ICTACT Journal on Soft Computing

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Machine Learning Algorithm for Fintech Innovation in Blockchain Applications


Affiliations
1 Department of Management Studies, Kalasalingam Academy of Research and Education, India
2 PG and Research Department of Commerce, Pasumpon Muthuramalinga Thevar College, India
     

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The rapid growth of Fintech innovation and the widespread adoption of blockchain technologies have indeed had a transformative impact on the financial industry. In this paper, the focus is on the application of machine learning algorithms, specifically the Random Forest Regression algorithm, within the context of Fintech and blockchain. This research contributes to the advancement of machine learning techniques in the field of Fintech and blockchain. The Random Forest Regression algorithm utilizes ensemble learning, combining multiple decision trees to analyze complex financial data and make predictions on various outcomes. This algorithm has proven to be effective in addressing key challenges within the industry, such as predicting loan defaults, detecting fraud, and assessing risks. Through experimental evaluations and case studies, the paper demonstrates the effectiveness of the Random Forest Regression algorithm in enhancing Fintech innovation in blockchain applications. The algorithm improved accuracy, scalability, and interpretability enable financial institutions to make data-driven decisions and optimize their operations.

Keywords

Fintech, Blockchain, Innovations, Random Forest Regression, Machine Learning, Industry.
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  • Machine Learning Algorithm for Fintech Innovation in Blockchain Applications

Abstract Views: 30  |  PDF Views: 1

Authors

V. Lakshmana Narayanan
Department of Management Studies, Kalasalingam Academy of Research and Education, India
G. Ramesh Pandi
Department of Management Studies, Kalasalingam Academy of Research and Education, India
K. Kaleeswari
PG and Research Department of Commerce, Pasumpon Muthuramalinga Thevar College, India
S. Veni
PG and Research Department of Commerce, Pasumpon Muthuramalinga Thevar College, India

Abstract


The rapid growth of Fintech innovation and the widespread adoption of blockchain technologies have indeed had a transformative impact on the financial industry. In this paper, the focus is on the application of machine learning algorithms, specifically the Random Forest Regression algorithm, within the context of Fintech and blockchain. This research contributes to the advancement of machine learning techniques in the field of Fintech and blockchain. The Random Forest Regression algorithm utilizes ensemble learning, combining multiple decision trees to analyze complex financial data and make predictions on various outcomes. This algorithm has proven to be effective in addressing key challenges within the industry, such as predicting loan defaults, detecting fraud, and assessing risks. Through experimental evaluations and case studies, the paper demonstrates the effectiveness of the Random Forest Regression algorithm in enhancing Fintech innovation in blockchain applications. The algorithm improved accuracy, scalability, and interpretability enable financial institutions to make data-driven decisions and optimize their operations.

Keywords


Fintech, Blockchain, Innovations, Random Forest Regression, Machine Learning, Industry.

References