Operational Challenges in Basel IV Credit Risk Compliance

Main Article Content

Ravikumar Mani Naidu Gunasekaran

Abstract

The implementation of Basel IV regulations represents a significant advancement in global banking supervision, with a strong focus on enhancing the accuracy, consistency, and transparency of credit risk measurement. While these reforms strengthen the resilience of financial institutions, they introduce substantial operational complexities, particularly in the areas of data management, system integration, model governance, and regulatory reporting. This paper examines the key operational challenges faced by banks in complying with Basel IV credit risk requirements, including the adoption of revised standardized approaches, restrictions on internal ratings-based (IRB) models, and the introduction of the output floor. The study highlights critical issues such as fragmented data architectures, legacy system constraints, increased computational demands, and the need for robust data lineage and governance frameworks. Additionally, the paper discusses the implications of heightened regulatory scrutiny and the requirement for greater model transparency and validation under evolving compliance standards. To address these challenges, the paper outlines strategic approaches involving modernization of technology infrastructure, adoption of cloud-based platforms, automation of reporting processes, and integration of advanced analytics. By providing a comprehensive assessment of operational barriers and potential solutions, this study aims to support financial institutions in navigating the complexities of Basel IV implementation. The findings underscore the importance of aligning organizational processes, technology, and governance frameworks to achieve effective and sustainable compliance in an increasingly data-driven regulatory environment.

Article Details

How to Cite
Gunasekaran, R. M. N. (2023). Operational Challenges in Basel IV Credit Risk Compliance. The Eastasouth Journal of Information System and Computer Science, 1(01), 169–178. https://doi.org/10.58812/esiscs.v1i01.1089
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Articles

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