Ethical AI in Financial Services: Balancing Innovation and Compliance

Authors

  • Balkishan Arugula Sr. Technical Architect/ Technical Manager at MobiquityInc (Hexaware), USA. Author

DOI:

https://doi.org/10.63282/3050-9262.IJAIDSML-V5I3P106

Keywords:

Ethical AI, Financial Services, FinTech, Compliance, Fairness, Transparency, Accountability, AI Governance, Bias Mitigation, Explainability, Responsible Innovation, Regulatory Frameworks, Data Privacy, AI Ethics, Risk Management, Machine Learning, Credit Scoring, Algorithmic Decision-Making, AML, Robo-Advisors

Abstract

By offering strong tools that improve decision-making, tailor customer experiences, maximize their operations, and lower risk management, artificial intelligence (AI) is revolutionizing the financial services industry. Institutions accelerating the use of AI create more need for ensuring that innovation does not transcend moral responsibility. The complex balance between using AI's revolutionary power & following the ethical and regulatory guidelines controlling the financial sector is investigated in this paper. It emphasizes the requirement of a strong ethical framework to guide the appropriate use of AI, thereby helping companies to solve important problems such as algorithmic bias, inadequate openness & uncertain accountability in negative contexts. These challenges compromise customer trust & have major legal and reputational effects for financial companies. First with the current legislative framework controlling artificial intelligence usage in finance, the article is arranged to provide a comprehensive viewpoint, then addressing the ethical risks usually connected with AI applications. A useful case study is given to show the benefits & negative effects of AI use, therefore producing specific understanding. The paper offers reasonable concepts for financial organizations to use AI in creative and ethically conscious ways. The concepts center on creating understandable models and encouraging cross-functional responsibility to help organizations to create responsibly & sustainably in a time mostly shaped by intelligent systems

References

[1] Truby, Jon, Rafael Brown, and Andrew Dahdal. "Banking on AI: mandating a proactive approach to AI regulation in the financial sector." Law and Financial Markets Review 14.2 (2020): 110-120.

[2] Aziz, Layla Abdel-Rahman, and Yuli Andriansyah. "The role artificial intelligence in modern banking: an exploration of AI-driven approaches for enhanced fraud prevention, risk management, and regulatory compliance." Reviews of Contemporary Business Analytics 6.1 (2023): 110-132.

[3] Aldboush, Hassan HH, and Marah Ferdous. "Building trust in fintech: an analysis of ethical and privacy considerations in the intersection of big data, AI, and customer trust." International Journal of Financial Studies 11.3 (2023): 90.

[4] Lee, Joseph. "Access to finance for artificial intelligence regulation in the financial services industry." European Business Organization Law Review 21.4 (2020): 731-757.

[5] Adams, Janet, and Hani Hagras. "A type-2 fuzzy logic approach to explainable AI for regulatory compliance, fair customer outcomes and market stability in the global financial sector." 2020 IEEE international conference on fuzzy systems (FUZZ-IEEE). IEEE, 2020.

[6] Talakola, Swetha, and Abdul Jabbar Mohammad. “Microsoft Power BI Monitoring Using APIs for Automation”. American Journal of Data Science and Artificial Intelligence Innovations, vol. 3, Mar. 2023, pp. 171-94

[7] Kumar Tarra, Vasanta, and Arun Kumar Mittapelly. “AI-Driven Lead Scoring in Salesforce: Using Machine Learning Models to Prioritize High-Value Leads and Optimize Conversion Rates”. International Journal of Emerging Trends in Computer Science and Information Technology, vol. 5, no. 2, June 2024, pp. 63-72

[8] Buckley, Ross P., et al. "Regulating artificial intelligence in finance: putting the human in the loop." Sydney Law Review, The 43.1 (2021): 43-81.

[9] Syed, Ali Asghar Mehdi. “Networking Automation With Ansible and AI: How Automation Can Enhance Network Security and Efficiency”. Los Angeles Journal of Intelligent Systems and Pattern Recognition, vol. 3, Apr. 2023, pp. 286-0

[10] Sangaraju, Varun Varma. "INTELLIGENT SYSTEMS AND APPLICATIONS IN ENGINEERING."

[11] Veluru, Sai Prasad, and Swetha Talakola. “Continuous Intelligence: Architecting Real-Time AI Systems With Flink and MLOps”. American Journal of Autonomous Systems and Robotics Engineering, vol. 3, Sept. 2023, pp. 215-42

[12] Christensen, Jonas. "AI in financial services." Demystifying AI for the Enterprise. Productivity Press, 2021. 149-192.

[13] Paidy, Pavan. “AI-Augmented SAST and DAST Integration in CI CD Pipelines”. Los Angeles Journal of Intelligent Systems and Pattern Recognition, vol. 2, Feb. 2022, pp. 246-72

[14] Paleti, Srinivasarao. "Adaptive AI In Banking Compliance: Leveraging Agentic AI For Real-Time KYC Verification, Anti-Money Laundering (AML) Detection, And Regulatory Intelligence." Anti-Money Laundering (AML) Detection, And Regulatory Intelligence (December 20, 2022) (2022).

[15] Vasanta Kumar Tarra, and Arun Kumar Mittapelly. “The Role of Generative AI in Salesforce CRM: Exploring How Tools Like ChatGPT and Einstein GPT Transform Customer Engagement”. JOURNAL OF RECENT TRENDS IN COMPUTER SCIENCE AND ENGINEERING ( JRTCSE), vol. 12, no. 1, May 2024, pp. 50-66

[16] Aitken, Mhairi, et al. "Establishing a social licence for Financial Technology: Reflections on the role of the private sector in pursuing ethical data practices." Big Data & Society 7.1 (2020): 2053951720908892.

[17] Atluri, Anusha, and Teja Puttamsetti. “Engineering Oracle HCM: Building Scalable Integrations for Global HR Systems ”. American Journal of Data Science and Artificial Intelligence Innovations, vol. 1, Mar. 2021, pp. 422-4

[18] Díaz-Rodríguez, Natalia, et al. "Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation." Information Fusion 99 (2023): 101896.

[19] Sangaraju, Varun Varma. "AI-Augmented Test Automation: Leveraging Selenium, Cucumber, and Cypress for Scalable Testing." International Journal of Science And Engineering 7 (2021): 59-68.

[20] Grima, Simon, Jonathan Spiteri, and Inna Romanova. "The challenges for regulation and control in an environment of rapid technological innovations." InsurTech: a legal and regulatory view. Cham: Springer International Publishing, 2019. 83-98.

[21] Veluru, Sai Prasad, and Mohan Krishna Manchala. “Federated AI on Kubernetes: Orchestrating Secure and Scalable Machine Learning Pipelines”. Essex Journal of AI Ethics and Responsible Innovation, vol. 1, Mar. 2021, pp. 288-12

[22] Talakola, Swetha. “Enhancing Financial Decision Making With Data Driven Insights in Microsoft Power BI”. Essex Journal of AI Ethics and Responsible Innovation, vol. 4, Apr. 2024, pp. 329-3

[23] Sangeeta Anand, and Sumeet Sharma. “Scalability of Snowflake Data Warehousing in Multi-State Medicaid Data Processing”. JOURNAL OF RECENT TRENDS IN COMPUTER SCIENCE AND ENGINEERING ( JRTCSE), vol. 12, no. 1, May 2024, pp. 67-82

[24] Atluri, Anusha. “Post-Deployment Excellence: Advanced Strategies for Agile Oracle HCM Configurations”. International Journal of Emerging Research in Engineering and Technology, vol. 4, no. 1, Mar. 2023, pp. 37-44

[25] Syed, Ali Asghar Mehdi, and Erik Anazagasty. “Hybrid Cloud Strategies in Enterprise IT: Best Practices for Integrating AWS With on-Premise Datacenters”. American Journal of Data Science and Artificial Intelligence Innovations, vol. 2, Aug. 2022, pp. 286-09

[26] Omopariola, Busayo, and Veronica Aboaba. "Advancing financial stability: The role of AI-driven risk assessments in mitigating market uncertainty." Int J Sci Res Arch 3.2 (2021): 254-270.

[27] Paidy, Pavan. “Testing Modern APIs Using OWASP API Top 10”. Essex Journal of AI Ethics and Responsible Innovation, vol. 1, Nov. 2021, pp. 313-37

[28] Anand, Sangeeta. “AI-Based Predictive Analytics for Identifying Fraudulent Health Insurance Claims”. International Journal of AI, BigData, Computational and Management Studies, vol. 4, no. 2, June 2023, pp. 39-47

[29] van den Broek, Tijs, and Anne Fleur van Veenstra. "Governance of big data collaborations: How to balance regulatory compliance and disruptive innovation." Technological Forecasting and Social Change 129 (2018): 330-338.

[30] Kupunarapu, Sujith Kumar. "Data Fusion and Real-Time Analytics: Elevating Signal Integrity and Rail System Resilience." International Journal of Science And Engineering 9.1 (2023): 53-61.

[31] Yasodhara Varma. “Modernizing Data Infrastructure: Migrating Hadoop Workloads to AWS for Scalability and Performance”. Newark Journal of Human-Centric AI and Robotics Interaction, vol. 4, May 2024, pp. 123-45

[32] Chaganti, Krishna. "Adversarial Attacks on AI-driven Cybersecurity Systems: A Taxonomy and Defense Strategies." Authorea Preprints.

[33] Giudici, Paolo. "Fintech risk management: A research challenge for artificial intelligence in finance." Frontiers in Artificial Intelligence 1 (2018): 1.

[34] Lui, Alison, and George William Lamb. "Artificial intelligence and augmented intelligence collaboration: regaining trust and confidence in the financial sector." Information & Communications Technology Law 27.3 (2018): 267-283.

[35] Mudunuri L.N.R.; (December, 2023); “AI-Driven Inventory Management: Never Run Out, Never Overstock”; International Journal of Advances in Engineering Research; Vol 26, Issue 6; 24-36

Published

2024-10-30

Issue

Section

Articles

How to Cite

1.
Arugula B. Ethical AI in Financial Services: Balancing Innovation and Compliance. IJAIDSML [Internet]. 2024 Oct. 30 [cited 2025 Oct. 3];5(3):46-54. Available from: https://ijaidsml.org/index.php/ijaidsml/article/view/150