The Impacts of Machine Learning in Financial Crisis Prediction
Keywords:Machine learning, financial crisis prediction, bankruptcy, creditworthiness
The most complicated and expected issue to be handled in corporate firms, small-scale businesses, and investors’ even governments are financial crisis prediction. To this effect, it was of interest to us to investigate the current impact of the newly employed technique that is machine learning (ML) to handle this menace in all spheres of business both private and public. The study uses systematic literature assessment to study the impact of ML in financial crisis prediction. From the selected works of literature, we have been able to establish the important role play by this method in the prediction of bankruptcy and creditworthiness that was not handled appropriately by others method. Also, machine learning helps in data handling, data privacy, and confidentiality. This study presents a leading approach to achieving financial growth and plasticity in corporate organizations. We, therefore, recommend a real-time study to investigate the impact of ML in FCP.
Andini, M., Boldrini, M., Ciani, E., de Blasio, G., D’Ignazio, A. and Paladini, A. (2019). Machine learning in the service of policy targeting: the case of public credit guarantees. Temi di discussion (working paper), 1-88.
Back, B., Laitinen, T. and Sere, K. (1996). Neural networks and genetic algorithms for bankruptcy predictions. Expert Syst. Appl., 11, 407–413.
Bensic, M., Sarlija, N. and Zekic-Susac, M. (2005). Modelling small-business credit scoring by using logistic regression, neural networks and decision trees. Intell. Syst. Account., Finance Manag, 13, 133–150.
Bhatnagar, V., Singh, G., Kumar, G. and Gupta, R. (2020). Internet of Things in Smart Agriculture: Applications and Open Challenges. International Journal of Students’ Research in Technology & Management, 8(1): 11-17.
Boritz, J. E. and Kennedy, D. B. (1995). Effectiveness of neural network types for prediction of business failure. Expert Syst. Appl., 9, 503–512.
Boyacioglu, M.A., Kara, Y. and Baykan, O. K. (2009). Predicting bank financial failures using neural networks, support vector machines and multivariate statistical methods: A comparative analysis in the sample of savings deposit insurance fund (SDIF) transferred banks in Turkey. Expert Syst. Appl., 36, 3355–3366.
Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2): 123–140.
Chi, L. C. and Tang, T. C. (2006). Bankruptcy prediction: Application of logit analysis in export credit risks. Australian J. Manag., 31(1):17–28.
Cho, S., Hong, H. and Ha, B.C. (2010). A hybrid approach based on the combination of variable selection using decision trees and case-based reasoning using the Mahalanobis distance: For bankruptcy prediction. Expert Syst. Appl., 37, 3482–3488.
Christy, S.A. and Arunkumar, R. (2019). Machine Learning Based Classification Models for Financial Crisis Prediction. International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, 8(4): 4887-4893.
Cinca, S. and Gutirrez-Nieto, B. (2013). Partial least square discriminant analysis for bankruptcy prediction, Decis. Support Syst., 54(3): 1245–1255.
Ding, Y., Song, X. and Zen, Y. (2008). Forecasting financial condition of Chinese listed companies based on support vector machine. Expert Syst. Appl., 34, 3081–3089.
Doerr, S., Gambacorta, L. and Serena, J. M. (2021). Big data and machine learning in central banking. BIS Working Papers, March 2021. 930, 1-26.
Donepudi, P. K. (2019). Automation and Machine Learning in Transforming the Financial Industry. Asian Business Review, 9(3), 129-138. https://doi.org/10.18034/abr.v9i3.494
Donepudi, P. K. (2014). Technology Growth in Shipping Industry: An Overview. American Journal of Trade and Policy, 1(3), 137-142. https://doi.org/10.18034/ajtp.v1i3.503
Duda, R. O., Hart, P. E. and Stork, D. G. (2001). Pattern Classification, 2nd ed. New York: Wiley.
Dybå, T., Dingsøyr, T. (2008). Empirical studies of agile software development: A systematic review. Inf. Softw. Technol., 50, 833–859.
Freund, Y. and Schapire, R. E. (1996). Experiments with a new boosting algorithm. In Proc. Int. Conf. Mach. Learning, Bari, Italy, pp. 148– 156.
Frosyniotis, D., Stafylopatis, A. and Likas, A. (2003). A divide-and-conquer method for multi-net classifiers. J. Pattern Analysis Appl., (6)1, pp. 32–40.
Jang, J. S., Sun, C. T. and Mizutani, E. (1996). Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, NJ: Prentice-Hall.
Keele, S. (2016). Guidelines for Performing Systematic Literature Reviews in Software Engineering; Technical Report 2016, Ver. 2.3 Technical Report; EBSE: Durham, UK.
Kittler, J., Hatef, M., Duin, R. P. W. and Matas, J. (1998). On combining classifiers. IEEE Trans. Pattern Anal. Mach. Intell., 20(3): 226– 239.
Kleinberg, J., Ludwig, J., Mullainathan, S., and Obermeyer, Z. (2015). Prediction policy problems. American Economic Review, 105(5): 491-495.
Lacher, R. C., Coats, P. K., Sharma, S. C. and Fant, L. F. (1995). A neural network for classifying the financial health of a firm. Eur. J. Oper. Res., 85, 53–65.
Lee, T. S., Chiu, C. C., Chou, Y. C. and Lu, C. J. (2006). Mining the customer credit using classification and regression tree and multivariate adaptive regression splines. Comput. Statist. Data Anal., 50, 1113–1130.
Lee, Y. C. (2007). Application of support vector machines to corporate credit rating prediction. Expert Syst. Appl., 33, 67–74.
Leshno, M. and Spector, Y. (1996). Neural network prediction analysis: The bankruptcy case. Neurocomputing, 10, 125–147.
Li, H., Huang, H. B., Sun, J. and Lin, C. (2010). On sensitivity of case-based reasoning to optimal feature selection subsets in business failure prediction. Expert Syst. Appl., 37, 4811–4821.
Lin, R. H., Wang, Y. T., Wu, C. H., and Chung, C. L. (2009). Developing a business failure prediction model via RST, GRA and CBR. Expert Syst. Appl., 36, 1593–1600.
Lin, W. Y., Hu, Y. H and Tsai, C. F. (2012). Machine Learning in Financial Crisis Prediction: A Survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews). 42(4), 421–436. https://doi.org/10.1109/tsmcc.2011.2170420
Lin, W. Y., Hu, Y. H and Tsai, C. F. (2012). Machine Learning in Financial Crisis Prediction: A Survey. IEEE Transactions on Systems, Man, and Cybernetics—Part C: Applications and Reviews, 42(4): 421-436.
Liu, Y. and Schumann, M. (2005). Data mining feature selection for credit scoring models. J. Oper. Res. Soc., 56(9):1099–1108.
Luo, S. T., Cheng, B. W. and Hsieh, C. H. (2009). Prediction model building with clustering-launched classification and support vector machines in credit scoring. Expert Syst. Appl., 36, 7562–7566.
Malhotra, R., and Malhotra, D. K. (2002). Differentiating between good credits and bad credits using neuro-fuzzy systems. Eur. J. Oper. Res., 136, 190–211.
Martens, D., Baesens, B., VanGestel, T. and Vanthienen, J. (2007). Comprehensible credit scoring models using rule extraction from support vector machines. Eur. J. Oper. Res., 183(3): 1466–1476.
Min J. H. and Jeong, C. (2009). A binary classification method for bankruptcy prediction. Expert Syst. Appl., 36, 5256–5263.
Min J. H. and Lee, Y. C. (2008). A practical approach to credit scoring. Expert Syst. Appl., 35, 1762–1770.
Min, J. H. and Lee, Y. C. (2005). Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters. Expert Syst. Appl., 28, 603–614.
Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. J. Account. Res., 18(1): 109–131.
Petersen, K., Feldt, R., Mujtaba, S. and Mattsson, M. (2006). Systematic mapping studies in software engineering. In Proceedings of the 12th International Conference on Evaluation and Assessment in Software Engineering (EASE), Bari, Italy, 8, 68–77.
Ravisankar P. and Ravi, V. (2010). Financial distress prediction in banks using Group Method of Data Handling neural network, counter propagation neural network and fuzzy ARTMAP. Know.-Based Syst., 23, 823–831.
Ravisankar, P., Ravi, V. and Bose, I. (2010). Failure prediction of dotcom companies using neural network-genetic programming hybrids. Inf. Sci., 180, 1257–1267.
Ryu Y.U. and Yue, W.T. 2005. Firm bankruptcy prediction: Experimental comparison of isotonic separation and other classification approaches. IEEE Trans. Syst., Man, Cybern. A, Syst. Humans, 35(5): 727–737.
Shin, K. S., Lee, T. S. and Kim, H. J. (2005). An application of support vector machines in bankruptcy prediction model. Expert Syst. Appl., 28, 127–135.
Sun, J. and Li, H. (2012). Financial distress prediction using support vector machines: Ensemble vs. Individual. Appl. Soft Comput. 12(8): 2254–2265.
Sun, J. and Li, H. (2008). Data mining method for listed companies’ financial distress prediction. Know-Based Syst., 21, 1–5.
Sung, T. K., Chang, N. and Lee, G. (1999). Dynamics of modeling in data mining: Interpretive approach to bankruptcy prediction. J. Manag. Inf. Syst., 16(1): 63–85.
Tay F. E. H. and Shen, L. (2002). Economic and financial prediction using rough sets model. Eur. J. Oper. Res., 141, 641–659.
Tiffin, A. (2019). Machine Learning and Causality: The Impact of Financial Crises on Growth. IMF Working Paper, November 2019, pp. 1-31.
Tsai, C. F. (2008). Financial decision support using Neural Networks and support vector machines. Expert Syst., 25(4): 380–393, 2008.
Tsai, C. F. and Chen, M. L. (2010). Credit rating by hybrid machine learning techniques. Appl. Soft Comput., 10, 374–380.
Vadlamudi, S. (2015). Enabling Trustworthiness in Artificial Intelligence - A Detailed Discussion. Engineering International, 3(2), 105-114. https://doi.org/10.18034/ei.v3i2.519
Vadlamudi, S. (2016). What Impact does Internet of Things have on Project Management in Project based Firms?. Asian Business Review, 6(3), 179-186. https://doi.org/10.18034/abr.v6i3.520
Vadlamudi, S. (2017). Stock Market Prediction using Machine Learning: A Systematic Literature Review. American Journal of Trade and Policy, 4(3), 123-128. https://doi.org/10.18034/ajtp.v4i3.521
Wang, G., Ma, J. and Yang, S. (2014). An improved boosting based on feature selection for corporate bankruptcy prediction. Expert Syst. Appl., 41(3): 2353–2361.
Wu, W. W. (2010). Beyond business failure prediction. Expert Syst. Appl., 37, 2371–2376.
Yang, Y. (2007). Adaptive credit scoring with kernel learning methods. Eur. J. Oper. Res., 183, 1521–1535.
Yeh I. C. and Lien, C. H. (2009). The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients. Expert Syst. Appl., 36, 2473–2480.
Zhou, L. (2013). Knowledge-Based Systems Performance of corporate bankruptcy prediction models on imbalanced dataset: The effect of sampling methods, 41, 16–25.
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