Abstract

The long list of AI governance challenges motivates the need for new solution approaches. A portion of these issues are general to deploying AI models. An exploration of the broader concerns attempting to address the high-level risks of AI use in financial services is presented. There is however a perceived gap between those objectives, and the practical applications supporting the mass adoptions of AI systems in the sector. The focus is on the latter. It is acknowledged that a vast amount of work remains to deliver the promised economic potential for society, first and foremost the widespread understandability and trustable AI systems. The financial services industry is undergoing a profound transformation from static compliance to continuous adjustment of short-term risks. Such transformation requires a decision-making system that can well respond to the proactively evolving operational environment in real time.

Such awareness has pointed out a long list of risks induced or amplified by AI systems. It is agreed that improper use can result in severe harm and entanglement. Critics have broadly categorized the hazards falling into bias, security, accountability, and oversight. The financial services are a significant application area of AI systems. This broad “financial services” comprises a vast ecosystem that includes different business activities involving personal, institutional or governmental finance. Several of the breakthroughs in machine learning and artificial intelligence have been leveraged by the finance sector, which has long been a source of fundamental research questions, particularly in the areas of prediction, time series, and optimization. Today most of the leading global financial institutions have adopted or are exploring the adoption of AI systems across their business activities, for B2B or B2C. The variety and complexities of AI systems used in the financial services, however, have also revealed a range of new challenges.

Keywords

  • Proactive risk
  • core banking
  • banking AI
  • banking risks monitoring
  • risk event prediction

References

  1. 1. Kalisetty, S., & Ganti, V. K. A. T. (2019). Transforming the Retail Landscape: Srinivas’s Vision for Integrating Advanced Technologies in Supply Chain Efficiency and Customer Experience. Online Journal of Materials Science, 1, 1254.
  2. 2. Sikha, V. K. (2020). Ease of Building Omni-Channel Customer Care Services with Cloud-Based Telephony Services & AI. Zenodo. https://doi.org/10.5281/ZENODO.14662553
  3. 3. Siramgari, D., & Korada, L. (2019). Privacy and Anonymity. Zenodo. https://doi.org/10.5281/ZENODO.14567952
  4. 4. Maguluri, K. K., & Ganti, V. K. A. T. (2019). Predictive Analytics in Biologics: Improving Production Outcomes Using Big Data.
  5. 5. Ganesan, P. (2020). PUBLIC CLOUD IN MULTI-CLOUD STRATEGIES INTEGRATION AND MANAGEMENT.
  6. 6. Siramgari, D., & Korada, L. (2019). Privacy and Anonymity. Zenodo. https://doi.org/10.5281/ZENODO.14567952
  7. 7. Polineni, T. N. S., & Ganti, V. K. A. T. (2019). Revolutionizing Patient Care and Digital Infrastructure: Integrating Cloud Computing and Advanced Data Engineering for Industry Innovation. World, 1, 1252.
  8. 8. Somepalli, S. (2019). Navigating the Cloudscape: Tailoring SaaS, IaaS, and PaaS Solutions to Optimize Water, Electricity, and Gas Utility Operations. Zenodo. https://doi.org/10.5281/ZENODO.14933534
  9. 9. Ganesan, P. (2020). Balancing Ethics in AI: Overcoming Bias, Enhancing Transparency, and Ensuring Accountability. North American Journal of Engineering Research, 1(1).
  10. 10. Somepalli, S., & Siramgari, D. (2020). Unveiling the Power of Granular Data: Enhancing Holistic Analysis in Utility Management. Zenodo. https://doi.org/10.5281/ZENODO.14436211
  11. 11. Ganesan, P. (2020). DevOps Automation for Cloud Native Distributed Applications. Journal of Scientific and Engineering Research, 7(2), 342-347.
  12. 12. Vankayalapati, R. K. (2020). AI-Driven Decision Support Systems: The Role Of High-Speed Storage And Cloud Integration In Business Insights. Available at SSRN 5103815.
  13. 13. Ganti, V. K. A. T. (2019). Data Engineering Frameworks for Optimizing Community Health Surveillance Systems. Global Journal of Medical Case Reports, 1, 1255.
  14. 14. Sondinti, K., & Reddy, L. (2019). Data-Driven Innovation in Finance: Crafting Intelligent Solutions for Customer-Centric Service Delivery and Competitive Advantage. Available at SSRN 5111781.
  15. 15. Pandugula, C., & Yasmeen, Z. (2019). A Comprehensive Study of Proactive Cybersecurity Models in Cloud-Driven Retail Technology Architectures. Universal Journal of Computer Sciences and Communications, 1(1), 1253. Retrieved from https://www.scipublications.com/journal/index.php/ujcsc/article/view/1253