Abstract
The technological advances in the areas of Big Data and machine learning have led to many useful applications in the financial industry. However, the success of these technologies depends on the analysis of useful information. The financial data is often asymmetrical in nature. It is the nature of information that is crucial in making financial decisions. It is often used to detect the financial frauds, predict the market trends, marketing financial products, and various other use cases. In this work, we are proposing that the ensemble random forests will be able to make better predictions on the asymmetrical financial data. We are taking two cases for making the predictions—one, predicting the customers who will buy the term deposit and two, credit card fraud detection. In both cases, the ensemble random forests were compared with the logistic regression and demonstrated with the results where the random forests performed better than the logistic regression.
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References
Akerlof, George A. 1970. The Market for “Lemons”: Quality Uncertainty and the Market Mechanism. The Quarterly Journal of Economics 84 (3): 488–500.
Bishop, C., and N. Nasrabadi. 2006. Pattern Recognition and Machine Learning, vol. 1. New York: Springer.
Biau, G. 2012. Analysis of a Random Forests Model. The Journal of Machine Learning Research 98888: 1063–1095.
Breiman, L. 2001. Random Forests. Machine Learning 45 (1): 5–32.
Moro, S., P. Cortez, and P. Rita. 2014. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems 62: 22–31.
Dal Pozzolo, Andrea, Olivier Caelen, Reid A. Johnson, and Gianluca Bontempi. 2015. Calibrating Probability with Undersampling for Unbalanced Classification. In Symposium on Computational Intelligence and Data Mining (CIDM), IEEE.
Chawla, N.V., K.W. Bowyer, L.O. Hall, and W.P. Kegelmeyer. 2002. SMOTE: Synthetic Minority Over-Sampling Technique. Journal of Artificial Intelligence Research 16: 321–357.
Pedregosa, et al. 2011. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12: 2825–2830.
Louppe, Gilles, and Alexandre Gramfort. API Design for Machine Learning Software: Experiences from the Scikit-Learn Project” in European Conference on Machine Learning and Principles and Practices of Knowledge Discovery in Databases.
Hastie, Trevor, Robert Tibshirani, Jerome Friedman. 2008. The Elements of Statistical Learning, 2nd ed. Berlin: Springer. ISBN 0-387-95284-5.
Ruangthong, Pumitara, and Saichon Jaiyen. 2015. Bank Direct Marketing Analysis of Asymmetric Information Based on Machine Learning. In 12th International Joint Conference on Computer Science and Software Engineering.
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Muppala, C., Dandu, S., Potluri, A. (2020). Efficient Predictions on Asymmetrical Financial Data Using Ensemble Random Forests. In: Raju, K., Govardhan, A., Rani, B., Sridevi, R., Murty, M. (eds) Proceedings of the Third International Conference on Computational Intelligence and Informatics . Advances in Intelligent Systems and Computing, vol 1090. Springer, Singapore. https://doi.org/10.1007/978-981-15-1480-7_31
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DOI: https://doi.org/10.1007/978-981-15-1480-7_31
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