Factor Analysis Framework for Credit, Operational, and Market Risk Modeling
Abstract: Risk management is a critical component for financial institutions in maintaining resilience against market, credit, and operational risks. Factor analysis, a statistical method, is commonly employed to simplify complex datasets by identifying latent risk factors that impact decision-making processes. This paper explores the methodologies, applications, and benefits of factor analysis in risk management. It further incorporates Python code examples and visualizations to demonstrate the practical application of factor analysis in credit risk management. Finally, the paper delves into the future direction of factor analysis, particularly in integrating machine learning, big data, and environmental, social, and governance (ESG) risks.
Keywords: Factor Analysis, Principal Component Analysis (PCA), Risk Management, Dimensionality Reduction, Statistical Modeling
How to Cite?: Sanjay Moolchandani, "Factor Analysis Framework for Credit, Operational, and Market Risk Modeling", Volume 13 Issue 4, April 2024, International Journal of Science and Research (IJSR), Pages: 1987-1993, https://www.ijsr.net/getabstract.php?paperid=SR24417094840, DOI: https://dx.doi.org/10.21275/SR24417094840