Optimizing Data Aggregation and Reporting for BASEL Compliance in a Big Data
DOI:
https://doi.org/10.62643/Keywords:
Big Data, Basel Compliance, Risk Management, Data Aggregation, Regulatory Reporting, Financial Analytics, AI in Finance, Predictive Modeling, Data Governance, Digital TransformationAbstract
This paper explains how Big Data architectures, automatic intelligence with the assistance of AI, and real-time analytics facilitate risk
information aggregation and reporting for the regulation underneath Basel III. Utilizing the secondary research approach, they have a look at analyses of financial facts units, industry reports, and the HSBC and Barclays case research. The findings display that the implementation of AI, Apache Spark, and blockchain improves regulatory accuracy, fraud detection, and reporting.
Although the technologies have added development with them,the issues associated with high prices of implementation,
statistics protection risks, and spread-out regulatory regimes persist. Recommends the implementation of hybrid
human-AI tracking, statistics governance rules, cloud-based threat structures, and AIpowered early caution
mechanisms. Quantum computing, deep knowledge of, and blockchain improvements are subjects for destiny research
to similarly streamline compliance and threat evaluation.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
