AI-Infused Data Warehousing: Redefining Data Governance in the Finance Industry

Abstract

The finance industry is undergoing a paradigm shift in data management with the integration of artificial intelligence (AI) into data warehousing. This paper explores the transformative potential of AI-infused data warehousing in redefining data governance within the finance sector. Key challenges such as data quality, regulatory compliance, and real-time risk management are analyzed alongside AI-powered solutions. By presenting applications and a comprehensive implementation framework, this article offers a roadmap for optimizing financial data warehouses to support enhanced decision-making, improved compliance, and advanced risk management strategies.

Country : USA

1 Srinivasa Chakravarthy Seethala

  1. Lead Developer, Buffalo, New York, USA

IRJIET, Volume 5, Issue 5, May 2021 pp. 150-152

doi.org/10.47001/IRJIET/2021.505028

References

  1. Barocas, S., & Selbst, A. D. (2016). Big data's disparate impact. California Law Review, 104, 671. https://www.jstor.org/stable/24758720.
  2. Batini, C., et al. (2009). Methodologies for data quality assessment and improvement. ACM Computing Surveys, 41(3), 1-52. http://dx.doi.org/10.1145/1541880.1541883.
  3. Butler, T. (2017). Towards a standards-based technology architecture for RegTech. Journal of Financial Transformation, 45, 49-59. https://www.semanticscholar.org/paper/Towards-a-Standards-Based-Technology-Architecture-Butler/4d32df73930490e5ab325ccebad7d51ef85206b7.
  4. Davenport, T. H., & Patil, D. J. (2012). Data scientist. Harvard Business Review, 90(5), 70-76.
  5. Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608.
  6. Dwork, C. (2008). Differential privacy: A survey of results. Springer. https://link.springer.com/chapter/10.1007/978-3-540-79228-4_1.
  7. Herschel, G. (2008). Magic quadrant for data quality tools. Gartner Research. https://www.gartner.com/en/documents/3905769.
  8. Khandani, A. E., et al. (2010). Consumer credit-risk models via machine-learning algorithms. Journal of Banking & Finance, 34(11), 2767-2787. https://www.sciencedirect.com/science/article/abs/pii/S0378426610002372.
  9. Phua, C., et al. (2010). A comprehensive survey of data mining-based fraud detection research. arXiv preprint arXiv:1009.6119.
  10. Tankard, C. (2016). What the GDPR means for businesses. Network Security, 2016(6), 5-8. http://dx.doi.org/10.1016/S1353-4858(16)30056-3.
  11. Kononenko, I. (2001). Machine learning for medical diagnosis: history, state of the art and perspective. Artificial Intelligence in Medicine, 23(1), 89-109. https://doi.org/10.1016/S0933-3657(01)00077-X.
  12. Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608. https://arxiv.org/abs/1702.08608.
  13. Frees, E. W., Meyers, G., & Cummings, A. D. (2014). Insurance ratemaking and a Gini index. Journal of Risk and Insurance, 81(2), 335-366. Insurance Ratemaking and a Gini Index on JSTOR.
  14. Batini, C., Cappiello, C., Francalanci, C., & Maurino, A. (2009). Methodologies for data quality assessment and improvement. ACM Computing Surveys, 41(3), 1-52. https://dl.acm.org/doi/10.1145/1541880.1541883.
  15. Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165-1188. https://doi.org/10.2307/41703503.