Abstract
It is the question of determining patterns in big data sets that correlate to helpful data. It involves techniques that are at the confluence of machine learning, statistics, and legacy system, and it is also known as data mining. Machine learning is a branch of artificial intelligence that emerged from the areas of object recognition and artificial intelligence. It is concerned with the research and development of methods that can understand from assessment tools. The study shows financial institutions use of financial data performance and ensure precise management of consumer data in order to identify defaulters, to reduce the number of equipment failures associated, to process transactions quickly and efficiently, to reduce the number of incorrect judgments, to categorize potential customers, and to minimize the wastage of the financial organizations.
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References
Adeoye, O.S., Ikemelu, C.R.K.: Industry wide applications of data mining. Int. J. Adv. Stud. Comput. Sci. Eng. 3(2), 28 (2014)
Bishop, C.M.: Model-based machine learning. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 371(1984), 20120222 (2013)
Damrongsakmethee, T., Neagoe, V.E.: Data mining and machine learning for financial analysis. Indian J. Sci. Technol. 10(39), 1–7 (2017)
Deshpande, S.P., Thakare, V.M.: Data mining system and applications: a review. Int. J. Distrib. Parallel Syst. (IJDPS) 1(1), 32–44 (2010)
Han, J., Pei, J., Kamber, M.: Data mining: concepts and techniques. Elsevier (2011)
Hu, Z.G., Li, J.P., Hu, L., Yang, Y.: Research and application of data warehouse and data mining technology in medical field. In: 2015 12th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), pp. 457–460. IEEE (2015)
Jain, A., Pandey, A.K.: Multiple quality optimizations in electrical discharge drilling of mild steel sheet. Mater Today Proc 4(8), 7252–7261 (2017)
Jain, A., Pandey, A.K.: Modeling and optimizing of different quality characteristics in electrical discharge drilling of titanium alloy (grade-5) sheet. Mater Today Proc 18, 182–191 (2019)
Jain, A., Yadav, A.K., Shrivastava, Y.: Modelling and optimization of different quality characteristics in electric discharge drilling of titanium alloy sheet. Mater Today Proc 21, 1680–1684 (2020)
Jiang, N., Zhang, K., Ai, M., Du, X.: The momentum investment style in Chinese markets. Front Econ Manage 1(7), 88–106 (2020)
Lin W.Y., Hu, Y.H., Tsai, C.F.: Machine learning in financial crisis prediction: a survey. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 42(4), 421–436 (2011)
Medelyan, O., Milne, D., Legg, C., Witten, I.H.: Mining meaning from Wikipedia. Int. J. Hum Comput Stud. 67(9), 716–754 (2009)
Mohammed, M., Anad, M., Mzher, A., Hasson, A.: Meta-data and data mart solutions for better understanding for data and information in e-government monitoring. Int. J. Comput. Sci. Issues (IJCSI) 9(6), 78 (2012)
Munawar, N.S., Ibrahim, R.: Quality oriented for physical design data warehouse (2006)
Nie, G., Zhang, L., Liu, Y., Zheng, X., Shi, Y.: Decision analysis of data mining project based on Bayesian risk. Expert Syst. Appl. 36(3), 4589–4594 (2009)
Panwar, V., Sharma, D.K., Kumar, K.P., Jain, A., Thakar, C.: Experimental investigations and optimization of surface roughness in turning of en 36 alloy steel using response surface methodology and genetic algorithm. Mater Today Proc (2021)
Seah, B.K., Selan, N.E.: Design and implementation of data warehouse with data model using survey-based services data. In: Fourth Edition of the International Conference on the Innovative Computing Technology (INTECH 2014), pp. 58–64. IEEE (2014)
Singh, S.: Data warehouse and its methods. J Glob. Res. Comput. Sci. 2(5), 113–115 (2011)
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Al Ayub Ahmed, A., Rajesh, S., Lohana, S., Ray, S., Maroor, J.P., Naved, M. (2023). Using Machine Learning and Data Mining to Evaluate Modern Financial Management Techniques. In: Yadav, S., Haleem, A., Arora, P.K., Kumar, H. (eds) Proceedings of Second International Conference in Mechanical and Energy Technology. Smart Innovation, Systems and Technologies, vol 290. Springer, Singapore. https://doi.org/10.1007/978-981-19-0108-9_26
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DOI: https://doi.org/10.1007/978-981-19-0108-9_26
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