1
College of Business Administration, American University in the Emirates, Dubai, UAE
(Noura.metawa@aue.ae)
2
College of Computer and Information Technology, American University in the Emirates, Dubai, UAE
(Mohamed.elhoseny@aue.ae)
Abstract :
Financial risk assessment becomes a hot research topic among financial firms or companies to assess the financial status and thereby avoid future crises. Earlier studies have focused on statistical models for the assessment of financial risks and the recently developed machine learning (ML) models find useful to improve the assessment performance. In this aspect, this study introduces a novel Butterfly Optimization based Feature Selection with Classification Model for Financial Risk Assessment (BOFS-CFRA) technique. The proposed BOFS-CFRA technique involves pre-processing at the primary stage to get rid of unwanted data. In addition, K-means clustering approach is developed to group the financial data into clusters. Then, the BOFS technique is applied to choose the subset of features from the clustered data. Finally, the classification of financial risks takes place by the use of functional link neural network (FLNN). In order to ensure the enhanced performance of the BOFS-CFRA technique, a series of simulations were carried out and the results are inspected under various measures. The simulation outcome portrayed the supremacy of the BOFS-CFRA technique over the other financial risk assessment models in terms of several performance measures.
Keywords :
Financial risk assessment , Classification , Feature selection , Butterfly optimization algorithm , FLNN.
References :
[1] Lee, I. and Shin, Y.J., 2020. Machine learning for enterprises: Applications, algorithm selection, and challenges. Business Horizons, 63(2), pp.157-170.
[2] Gupta, A. and Lohani, M.C., 2022. Comparative Analysis of Numerous Approaches in Machine Learning to Predict Financial Fraud in Big Data Framework. In Soft Computing: Theories and Applications (pp. 107-123). Springer, Singapore.
[3] Gupta, A. and Lohani, M.C., 2022. Comparative Analysis of Numerous Approaches in Machine Learning to Predict Financial Fraud in Big Data Framework. In Soft Computing: Theories and Applications (pp. 107-123). Springer, Singapore.
[4] Gu, S., Kelly, B. and Xiu, D., 2018. Empirical asset pricing via machine learning (No. w25398). National bureau of economic research.
[5] Chen, Y., Zheng, W., Li, W. and Huang, Y., 2021. Large group activity security risk assessment and risk early warning based on random forest algorithm. Pattern Recognition Letters, 144, pp.1-5.
[6] Addo, P.M., Guegan, D. and Hassani, B., 2018. Credit risk analysis using machine and deep learning models. Risks, 6(2), p.38.
[7] Paiva, F.D., Cardoso, R.T.N., Hanaoka, G.P. and Duarte, W.M., 2019. Decision-making for financial trading: A fusion approach of machine learning and portfolio selection. Expert Systems with Applications, 115, pp.635-655.
[8] Kunnathuvalappil Hariharan, N., 2017. Predictive model building for driver-based budgeting using machine learning. Predictive Model Building for Driver-Based Budgeting Using Machine Learning (June 5, 2017).
[9] Polak, P., Nelischer, C., Guo, H. and Robertson, D.C., 2020. “Intelligent” finance and treasury management: what we can expect. AI & SOCIETY, 35(3), pp.715-726.
[10] Henckaerts, R., Côté, M.P., Antonio, K. and Verbelen, R., 2021. Boosting insights in insurance tariff plans with tree-based machine learning methods. North American Actuarial Journal, 25(2), pp.255-285.
[11] Le, H.H. and Viviani, J.L., 2018. Predicting bank failure: An improvement by implementing a machine-learning approach to classical financial ratios. Research in International Business and Finance, 44, pp.16-25.
[12] Dumitrescu, E., Hue, S., Hurlin, C. and Tokpavi, S., 2021. Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects. European Journal of Operational Research.
[13] Roa, L., Rodríguez-Rey, A., Correa-Bahnsen, A. and Valencia, C., 2021. Supporting Financial Inclusion with Graph Machine Learning and Super-App Alternative Data. arXiv preprint arXiv:2102.09974.
[14] Chang, Y.C., Chang, K.H. and Wu, G.J., 2018. Application of eXtreme gradient boosting trees in the construction of credit risk assessment models for financial institutions. Applied Soft Computing, 73, pp.914-920.
[15] Lee, T.K., Cho, J.H., Kwon, D.S. and Sohn, S.Y., 2019. Global stock market investment strategies based on financial network indicators using machine learning techniques. Expert Systems with Applications, 117, pp.228-242.
[16] Munkhdalai, L., Munkhdalai, T., Namsrai, O.E., Lee, J.Y. and Ryu, K.H., 2019. An empirical comparison of machine-learning methods on bank client credit assessments. Sustainability, 11(3), p.699.
[17] Zhu, Y., Zhou, L., Xie, C., Wang, G.J. and Nguyen, T.V., 2019. Forecasting SMEs' credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach. International Journal of Production Economics, 211, pp.22-33.
[18] 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, pp.301-315.
[19] Na, S., Xumin, L. and Yong, G., 2010, April. Research on k-means clustering algorithm: An improved k-means clustering algorithm. In 2010 Third International Symposium on intelligent information technology and security informatics (pp. 63-67). Ieee.
[20] Arora, S. and Singh, S., 2019. Butterfly optimization algorithm: a novel approach for global optimization. Soft Computing, 23(3), pp.715-734.
[21] https://transpireonline.blog/2020/04/15/butterfly-optimization-algorithmboa-to-solve-engineering-problems/
[22] Dehuri, S., Roy, R., Cho, S.B. and Ghosh, A., 2012. An improved swarm optimized functional link artificial neural network (ISO-FLANN) for classification. Journal of Systems and Software, 85(6), pp.1333-1345.
[23] Al-Jumeily, D., Ghazali, R. and Hussain, A., 2014. Predicting physical time series using dynamic ridge polynomial neural networks. PLOS one, 9(8), p.e105766.
[24] Acharya, S., Pustokhina, I.V., Pustokhin, D.A., Geetha, B.T., Joshi, G.P., Nebhen, J., Yang, E. and Seo, C., 2021. An improved gradient boosting tree algorithm for financial risk management. Knowledge Management Research & Practice, pp.1-12.
| Style | # |
|---|---|
| MLA | Noura Metawa, Mohamed Elhoseny. "Feature Selection Optimization Model for Business Risk Assessment Model." American Journal of Business and Operations Research, Vol. 2, No. 1, 2021 ,PP. 51-64 (Doi : https://doi.org/10.54216/AJBOR.020104) |
| APA | Noura Metawa, Mohamed Elhoseny. (2021). Feature Selection Optimization Model for Business Risk Assessment Model. Journal of American Journal of Business and Operations Research, 2 ( 1 ), 51-64 (Doi : https://doi.org/10.54216/AJBOR.020104) |
| Chicago | Noura Metawa, Mohamed Elhoseny. "Feature Selection Optimization Model for Business Risk Assessment Model." Journal of American Journal of Business and Operations Research, 2 no. 1 (2021): 51-64 (Doi : https://doi.org/10.54216/AJBOR.020104) |
| Harvard | Noura Metawa, Mohamed Elhoseny. (2021). Feature Selection Optimization Model for Business Risk Assessment Model. Journal of American Journal of Business and Operations Research, 2 ( 1 ), 51-64 (Doi : https://doi.org/10.54216/AJBOR.020104) |
| Vancouver | Noura Metawa, Mohamed Elhoseny. Feature Selection Optimization Model for Business Risk Assessment Model. Journal of American Journal of Business and Operations Research, (2021); 2 ( 1 ): 51-64 (Doi : https://doi.org/10.54216/AJBOR.020104) |
| IEEE | Noura Metawa, Mohamed Elhoseny, Feature Selection Optimization Model for Business Risk Assessment Model, Journal of American Journal of Business and Operations Research, Vol. 2 , No. 1 , (2021) : 51-64 (Doi : https://doi.org/10.54216/AJBOR.020104) |