Modelos para la evaluación de riego crediticio en el ámbito de la tecnología financiera: una revisión
Resumen
Esta revisión analiza una selección de artículos científicos sobre la implantación de sistemas de evaluación del riesgo de crédito para identificar las soluciones existentes, las más acertadas y las limitaciones y problemas en su desarrollo. Se adoptó la declaración PRISMA del siguiente modo: se formularon las preguntas de investigación, se definieron los criterios de inclusión, se seleccionaron las palabras clave y se diseñó la cadena de búsqueda. Por último, se calcularon varios estadísticos descriptivos de los artículos seleccionados. En los estudios seleccionados se identificaron 31 soluciones, entre métodos, modelos y algoritmos. Algunos de los modelos más utilizados se basan en técnicas de Inteligencia Artificial (IA), especialmente Redes Neuronales y Bosques Aleatorios. Se concluyó que las Redes Neuronales son las soluciones más eficientes, con precisiones medias superiores al 90 %, pero su desarrollo puede tener limitaciones. Estas soluciones deben implementarse teniendo en cuenta el contexto en el que se van a emplear.
Referencias bibliográficas
S. R. Lenka, S. K. Bisoy, R. Priyadarshini, J. Hota, and R. K. Barik, “An effective credit scoring model implementation by optimal feature selection scheme,” 2021 Int. Conf. Emerg. Smart Comput. Informatics (ESCI), Pune, India, 2021, pp. 106–109. https://doi.org/10.1109/ESCI50559.2021.9396911
H. Kvamme, N. Sellereite, K. Aas, and S. Sjursen, “Predicting mortgage default using convolutional neural networks,” Expert Syst. Appl., vol. 102, pp. 207–217, Jul. 2018. https://doi.org/10.1016/j.eswa.2018.02.029
S. Wen, B. Zeng, W. Liao, P. Wei, and Z. Pan, “Research and Design of Credit Risk Assessment System Based on Big Data and Machine Learning,” 2021 IEEE 6th Int. Conf. Big Data Analytics (ICBDA), Xiamen, China, 2021, pp. 9–13. https://doi.org/10.1109/ICBDA51983.2021.9403128
F. Wu, X. Su, Y. S. Ock, and Z. Wang, “Personal credit risk evaluation model of P2P online lending based on AHP,” Symmetry, vol. 13, no. 1, p. 83, Jan. 2021. https://doi.org/10.3390/sym13010083
J. Nourmohammadi-Khiarak, M.-R. Feizi-Derakhshi, F. Razeghi, S. Mazaheri, Y. Zamani-Harghalani, and R. Moosavi-Tayebi, “New hybrid method for feature selection and classification using meta-heuristic algorithm in credit risk assessment,” Iran J. Comput. Sci., vol. 3, pp. 1–11, Jun. 2020. https://doi.org/10.1007/s42044-019-00038-x
M. Wang and H. Ku, “Utilizing historical data for corporate credit rating assessment,” Expert Syst. Appl., vol. 165, p. 113925, Mar. 2021. https://doi.org/10.1016/j.eswa.2020.113925
S. Moradi and F. Mokhatab Rafiei, “A dynamic credit risk assessment model with data mining techniques: evidence from Iranian banks,” Financ. Innov., vol. 5, no. 15, Mar. 2019. https://doi.org/10.1186/s40854-019-0121-9
A. Fenerich et al., “Use of machine learning techniques in bank credit risk analysis,” Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería, vol. 36, no. 3, p. 40, Sep. 2020. https://doi.org/10.23967/J.RIMNI.2020.08.003
A. Wójcicka-Wójtowicz, A. Lyczkowska-Hanckowiak, and K. Maciej Piasecki, “Credit Risk Assessment by Ordered Fuzzy Numbers,” SSRN Electron. J., Nov. 2019. https://doi.org/10.2139/ssrn.3479218
A. Niu, B. Cai, and S. Cai, “Big Data Analytics for Complex Credit Risk Assessment of Network Lending Based on SMOTE Algorithm,” Complexity, vol. 2020, p. 8563030, Sep. 2020. https://doi.org/10.1155/2020/8563030
A. Agosto, P. Giudici, and T. Leach, “Spatial Regression Models to Improve P2P Credit Risk Management,” Front. Artif. Intell., vol. 2, May. 2019. https://doi.org/10.3389/frai.2019.00006
Y. Cao, “Internet financial supervision based on machine learning and improved neural network,” J. Intell. Fuzzy Syst., vol. 40, no. 4, pp. 7297–7308, Apr. 2021. https://doi.org/10.3233/JIFS-189555
C. Luo, “A comprehensive decision support approach for credit scoring,” Ind. Manag. Data Syst., vol. 120, no. 2, pp. 280–290, Oct. 2019. https://doi.org/10.1108/IMDS-03-2019-0182
A. A. Turjo, Y. Rahman, S. M. M. Karim, T. H. Biswas, I. Dewan, and M. I. Hossain, “CRAM: A Credit Risk Assessment Model by Analyzing Different Machine Learning Algorithms,” 4th International Conference on Information and Communications Technology, Yogyakarta, Indonesia, 2021 pp. 125–130. https://doi.org/10.1109/ICOIACT53268.2021.9563995
A. Wójcicka-Wójtowicz and K. Piasecki, “Application of the oriented fuzzy numbers in credit risk assessment,” Mathematics, vol. 9, no. 5, p. 535, Mar. 2021. https://doi.org/10.3390/math9050535
C. Yung-Chia, C. Kuei-Hu, and H. Yi-Hsuan, “A novel fuzzy credit risk assessment decision support system based on the python web framework,” J. Ind. Prod. Eng., vol. 37, no. 5, pp. 229–244, Jun. 2020. https://doi.org/10.1080/21681015.2020.1772385
S. Haloui and A. El Moudden, “An optimal prediction model’s credit risk: The implementation of the backward elimination and forward regression method,” International Journal of Advanced Computer Science and Applications, vol. 11, no. 2, p. 9549868, 2020. https://doi.org/10.14569/ijacsa.2020.0110259
H. Xie and Y. Shi, “A Big Data Technique for Internet Financial Risk Control,” Mob. Inf. Syst., vol. 2022, Jul. 2022. https://doi.org/10.1155/2022/9549868
L. Cheng-yong, D. Tian-yu, and M. Ling-xing, “The Prevention of Financial Legal Risks of B2B E-commerce Supply Chain,” Wirel. Commun. Mob. Comput., vol. 2022, p. 6154011, Jan. 2022. https://doi.org/10.1155/2022/6154011
Y. Li, “Credit risk prediction based on machine learning methods,” 14th Int. Conf. Comput. Sci. Education. Toronto, Canada, 2019 pp. 1011–1013. https://doi.org/10.1109/ICCSE.2019.8845444
A. Liberati et al., The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration, Journal of Clinical Epidemiology, vol. 62, no. 10, pp. e1-e34 Oct. 2009. https://doi.org/10.1016/j.jclinepi.2009.06.006
Y. Zhu, L. Zhou, C. Xie, W. Gang-Jin, and N. Truong. V, “Forecasting SMEs’ credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach,” Int. J. Prod. Econ., vol. 211, pp. 22–33, May. 2019. https://doi.org/10.1016/j.ijpe.2019.01.032
P. Pławiak, M. Abdar, and U. R. Acharya, “Application of new deep genetic cascade ensemble of SVM classifiers to predict the Australian credit scoring,” Appl. Soft Comput. J., vol. 84, p. 105740, Nov. 2019. https://doi.org/10.1016/j.asoc.2019.105740
X. Huang, X. Liu, and Y. Ren, “Enterprise credit risk evaluation based on neural network algorithm,” Cogn. Syst. Res., vol. 52, pp. 317–324, Dec. 2018. https://doi.org/10.1016/j.cogsys.2018.07.023
X. Ye, D. Lu-an, and D. Ma, “Loan evaluation in P2P lending based on Random Forest optimized by genetic algorithm with profit score,” Electron. Commer. Res. Appl., vol. 32, pp. 23–36, Nov-Dec. 2018. https://doi.org/10.1016/j.elerap.2018.10.004
K. Cheng et al., “SecureBoost: A Lossless Federated Learning Framework,” IEEE Intell. Syst., vol. 36, no. 6, pp. 87–98, Nov.-Dec. 2021. https://doi.org/10.1109/MIS.2021.3082561
K. Masmoudi, L. Abid, and A. Masmoudi, “Credit risk modeling using Bayesian network with a latent variable,” Expert Syst. Appl., vol. 127, pp. 157–166, Aug. 2019. https://doi.org/10.1016/j.eswa.2019.03.014
Y. Song, Y. Wang, X. Ye, D. Wang, Y. Yin, and Y. Wang, “Multi-view ensemble learning based on distance-to-model and adaptive clustering for imbalanced credit risk assessment in P2P lending,” Inf. Sci., vol. 525, pp. 182–204, Jul. 2020. https://doi.org/10.1016/j.ins.2020.03.027
D. Liang, T. Chih-Fong, D. An-Jie, and W. Eberle, “A novel classifier ensemble approach for financial distress prediction,” Knowl. Inf. Syst., vol. 54, pp. 437–462, May. 2018. https:/doi.org/10.1007/s10115-017-1061-1
Y. O. Serrano-Silva, Y. Villuendas-Rey, and C. Yáñez-Márquez, “Automatic feature weighting for improving financial Decision Support Systems,” Decis. Support Syst., vol. 107, pp. 78–87, Mar. 2018. https://doi.org/10.1016/j.dss.2018.01.005
G. De Tre, A. Hallez, and A. Bronselaer, “Performance optimization of object comparison,” Int. J. Intell. Syst., vol. 24, no. 10, pp. 1057–1076, Jul. 2009. https://doi.org/10.1002/int.20373
P. Mahata, G. Chandra. Mahata, and S. Kumar. De, “An economic order quantity model under two-level partial trade credit for time varying deteriorating items,” Int. J. Syst. Sci. Oper. Logist., vol. 7, no. 1, pp. 1–17, May. 2020. https://doi.org/10.1080/23302674.2018.1473526
Y. Li-Li, Q. Yi-Wen, H. Yuan, and R. Zhao-Jun, “A Convolutional Neural Network-Based Model for Supply Chain Financial Risk Early Warning,” Comput. Intell. Neurosci., vol. 2022, p. 7825597, Apr. 2022. https://doi.org/10.1155/2022/7825597
W. Cheng-Feng, H. Shian-Chang, C. Chei-Chang, and W. Yu-Min, “A predictive intelligence system of credit scoring based on deep multiple kernel learning,” Appl. Soft Comput., vol. 111, p. 107668, Nov. 2021. https://doi.org/10.1016/j.asoc.2021.107668
W. Yang and L. Gao, “A Study on RB-XGBoost Algorithm-Based e-Commerce Credit Risk Assessment Model,” J. Sensors, vol. 2021, p. 7066304, Oct. 2021. https://doi.org/10.1155/2021/7066304
S. Lahmiri, A. Giakoumelou, and S. Bekiros, “An adaptive sequential-filtering learning system for credit risk modeling,” Soft Comput., vol. 25, no. 13, pp. 8817–8824, May. 2021. https://doi.org/10.1007/s00500-021-05833-y
X. Ye, L. an Dong, and D. Ma, “Loan evaluation in P2P lending based on Random Forest optimized by genetic algorithm with profit score,” Electron. Commer. Res. Appl., vol. , pp. 23–36, Nov-Dec. 2018. https://doi.org/10.1016/j.elerap.2018.10.004
S. Luo, M. Xing, and J. Zhao, “Construction of Artificial Intelligence Application Model for Supply Chain Financial Risk Assessment,” Sci. Program., vol. 2022, p. 4194576, Jun. 2022. https://doi.org/10.1155/2022/4194576
H. Zeng, “Credit Risk Evaluation in Enterprise Financial Management by Using Convolutional Neural Network under the Construction of Smart City,” Secur. Commun. Networks., vol. 2022, p. 8532918, Aug. 2022. https://doi.org/10.1155/2022/8532918
A. Merćep, L. Mrčela, M. Birov, and Z. Kostanjčar, “Deep neural networks for behavioral credit rating,” Entropy, vol. 23, no. 1, Dec. 2021. https://doi.org/10.3390/e23010027
G. Yangyudongnanxin, “Financial Credit Risk Control Strategy Based on Weighted Random Forest Algorithm,” Scientific Programming, vol. 2021, p. 6276155, Oct. 2021. https://doi.org/10.1155/2021/6276155
Y. Xi and Q. Li, “Improved AHP Model and Neural Network for Consumer Finance Credit Risk Assessment,” Advances in Multimedia, vol. 2022, p. 9588486, Jul. 2022. https://doi.org/10.1155/2022/9588486
J. R. de Castro Vieira, F. Barboza, V. A. Sobreiro, and H. Kimura, “Machine learning models for credit analysis improvements: Predicting low-income families’ default,” Appl. Soft Comput. J., vol. 83, p. 105640, Oct. 2019. https://doi.org/10.1016/j.asoc.2019.105640
W. Liu, H. Fan, and M. Xia, “Multi-grained and multi-layered gradient boosting decision tree for credit scoring,” Appl. Intell., vol. 52, pp. 5325–5341, Mar. 2022. https://doi.org/10.1007/s10489-021-02715-6
B. Li, “Online Loan Default Prediction Model Based on Deep Learning Neural Network,” Computational Intelligence and Neuroscience, vol. 2022, p. 4276253, Aug. 2022. https://doi.org/10.1155/2022/4276253
M. Almutairi, F. Stahl, and M. Bramer, “ReG-Rules: An Explainable Rule-Based Ensemble Learner for Classification,” IEEE Access, vol. 9, pp. 52015–52035, Feb. 2021. https://doi.org/10.1109/ACCESS.2021.3062763
W. Liu, H. Fan, and M. Xia, “Step-wise multi-grained augmented gradient boosting decision trees for credit scoring,” Eng. Appl. Artif. Intell., vol. 97, p. 104036, Jan. 2021. https://doi.org/10.1016/j.engappai.2020.104036
M. Yin and G. Li, “Supply Chain Financial Default Risk Early Warning System Based on Particle Swarm Optimization Algorithm,” Mathematical Problems in Engineering, vol. 2022, p. 7255967, 2022. https://doi.org/10.1155/2022/7255967
Z. Hassani, M. Alambardar Meybodi, and V. Hajihashemi, “Credit Risk Assessment Using Learning Algorithms for Feature Selection,” Fuzzy Inf. Eng., vol. 12, no. 4, pp. 529–544, Jun. 2020. https://doi.org/10.1080/16168658.2021.1925021
L. Wang and H. Song, “E-Commerce Credit Risk Assessment Based on Fuzzy Neural Network,” Computational Intelligence and Neuroscience, vol. 2022, p. 3088915, Jan. 2022. https://doi.org/10.1155/2022/3088915
N. H. Putri, M. Fatekurohman, and I. M. Tirta, “Credit risk analysis using support vector machines algorithm,” J. Phys. Conf. Ser., vol. 1836, p. 012039, 2021. https://doi.org/10.1088/1742-6596/1836/1/012039
S. Barua, D. Gavandi, P. Sangle, L. Shinde, and J. Ramteke, “Swindle: Predicting the Probability of Loan Defaults using CatBoost Algorithm,” 5th Int. Conf. Comput. Methodol. Commun., Erode, India, 2021, pp. 1710–1715. https://doi.org/10.1109/ICCMC51019.2021.9418277
Y. Liu and L. Huang, “Supply chain finance credit risk assessment using support vector machine–based ensemble improved with noise elimination,” International Journal of Distributed Sensor Networks, vol. 16, no. 1, Feb. 2020. https://doi.org/10.1177/1550147720903631
A. L. Leal Fica, M. A. Aranguiz Casanova Y J. Gallegos Mardones. "Análisis De Riesgo Crediticio, Propuesta Del Modelo Credit Scoring". Redalyc, vol. 26, no. 1, pp.181-207, 2018. https://doi.org/10.18359/rfce.2666
Q. Liu, C. Wu, and L. Lou, “Evaluation research on commercial bank counterparty credit risk management based on new intuitionistic fuzzy method,” Soft Comput., vol. 22, pp. 5363–5375, Feb. 2018. https://doi.org/10.1007/s00500-018-3042-z
V. B. Djeundje and J. Crook, “Identifying hidden patterns in credit risk survival data using Generalised Additive Models,” Eur. J. Oper. Res., vol. 277, no. 1, pp. 366–376, Aug. 2019. https://doi.org/10.1016/j.ejor.2019.02.006
D. Mhlanga, “Financial inclusion in emerging economies: The application of machine learning and artificial intelligence in credit Risk assessment,” Int. J. Financ. Studies., vol. 9, no. 3, Jul. 2021. https://doi.org/10.3390/ijfs9030039
A. Dattachaudhuri, S. K. Biswas, S. Sarkar, A. N. Boruah, M. Chakraborty, and B. Purkayastha, “Transparent Neural based Expert System for Credit Risk (TNESCR): An Automated Credit Risk Evaluation System,” 2020 Int. Conf. Comput. Perform. Eval. ComPE, Shillong, India, 2020, pp. 013–017. https://doi.org/10.1109/ComPE49325.2020.9199998
P. Z. Lappas and A. N. Yannacopoulos, “A machine learning approach combining expert knowledge with genetic algorithms in feature selection for credit risk assessment,” Applied Soft Computing, vol. 107, p. 107391, Aug. 2021. https://doi.org/10.1016/j.asoc.2021.107391
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