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.