Machine Learning algorithms are widely used by lenders in risk early warning models. With Machine Learning, the risk levels of individual and corporate customers are determined at the account and customer level. Lenders want to manage risk by evaluating the payment performance of customer or account with the help of Machine Learning algorithms. Banks, which have an important place among lenders, develop risk early warning models with the help of learning algorithms using customer information. In the development process of risk early warning models, while banks generally use customer information and credit bureau information for the individual segment, they use financial, non-financial and behaviour-based information for the corporate segment. In this study, it is planned to develop a risk early model for customers in corporate service segment. For the customers of corporate service segment, Balance Sheet and Income Statement items were used and the financial ratios were calculated for risk early warning models. In the development of risk early warning models, Mutual Information method was used as a novel feature selection approach and Support Vector Machine method (linear function, radial basis function and sigmoid function) was used as a supervised learning approach. By changing the neighbourhood metric (k), important patterns were discovered with the Mutual Information method in feature selection process. The optimal C and gamma parameters for Support Vector Machine models have been tried to be determined with the Genetic Algorithm, which is among the Meta-Heuristic algorithms. In order to find the optimal metrics in this study, the metric values for all parameters of the SVM model (function specific) have been kept quite wide. In this dataset of corporate service customers, the small neighbourhood metric has been found to have a significant impact on model learning and performance.
Farooq, U., Jibran Qamar, M. A., & Haque, A. (2018). A three-stage dynamic model of financial distress. Managerial Finance, 44(9), 1101–1116. https://doi.org/10.1108/MF-07-2017-0244
Kumbure, M. M., Lohrmann, C., Luukka, P., & Porras, J. (2022). Machine learning techniques and data for stock market forecasting: A literature review. Expert Systems with Applications, 197, 116659. https://doi.org/10.1016/j.eswa.2022.116659
Geršl, A., & Jašová, M. (2018). Credit-based early warning indicators of banking crises in emerging markets. Economic Systems, 42(1), 18–31. https://doi.org/10.1016/j.ecosys.2017.05.004
Shen, C., Lee, Y., & Fang, H. (2020). Predicting banking crises based on credit, housing and capital booms. International Finance, 23(3), 472–505. https://doi.org/10.1111/infi.12367
Zhang, C., Wang, Z., & Lv, J. (2022). Research on early warning of agricultural credit and guarantee risk based on deep learning. Neural Computing and Applications, 34(9), 6673–6682. https://doi.org/10.1007/s00521-021-06114-3
Feng, Q., Chen, H., & Jiang, R. (2021). Analysis of early warning of corporate financial risk via deep learning artificial neural network. Microprocessors and Microsystems, 87, 104387. https://doi.org/10.1016/j.micpro.2021.104387
Kou, G., Xu, Y., Peng, Y., Shen, F., Chen, Y., Chang, K., & Kou, S. (2021). Bankruptcy prediction for SMEs using transactional data and two-stage multiobjective feature selection. Decision Support Systems, 140, 113429. https://doi.org/10.1016/j.dss.2020.113429
Du, G., Liu, Z., & Lu, H. (2021). Application of innovative risk early warning mode under big data technology in Internet credit financial risk assessment. Journal of Computational and Applied Mathematics, 386, 113260. https://doi.org/10.1016/j.cam.2020.113260
Wen, C., Yang, J., Gan, L., & Pan, Y. (2021). Big data driven Internet of Things for credit evaluation and early warning in finance. Future Generation Computer Systems, 124, 295–307. https://doi.org/10.1016/j.future.2021.06.003
Rosa, N. L. (2020). Analysing Financial Performance: Using Integrated Ratio Analysis (1st ed.).Routledge. https://doi.org/10.4324/9781003092575
Ravisankar, P., Ravi, V., Raghava Rao, G., & Bose, I. (2011). Detection of Financial Statement Fraud and Feature Selection using Data Mining Techniques. Decision Support Systems, 50(2), 491–500. https://doi.org/10.1016/j.dss.2010.11.006
Zhang, J. (2020). Investment risk model based on intelligent fuzzy neural network and VaR. Journal of Computational and Applied Mathematics, 371, 112707. https://doi.org/10.1016/j.cam.2019.112707
Lin, M. (2022). Innovative Risk Early Warning Model under Data Mining Approach in Risk Assessment of Internet Credit Finance. Computational Economics, 59(4), 1443–1464. https://doi.org/10.1007/s10614-021-10180-z
Lahmiri, S., Bekiros, S., Giakoumelou, A., & Bezzina, F. (2020). Performance assessment of ensemble learning systems in financial data classification. Intelligent Systems in Accounting, Finance and Management, 27(1), 3–9. https://doi.org/10.1002/isaf.1460
Bhatore, S., Mohan, L., & Reddy, Y. R. (2020). Machine Learning Techniques for Credit Risk Evaluation: A Systematic Literature Review. Journal of Banking and Financial Technology, 4(1), 111–138. https://doi.org/10.1007/s42786-020-00020-3
Zhang, W., He, H., & Zhang, S. (2019). A Novel Multi-Stage Hybrid Model with Enhanced Multi-Population Niche Genetic Algorithm: An Application in Credit Scoring. Expert Systems with Applications, 121, 221–232. https://doi.org/10.1016/j.eswa.2018.12.020
Catullo, E., Gallegati, M., & Palestrini, A. (2015). Towards a credit network based early warning indicator for crises. Journal of Economic Dynamics and Control, 50, 78–97. https://doi.org/10.1016/j.jedc.2014.08.011
Luo, C., Wu, D., & Wu, D. (2017). A deep learning approach for credit scoring using credit default swaps. Engineering Applications of Artificial Intelligence, 65, 465–470. https://doi.org/10.1016/j.engappai.2016.12.002
Hilal, W., Gadsden, S. A., & Yawney, J. (2022). Financial Fraud: A Review of Anomaly Detection Techniques and Recent Advances. Expert Systems with Applications, 193, 116429. https://doi.org/10.1016/j.eswa.2021.116429
Bequé, A., & Lessmann, S. (2017). Extreme Learning Machines for Credit Scoring: An Empirical Evaluation. Expert Systems with Applications, 86, 42–53. https://doi.org/10.1016/j.eswa.2017.05.050
Koutanaei, F. N., Sajedi, H., & Khanbabaei, M. (2015). A hybrid data mining model of feature selection algorithms and ensemble learning classifiers for credit scoring. Journal of Retailing and Consumer Services, 27, 11–23. https://doi.org/10.1016/j.jretconser.2015.07.003
Nguyen, M. H., & de la Torre, F. (2010). Optimal feature selection for support vector machines. Pattern Recognition, 43(3), 584–591. https://doi.org/10.1016/j.patcog.2009.09.003
Jadhav, S., He, H., & Jenkins, K. (2018). Information Gain Directed Genetic Algorithm Wrapper Feature Selection for Credit Rating. Applied Soft Computing, 69, 541–553. https://doi.org/10.1016/j.asoc.2018.04.033
Vijayanand, R., Devaraj, D., & Kannapiran, B. (2018). Intrusion Detection System for Wireless Mesh Network using Multiple Support Vector Machine Classifiers with Genetic-Algorithm-Based Feature Selection. Computers & Security, 77,304–314. https://doi.org/10.1016/j.cose.2018.04.010
Talbi, E.-G. (2009). Metaheuristics: From Design To Implementation. John Wiley & Sons.
Manurung, J., Mawengkang, H., & Zamzami, E. (2017). Optimizing Support Vector Machine Parameters with Genetic Algorithm for Credit Risk Assessment. Journal of Physics: Conference Series, 930, 012026. https://doi.org/10.1088/1742-6596/930/1/012026
İlhan, İ., & Tezel, G. (2013). A genetic algorithm–support vector machine method with parameter optimization for selecting the tag SNPs. Journal of Biomedical Informatics, 46(2), 328–340. https://doi.org/10.1016/j.jbi.2012.12.002
Onay, C., & Öztürk, E. (2018). A review of credit scoring research in the age of Big Data. Journal of Financial Regulation and Compliance, 26(3), 382–405. https://doi.org/10.1108/JFRC-06-2017-0054
Işık, M. (2023). Dataset. Işık, Muhammed (2023), “Early Warning Model Dataset for Corporate Segment”, Mendeley Data, V5, doi: 10.17632/pp599dy9c8.5
Bishop, C.M., 2006. Pattern recognition and machine learning, Information science and statistics. Springer, New York
Su, X., Li, L., Shi, F., & Qian, H. (2018). Research on the Fusion of Dependent Evidence Based on Mutual Information. IEEE Access, 6, 71839–71845. https://doi.org/10.1109/Access.2018.2882545
Barraza, N., Moro, S., Ferreyra, M., & de la Peña, A. (2019). Mutual Information and Sensitivity Analysis for Feature Selection in Customer Targeting: A Comparative Study. Journal of Information Science, 45(1), 53–67. https://doi.org/10.1177/0165551518770967
Yan, C., Kang, X., Li, M., & Wang, J. (2021). A Novel Feature Selection Method on Mutual Information and Improved Gravitational Search Algorithm for High Dimensional Biomedical Data. 2021 13th International Conference on Computer and Automation Engineering (ICCAE), 24–30. https://doi.org/10.1109/ICCAE51876.2021.9426130
Risk Erken Uyarı Modellerinde Destek Vektör Makinesinin Genetik Algoritma ile Parametre Optimizasyonu
Makine Öğrenimi ve Derin Öğrenme algoritmaları, kredi verenler tarafından risk erken uyarı modellerinde yaygın olarak kullanılmaktadır. Makine Öğrenmesi ve Derin Öğrenme algoritmaları ile bireysel ve kurumsal müşterilerin risk seviyeleri hesap ve müşteri bazında belirlenmektedir. Kredi verenler, müşterinin veya hesabın ödeme performansını Makine Öğrenmesi ve Derin Öğrenme algoritmaları yardımıyla değerlendirerek riski yönetmek ister. Kredi verenler arasında önemli bir yere sahip olan bankalar, müşteri bilgilerini kullanarak öğrenme algoritmaları yardımıyla risk erken uyarı modelleri geliştirmektedirler. Risk erken uyarı modellerinin geliştirilmesi sürecinde bankalar bireysel segment için genellikle müşteri bilgileri ve kredi bürosu bilgilerini kullanırken, kurumsal segment için finansal, finansal olmayan ve davranış bazlı bilgileri kullanmaktadırlar. Bu çalışmada kurumsal hizmet segmentindeki müşterilere yönelik bir risk erken modelinin geliştirilmesi planlanmaktadır. Kurumsal hizmet segmentindeki müşteriler için Bilanço ve Gelir Tablosu kalemleri kullanılmıştır. Bu tablolar kullanılarak risk erken uyarı modelleri için finansal rasyolar hesaplanmıştır. Risk erken uyarı modellerinin geliştirilmesinde yeni bir özellik seçme yaklaşımı olarak Karşılıklı Bilgi yöntemi, denetimli öğrenme yaklaşımı olarak ise Destek Vektör Makinesi yöntemi (doğrusal fonksiyon, radyal temel fonksiyon ve sigmoid fonksiyon) kullanılmıştır. Komşuluk metriği (k) değiştirilerek, Karşılıklı Bilgi yöntemiyle özellik seçimi sürecinde önemli örüntüler keşfedilmiştir. Meta-sezgisel algoritmalar arasında yer alan Genetik Algoritma ile Destek Vektör Makinesi modelleri için en uygun C ve gamma parametreleri belirlenmeye çalışılmıştır. Bu veri seti için küçük komşuluk ölçüsünün modellerin öğrenme ve performansları üzerinde önemli bir etkiye sahip olduğu sonucuna varılmıştır.
Bu çalışmanın özgün bir çalışma olduğunu ve tüm aşamalarında bilimsel etik, ilke ve kurallara uygun
davrandığımı beyan ederim.
Kaynakça
Farooq, U., Jibran Qamar, M. A., & Haque, A. (2018). A three-stage dynamic model of financial distress. Managerial Finance, 44(9), 1101–1116. https://doi.org/10.1108/MF-07-2017-0244
Kumbure, M. M., Lohrmann, C., Luukka, P., & Porras, J. (2022). Machine learning techniques and data for stock market forecasting: A literature review. Expert Systems with Applications, 197, 116659. https://doi.org/10.1016/j.eswa.2022.116659
Geršl, A., & Jašová, M. (2018). Credit-based early warning indicators of banking crises in emerging markets. Economic Systems, 42(1), 18–31. https://doi.org/10.1016/j.ecosys.2017.05.004
Shen, C., Lee, Y., & Fang, H. (2020). Predicting banking crises based on credit, housing and capital booms. International Finance, 23(3), 472–505. https://doi.org/10.1111/infi.12367
Zhang, C., Wang, Z., & Lv, J. (2022). Research on early warning of agricultural credit and guarantee risk based on deep learning. Neural Computing and Applications, 34(9), 6673–6682. https://doi.org/10.1007/s00521-021-06114-3
Feng, Q., Chen, H., & Jiang, R. (2021). Analysis of early warning of corporate financial risk via deep learning artificial neural network. Microprocessors and Microsystems, 87, 104387. https://doi.org/10.1016/j.micpro.2021.104387
Kou, G., Xu, Y., Peng, Y., Shen, F., Chen, Y., Chang, K., & Kou, S. (2021). Bankruptcy prediction for SMEs using transactional data and two-stage multiobjective feature selection. Decision Support Systems, 140, 113429. https://doi.org/10.1016/j.dss.2020.113429
Du, G., Liu, Z., & Lu, H. (2021). Application of innovative risk early warning mode under big data technology in Internet credit financial risk assessment. Journal of Computational and Applied Mathematics, 386, 113260. https://doi.org/10.1016/j.cam.2020.113260
Wen, C., Yang, J., Gan, L., & Pan, Y. (2021). Big data driven Internet of Things for credit evaluation and early warning in finance. Future Generation Computer Systems, 124, 295–307. https://doi.org/10.1016/j.future.2021.06.003
Rosa, N. L. (2020). Analysing Financial Performance: Using Integrated Ratio Analysis (1st ed.).Routledge. https://doi.org/10.4324/9781003092575
Ravisankar, P., Ravi, V., Raghava Rao, G., & Bose, I. (2011). Detection of Financial Statement Fraud and Feature Selection using Data Mining Techniques. Decision Support Systems, 50(2), 491–500. https://doi.org/10.1016/j.dss.2010.11.006
Zhang, J. (2020). Investment risk model based on intelligent fuzzy neural network and VaR. Journal of Computational and Applied Mathematics, 371, 112707. https://doi.org/10.1016/j.cam.2019.112707
Lin, M. (2022). Innovative Risk Early Warning Model under Data Mining Approach in Risk Assessment of Internet Credit Finance. Computational Economics, 59(4), 1443–1464. https://doi.org/10.1007/s10614-021-10180-z
Lahmiri, S., Bekiros, S., Giakoumelou, A., & Bezzina, F. (2020). Performance assessment of ensemble learning systems in financial data classification. Intelligent Systems in Accounting, Finance and Management, 27(1), 3–9. https://doi.org/10.1002/isaf.1460
Bhatore, S., Mohan, L., & Reddy, Y. R. (2020). Machine Learning Techniques for Credit Risk Evaluation: A Systematic Literature Review. Journal of Banking and Financial Technology, 4(1), 111–138. https://doi.org/10.1007/s42786-020-00020-3
Zhang, W., He, H., & Zhang, S. (2019). A Novel Multi-Stage Hybrid Model with Enhanced Multi-Population Niche Genetic Algorithm: An Application in Credit Scoring. Expert Systems with Applications, 121, 221–232. https://doi.org/10.1016/j.eswa.2018.12.020
Catullo, E., Gallegati, M., & Palestrini, A. (2015). Towards a credit network based early warning indicator for crises. Journal of Economic Dynamics and Control, 50, 78–97. https://doi.org/10.1016/j.jedc.2014.08.011
Luo, C., Wu, D., & Wu, D. (2017). A deep learning approach for credit scoring using credit default swaps. Engineering Applications of Artificial Intelligence, 65, 465–470. https://doi.org/10.1016/j.engappai.2016.12.002
Hilal, W., Gadsden, S. A., & Yawney, J. (2022). Financial Fraud: A Review of Anomaly Detection Techniques and Recent Advances. Expert Systems with Applications, 193, 116429. https://doi.org/10.1016/j.eswa.2021.116429
Bequé, A., & Lessmann, S. (2017). Extreme Learning Machines for Credit Scoring: An Empirical Evaluation. Expert Systems with Applications, 86, 42–53. https://doi.org/10.1016/j.eswa.2017.05.050
Koutanaei, F. N., Sajedi, H., & Khanbabaei, M. (2015). A hybrid data mining model of feature selection algorithms and ensemble learning classifiers for credit scoring. Journal of Retailing and Consumer Services, 27, 11–23. https://doi.org/10.1016/j.jretconser.2015.07.003
Nguyen, M. H., & de la Torre, F. (2010). Optimal feature selection for support vector machines. Pattern Recognition, 43(3), 584–591. https://doi.org/10.1016/j.patcog.2009.09.003
Jadhav, S., He, H., & Jenkins, K. (2018). Information Gain Directed Genetic Algorithm Wrapper Feature Selection for Credit Rating. Applied Soft Computing, 69, 541–553. https://doi.org/10.1016/j.asoc.2018.04.033
Vijayanand, R., Devaraj, D., & Kannapiran, B. (2018). Intrusion Detection System for Wireless Mesh Network using Multiple Support Vector Machine Classifiers with Genetic-Algorithm-Based Feature Selection. Computers & Security, 77,304–314. https://doi.org/10.1016/j.cose.2018.04.010
Talbi, E.-G. (2009). Metaheuristics: From Design To Implementation. John Wiley & Sons.
Manurung, J., Mawengkang, H., & Zamzami, E. (2017). Optimizing Support Vector Machine Parameters with Genetic Algorithm for Credit Risk Assessment. Journal of Physics: Conference Series, 930, 012026. https://doi.org/10.1088/1742-6596/930/1/012026
İlhan, İ., & Tezel, G. (2013). A genetic algorithm–support vector machine method with parameter optimization for selecting the tag SNPs. Journal of Biomedical Informatics, 46(2), 328–340. https://doi.org/10.1016/j.jbi.2012.12.002
Onay, C., & Öztürk, E. (2018). A review of credit scoring research in the age of Big Data. Journal of Financial Regulation and Compliance, 26(3), 382–405. https://doi.org/10.1108/JFRC-06-2017-0054
Işık, M. (2023). Dataset. Işık, Muhammed (2023), “Early Warning Model Dataset for Corporate Segment”, Mendeley Data, V5, doi: 10.17632/pp599dy9c8.5
Bishop, C.M., 2006. Pattern recognition and machine learning, Information science and statistics. Springer, New York
Su, X., Li, L., Shi, F., & Qian, H. (2018). Research on the Fusion of Dependent Evidence Based on Mutual Information. IEEE Access, 6, 71839–71845. https://doi.org/10.1109/Access.2018.2882545
Barraza, N., Moro, S., Ferreyra, M., & de la Peña, A. (2019). Mutual Information and Sensitivity Analysis for Feature Selection in Customer Targeting: A Comparative Study. Journal of Information Science, 45(1), 53–67. https://doi.org/10.1177/0165551518770967
Yan, C., Kang, X., Li, M., & Wang, J. (2021). A Novel Feature Selection Method on Mutual Information and Improved Gravitational Search Algorithm for High Dimensional Biomedical Data. 2021 13th International Conference on Computer and Automation Engineering (ICCAE), 24–30. https://doi.org/10.1109/ICCAE51876.2021.9426130
Toplam 33 adet kaynakça vardır.
Ayrıntılar
Birincil Dil
İngilizce
Konular
Denetimli Öğrenme, Makine Öğrenmesi Algoritmaları, Veri Madenciliği ve Bilgi Keşfi, Evrimsel Hesaplama
Işık, M. (2024). The Parameter Optimization of Support Vector Machine with Genetic Algorithm in Risk Early Warning Models. International Journal of Advances in Engineering and Pure Sciences, 36(4), 354-366. https://doi.org/10.7240/jeps.1519469
AMA
Işık M. The Parameter Optimization of Support Vector Machine with Genetic Algorithm in Risk Early Warning Models. JEPS. Aralık 2024;36(4):354-366. doi:10.7240/jeps.1519469
Chicago
Işık, Muhammed. “The Parameter Optimization of Support Vector Machine With Genetic Algorithm in Risk Early Warning Models”. International Journal of Advances in Engineering and Pure Sciences 36, sy. 4 (Aralık 2024): 354-66. https://doi.org/10.7240/jeps.1519469.
EndNote
Işık M (01 Aralık 2024) The Parameter Optimization of Support Vector Machine with Genetic Algorithm in Risk Early Warning Models. International Journal of Advances in Engineering and Pure Sciences 36 4 354–366.
IEEE
M. Işık, “The Parameter Optimization of Support Vector Machine with Genetic Algorithm in Risk Early Warning Models”, JEPS, c. 36, sy. 4, ss. 354–366, 2024, doi: 10.7240/jeps.1519469.
ISNAD
Işık, Muhammed. “The Parameter Optimization of Support Vector Machine With Genetic Algorithm in Risk Early Warning Models”. International Journal of Advances in Engineering and Pure Sciences 36/4 (Aralık 2024), 354-366. https://doi.org/10.7240/jeps.1519469.
JAMA
Işık M. The Parameter Optimization of Support Vector Machine with Genetic Algorithm in Risk Early Warning Models. JEPS. 2024;36:354–366.
MLA
Işık, Muhammed. “The Parameter Optimization of Support Vector Machine With Genetic Algorithm in Risk Early Warning Models”. International Journal of Advances in Engineering and Pure Sciences, c. 36, sy. 4, 2024, ss. 354-66, doi:10.7240/jeps.1519469.
Vancouver
Işık M. The Parameter Optimization of Support Vector Machine with Genetic Algorithm in Risk Early Warning Models. JEPS. 2024;36(4):354-66.