Elshawi et al., 2019] Elshawi, R., Maher, M., and Sakr, S. (2019). Automated machine learning: State-of-the-art and open challenges.[Fang and Huang, 2011] Fang, K. and Huang, H. (2011). Variable selection for credit risk model using data mining technique. Journal of Computers, 6:1868–1874.[Fenerich et al., 2018] Fenerich, A. T., Steiner, M. T. A., Neto, P. J. S., Tochetto, E. A., Tsutsumi, D. P., Assef, F. M., and Santos, B. S. D. (2018). Use of machine learning tech- niques in bank credit risk analysis. volume 2018-December.Fitch, 2021] Fitch (2021). Rating definitions.[Gilpin et al., 2019] Gilpin, L. H., Bau, D., Yuan, B. Z., Bajwa, A., Specter, M., and Kagal, L. (2019). Explaining explanations: An overview of interpretability of machine learning. Proceedings - 2018 IEEE 5th International Conference on Data Science and Advanced Analytics, DSAA 2018, pages 80–89.[Golbayani et al., 2020a] Golbayani, P., Florescu, I., and Chatterjee, R. (2020a). A compa- rative study of forecasting corporate credit ratings using neural networks, support vector machines, and decision trees. arXiv.[Golbayani et al., 2020b] Golbayani, P., Wang, D., and Florescu, I. (2020b). Application of deep neural networks to assess corporate credit rating. pages 1–18.[Groth and Muntermann, 2011] Groth, S. S. and Muntermann, J. (2011). An intraday mar- ket risk management approach based on textual analysis. Decision Support Systems, 50:680–691.[Guo et al., 2012] Guo, X., Zhu, Z., and Shi, J. (2012). A corporate credit rating model using support vector domain combined with fuzzy clustering algorithm. Mathematical Problems in Engineering, 2012.[Hajek, 2010] Hajek, P. (2010). Credit Rating Modelling by Neural Networks.[Hajek et al., 2014] Hajek, P., Olej, V., and Myskova, R. (2014). Forecasting corporate financial performance using sentiment in annual reports for stakeholders’ decision-making. Technological and Economic Development of Economy, 20:721–738.[Hajek et al., 2017] Hajek, P., Olej, V., and Prochazka, O. (2017). Predicting corporate credit ratings using content analysis of annual reports –a na ̈ıve bayesian network approach. Lecture Notes in Business Information Processing, 276:47–61.[Herv ́e and Erwan, 2020] Herv ́e, P. and Erwan, R. (2020). Explain artificial intelligence for credit risk management.[Hilscher et al., 2010] Hilscher, J., Bruche, M., Campbell, J., Cecchetti, S., Jarrow, R., Le- baron, B., Ramadorai, T., Shleifer, A., Singhal, M., Stein, J., Szilagyi, J., and Verdelhan, A. (2010). Credit ratings and credit risk.[Hsieh and Hung, 2010] Hsieh, N.-C. and Hung, L.-P. (2010). A data driven ensemble clas- sifier for credit scoring analysis. Expert Systems with Applications, 37:534–545.[Hu, 2009] Hu, J. (2009). Personal credit rating using artificial intelligence technology for the national student loans. pages 103–106.[Huang, 2012] Huang, C.-F. (2012). A hybrid stock selection model using genetic algorithms and support vector regression. Applied Soft Computing Journal, 12:807–818. Cited By :107 Export Date: 26 September 2019.[Huang et al., 2020] Huang, J., Chai, J., and Cho, S. (2020). Deep learning in finance and banking: A literature review and classification. Frontiers of Business Research in China, 14.[Huang, 2009] Huang, S.-C. (2009). Integrating nonlinear graph based dimensionality reduc- tion schemes with svms for credit rating forecasting. Expert Systems with Applications, 36:7515–7518.[Huang et al., 2010] Huang, W., Zhu, X., and Su, Q. (2010). Research on application of personal credit scoring based on bp-logistic hybrid algorithm. volume 4, pages V4735– V4739.[Huang et al., 2004] Huang, Z., Chen, H., Hsu, C. J., Chen, W. H., and Wu, S. (2004). Credit rating analysis with support vector machines and neural networks: A market comparative study. Decision Support Systems, 37:543–558.[H ́ajek and Olej, 2014] H ́ajek, P. and Olej, V. (2014). Predicting firms’ credit ratings using ensembles of artificial immune systems and machine learning – an over-sampling approach. IFIP Advances in Information and Communication Technology, 436:29–38.[Jagric et al., 2011] Jagric, V., Kracun, D., and Jagric, T. (2011). Does non-linearity matter in retail credit risk modeling? Finance a Uver - Czech Journal of Economics and Finance, 61:384–402.[Khashman, 2009] Khashman, A. (2009). A neural network model for credit risk evaluation. International Journal of Neural Systems, 19:285–294.[Khashman, 2010] Khashman, A. (2010). Neural networks for credit risk evaluation: Investi- gation of different neural models and learning schemes. Expert Systems with Applications, 37:6233–6239.[Khashman, 2011] Khashman, A. (2011). Credit risk evaluation using neural networks: Emo- tional versus conventional models. Applied Soft Computing Journal, 11:5477–5484.[Khemakhem and Boujelbene, 2018] Khemakhem, S. and Boujelbene, Y. (2018). Predicting credit risk on the basis of financial and non-financial variables and data mining. Review of Accounting and Finance, 17:316–340.[Kim and Ahn, 2012] Kim, K.-J. and Ahn, H. (2012). A corporate credit rating model using multi-class support vector machines with an ordinal pairwise partitioning approach. Com- puters and Operations Research, 39:1800–1811.[Kotsiantis et al., 2009] Kotsiantis, S. B., Kanellopoulos, D., Karioti, V., and Tampakas, V. (2009). An ontology-based portal for credit risk analysis. pages 165–169.[Kulkarni and Dhage, 2019] Kulkarni, S. V. and Dhage, S. N. (2019). Advanced credit score calculation using social media and machine learning. Journal of Intelligent and Fuzzy Systems, 36:2373–2380.[Kvamme et al., 2018] Kvamme, H., Sellereite, N., Aas, K., and Sjursen, S. (2018). Predic- ting mortgage default using convolutional neural networks. Expert Systems with Applica- tions, 102:207–217.[Ledell and Poirier, 2020] Ledell, E. and Poirier, S. (2020). H2o automl: Scalable automatic machine learning.[Lei et al., 2019] Lei, K., Xie, Y., Zhong, S., Dai, J., Yang, M., and Shen, Y. (2019). Ge- nerative adversarial fusion network for class imbalance credit scoring. Neural Computing and Applications.[Leo et al., 2019] Leo, M., Sharma, S., and Maddulety, K. (2019). Machine learning in ban- king risk management: A literature review. Risks, 7.[Letizia and Lillo, 2019] Letizia, E. and Lillo, F. (2019). Corporate payments networks and credit risk rating. EPJ Data Science, 8.[Li et al., 2017] Li, Z., Tian, Y., Li, K., Zhou, F., and Yang, W. (2017). Reject inference in credit scoring using semi-supervised support vector machines. Expert Systems with Applications, 74:105–114.[Lin and Zhang, 2012] Lin, Y. and Zhang, Y. (2012). Credit risk assessment based on neural network. pages 402–404.[Linardatos et al., 2020] Linardatos, P., Papastefanopoulos, V., and Kotsiantis, S. (2020). Explainable ai: A review of machine learning interpretability methods. Entropy 2021, Vol. 23, Page 18, 23:18.[Liu et al., 2010] Liu, X., Fu, H., and Lin, W. (2010). A modified support vector machine model for credit scoring. International Journal of Computational Intelligence Systems, 3:797–804.[Liu et al., 2009] Liu, Y., Cai, Q., Luo, Y., Qian, J., and Ye, F. (2009). Artificial neu- ral networks for corporation credit rating analysis. volume 1, pages 81–84.[Lu and Cao, 2009] Lu, H.-K. and Cao, J. (2009). Personal credit scoring model based on integration of rough set and ga-neural network. Nanjing Li Gong Daxue Xuebao/Journal of Nanjing University of Science and Technology, 33:194–198.[Luo et al., 2017] Luo, C., Wu, D., and Wu, D. (2017). A deep learning approach for credit scoring using credit default swaps. Engineering Applications of Artificial Intelligence, 65:465–470.[Malik and Hermawan, 2018] Malik, R. F. and Hermawan (2018). Credit scoring using cart algorithm and binary particle swarm optimization. International Journal of Electrical and Computer Engineering, 8:5425–5431.[Mall, 2018] Mall, S. (2018). An empirical study on credit risk management: The case of nonbanking financial companies. Journal of Credit Risk, 14:49–66.[Metz and Cantor, 2006] Metz, A. and Cantor, R. (2006). Moody’s credit rating prediction model. Moody’s Investors Service, pages 1–20.[Molnar, 2022] Molnar, C. (2022). Interpretable Machine Learning. 2 edition.[Moonasar, 2007] Moonasar, V. (2007). Credit risk analysis using credit risk analysis using artificial intelligence : Evidence from a leading south african banking institution. Metho- dology.[Moscatelli et al., 2020] Moscatelli, M., Parlapiano, F., Narizzano, S., and Viggiano, G. (2020). Corporate default forecasting with machine learning. Expert Systems with Appli- cations, 161.[Nanni and Lumini, 2009] Nanni, L. and Lumini, A. (2009). An experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring. Expert Systems with Applications, 36:3028–3033.[Olson et al., 2016] Olson, R. S., Bartley, N., Urbanowicz, R. J., and Moore, J. H. (2016). Evaluation of a tree-based pipeline optimization tool for automating data science. GECCO 2016 - Proceedings of the 2016 Genetic and Evolutionary Computation Conference, pages 485–492.[Paper et al., 2017] Paper, F. I. E. T., Carlenius, B., Døvik, E. K., Kolberg, J. K., and Aanes, B. (2017). Corporate credit rating using deep learning with genetic algorithms norwegian school of economics. pages 1–68.[Patron et al., 2019] Patron, G., Leon, D., Lopez, E., and Hernandez, G. (2019). An inter- pretable automated machine learning credit risk model.[Pazhoheshfara et al., 2010] Pazhoheshfara, P., Azadeh, A., and Saberia, M. (2010). Impro- ving accuracy of artificial neural networks for credit scoring models using voting algorithm.[Petropoulos et al., 2018] Petropoulos, A., Siakoulis, V., Stavroulakis, E., and Klamargias, A. (2018). A robust machine learning approach for credit risk analysis of large loan level datasets using deep learning and extreme gradient boosting. The use of big data analytics and artificial intelligence in central banking, 50:30–31.[Ping, 2009] Ping, Y. (2009). Hybrid classifier using neighborhood rough set and svm for credit scoring. pages 138–142. Cited By :21 Export Date: 26 September 2019.[Ping and Yongheng, 2011] Ping, Y. and Yongheng, L. (2011). Neighborhood rough set and svm based hybrid credit scoring classifier. Expert Systems with Applications, 38:11300– 11304.[Provenzano et al., 2020] Provenzano, A. R., Trifir`o, D., Jean, N., Pera, G. L., Spadaccino, M., Massaron, L., and Nordio, C. (2020). An artificial intelligence approach to shadow rating. SSRN Electronic Journal, pages 1–18.[Rahayu et al., 2010] Rahayu, S. P., Zain, J. M., Embong, A., and Purnami, S. W. (2010). Credit risk classification using kernel logistic regression with optimal parameter. pages 602–605. Export Date: 26 September 2019.[Ribeiro and Chen, 2017] Ribeiro, B. and Chen, N. (2017). Financial credit risk assess- ment via learning-based hashing. Intelligent Decision Technologies, 11:177–186.[Rom, 2009] Rom, M. C. (2009). The credit rating agencies and the subprime mess: Greedy, ignorant, and stressed?[Rudin, 2019] Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead.[Ryser and Denzler, 2009] Ryser, M. and Denzler, S. (2009). Selecting credit rating mo- dels: A cross-validation-based comparison of discriminatory power. Financial Markets and Portfolio Management, 23:187–203.[Saitoh, 2016] Saitoh, F. (2016). Predictive modeling of corporate credit ratings using a semi-supervised random forest regression. volume 2016-December, pages 429–433.[Salehi and Mansoury, 2011] Salehi, M. and Mansoury, A. (2011). An evaluation of iranian banking system credit risk: Neural network and logistic regression approach. International Journal of Physical Sciences, 6:6082–6090.[Santos, 2007] Santos, K. (2007). Corporate credit ratings: a quick guide. 44:45–49.[Selau and Ribeiro, 2011] Selau, L. P. R. and Ribeiro, J. L. D. (2011). A systematic approach to construct credit risk forecast models. Pesquisa Operacional, 31:41–56.[Sitt and Wu, 2011] Sitt, M. and Wu, T. (2011). Evaluation of credit risk. pages 1–5.[SP, 2021] SP (2021). Guide to credit rating essentials what are credit ratings and how do they work?[Studer et al., 2021] Studer, S., Bui, T. B., Drescher, C., Hanuschkin, A., Winkler, L., Peters, S., and M ̈uller, K.-R. (2021). Towards crisp-ml(q): A machine learning process model with quality assurance methodology. Machine Learning and Knowledge Extraction 2021, Vol. 3, Pages 392-413, 3:392–413.[Truong et al., 2019] Truong, A., Walters, A., Goodsitt, J., Hines, K., Bruss, B., and Farivar, R. (2019). Towards automated machine learning: Evaluation and comparison of automl approaches and tools.[Tsai and Chen, 2010] Tsai, C.-F. and Chen, M.-L. (2010). Credit rating by hybrid machine learning techniques. Applied Soft Computing Journal, 10:374–380.[Turkson et al., 2016] Turkson, R. E., Baagyere, E. Y., and Wenya, G. E. (2016). A ma- chine learning approach for predicting bank credit worthiness. pages 81–87.[Twala, 2009] Twala, B. (2009). Combining classifiers for credit risk prediction. Journal of Systems Science and Systems Engineering, 18:292–311.[Vahid and Ahmadi, 2016] Vahid, P. R. and Ahmadi, A. (2016). Modeling corporate custo- mers’ credit risk considering the ensemble approaches in multiclass classification: Evidence from iranian corporate credits. Journal of Credit Risk, 12:71–95.[Vidovic and Yue, 2020] Vidovic, L. and Yue, L. (2020). Machine learning and credit risk modelling machine learning in finance.[Wang et al., 2012] Wang, J., Hedar, A.-R., Wang, S., and Ma, J. (2012). Rough set and scatter search metaheuristic based feature selection for credit scoring. Expert Systems with Applications, 39:6123–6128.[Wu, 2010] Wu, Y. (2010). Research of credit sale risk evaluation based on bp neural network.[Yao, 2009] Yao, P. (2009). Credit scoring using ensemble machine learning. volume 3, pages 244–246.[Yao et al., 2018] Yao, Q., Wang, M., Chen, Y., Dai, W., Li, Y.-F., Tu, W.-W., Yang, Q., and Yu, Y. (2018). Taking human out of learning applications: A survey on automated machine learning.[Ye et al., 2008] Ye, Y., Liu, S., and Li, J. (2008). A multiclass machine learning approach to credit rating prediction. Proceedings - International Symposium on Information Pro- cessing, ISIP 2008 and International Pacific Workshop on Web Mining and Web-Based Application, WMWA 2008, pages 57–61.[Yi, 2009] Yi, J. (2009). Credit scoring model based on the decision tree and the simulated annealing algorithm. volume 4, pages 18–22.[Zhang et al., 2009] Zhang, C., Li, Y., Liu, M., and Liu, Z. (2009). An enhanced approach for investment risk forecasting of electric power projects. volume 1, pages 29–32.[Zhao and Chen, 2009] Zhao, S.-F. and Chen, L.-C. (2009). The bp neural networks appli- cations in bank credit risk management system. pages 527–532.[Zhu et al., 2010] Zhu, C., Zhan, Y., and Jia, S. (2010). Research on bp neural network evaluation model of credit risk of bank clients.EstudiantesInvestigadoresMaestrosPúblico generalORIGINAL1031151220.2022.pdf1031151220.2022.pdfTesis de Maestría en Ingeniería - Ingeniería de Sistemas y Computaciónapplication/pdf1333673https://repositorio.unal.edu.co/bitstream/unal/81669/3/1031151220.2022.pdf0e24f2f68f971b5d796bcf2966cb157fMD53LICENSElicense.txtlicense.txttext/plain; charset=utf-84074https://repositorio.unal.edu.co/bitstream/unal/81669/4/license.txt8153f7789df02f0a4c9e079953658ab2MD54THUMBNAIL1031151220.2022.pdf.jpg1031151220.2022.pdf.jpgGenerated Thumbnailimage/jpeg4573https://repositorio.unal.edu.co/bitstream/unal/81669/5/1031151220.2022.pdf.jpg0b59ebeebfa668eb7134d5453a3fc97eMD55unal/81669oai:repositorio.unal.edu.co:unal/816692023-08-05 23:04:04.883Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.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

Modelo interpretable de aprendizaje de máquina para la predicción de calificaciones crediticias corporativas

Publicaciones similares