Data Science Applications in Financial Risk Assessment
TL;DR
Experimental insights show that models like random forests, gradient boosting, SVMs, and neural networks provide better predictive accuracy and early warning capabilities compared to traditional methods, but challenges related to data quality, interpretability, and ethical concerns remain.
Abstract
Financial risk assessment is essential in handling uncertainties and complexities in modern financial systems. Traditional statistical models often struggle with nonlinear relationships and dynamic market conditions. This paper explores the application of data science techniques—such as machine learning, deep learning, and big data analytics—in evaluating various financial risks, including credit, market, operational, and systemic risks. The proposed framework integrates data preprocessing, feature engineering, predictive modeling, and explainability to enhance decision-making and regulatory compliance. Experimental insights show that models like random forests, gradient boosting, SVMs, and neural networks provide better predictive accuracy and early warning capabilities compared to traditional methods. However, challenges related to data quality, interpretability, and ethical concerns remain. Overall, data science is identified as a transformative approach to financial risk assessment when supported by proper governance and validation practices.