• Harsh Daiya

Accurate financial risk assessment is critical in the quickly changing fintech environment to protect against any losses and guarantee regulatory compliance. This paper investigates how ensemble learning techniques could be applied to improve fintech sector risk assessment capabilities. Comparing ensemble techniques like Random Forest, Gradient Boosting, and AdaBoost to conventional single-model methods, the former may provide better prediction performance. We start our study by painstakingly compiling and preparing a large dataset that represents several financial risk indicators. Next, we find the most important financial risk predictors using sophisticated feature selection methods. Then we train and assess many ensemble models with rigorous assessment criteria like accuracy, precision, recall, and AUC-ROC. The results show that ensemble learning techniques produce more accurate and dependable risk predictions than traditional risk assessment models by a considerable margin. In particular, gradient boosting proved to be the best method, providing a significant increase in robustness and prediction accuracy. The fintech sector may benefit greatly from these results, which imply that using ensemble learning might improve risk management techniques and result in better-informed decision-making. This study advances our knowledge of machine learning applications in fintech, both academically and practically, for industry professionals looking to use cutting-edge technology to reduce financial risk. This paper opens the door for more robust and flexible fintech systems that can adjust to the complexity of contemporary financial markets by developing the techniques employed in financial risk assessment.

Financial Risk Assessment, Fintech, Ensemble Learning, Machine Learning, Risk Management

"FINANCIAL RISK ASSESSMENT IN FINTECH USING ENSEMBLE LEARNING METHODS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 4, page no.n106-n114, April-2023, Available :http://www.jetir.org/papers/JETIR2304F17.pdf

"FINANCIAL RISK ASSESSMENT IN FINTECH USING ENSEMBLE LEARNING METHODS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 4, page no. ppn106-n114, April-2023, Available at : http://www.jetir.org/papers/JETIR2304F17.pdf