Credit Risk Modeling with Big Data Analytics: Regulatory Compliance and Data Analytics in Credit Risk Modeling

Abstract

The ever-evolving landscape of financial markets necessitates innovative approaches to credit risk modeling, especially with the advent of big data analytics. This paper explores the intersection of regulatory compliance and data analytics in credit risk modeling, highlighting the challenges and opportunities inherent in this dynamic field. In recent years, financial institutions have increasingly turned to big data analytics to enhance their credit risk assessment processes. This shift is driven by the growing volume, velocity, and variety of data available, including traditional financial indicators, alternative data sources, and unstructured data. Leveraging big data analytics enables more accurate risk assessments, improved decision-making, and enhanced portfolio management. However, the adoption of big data analytics in credit risk modeling brings about various regulatory compliance considerations. Financial institutions must navigate a complex regulatory landscape, including guidelines set forth by regulatory bodies such as the Basel Committee on Banking Supervision and local regulatory authorities. Compliance with these regulations is imperative to ensure the soundness and stability of financial markets.