The Impact of AI-Driven Incentive Compensation Management on Financial Advisors’ Performance and Compliance
Keywords:
artificial intelligence, incentive compensation management, financial advisorsAbstract
Incentive compensation management is revolutionized by the integration of artificial intelligence which helps the financial institutions by enhancing the accuracy of commission calculations, ensuring regulatory compliance, and optimizing financial advisors’ performance. AI-driven ICM system uses machine learning algorithms for predictive analytics automation to minimise human errors, streamline compensation structures and align incentive with institutional objectives. This study examines the role of AI in icm by analysing its implications for financial advisors, behavioral dynamics, productivity, and adherence to compliance mandates.
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References
S. J. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 4th ed. Upper
Saddle River, NJ, USA: Pearson, 2020.
George, Jabin Geevarghese. "Advancing Enterprise Architecture for Post-Merger
Financial Systems Integration in Capital Markets laying the Foundation for Machine
Learning Application." Aus. J. ML Res. & App 3.2 (2023): 429.
Dash, S. "Architecting Intelligent Sales and Marketing Platforms: The Role of
Enterprise Data Integration and AI for Enhanced Customer Insights." Journal of
Artificial Intelligence Research 3.2 (2023): 253-291.
Singu, Santosh Kumar. "Migration strategies for legacy data warehousing systems to
cloud platforms." Internafional Journal of Science and Research (IJSR) 12, no. 12
(2023): 2164-2167.
George, Jabin Geevarghese. "HARNESSING GENERATIVE AI FOR ENTERPRISE
APPLICATION MODERNIZATION: ENHANCING CYBERSECURITY AND DRIVING
INNOVATION." INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN
ENGINEERING AND TECHNOLOGY (IJARET) 15.3 (2024): 377-392.
Dash, S. "Designing Modular Enterprise Software Architectures for AI-Driven Sales
Pipeline Optimization." Journal of Artificial Intelligence Research 3.2 (2023): 292-334.
Godbole, Aditi, Jabin Geevarghese George, and Smita Shandilya. "Leveraging Long-
Context Large Language Models for Multi-Document Understanding and
Summarization in Enterprise Applications." arXiv preprint arXiv:2409.18454 (2024).
Akhilandeswari, P., and Jabin G. George. "Secure Text Steganography." Proceedings
of International Conference on Internet Computing and Information Communications:
ICICIC Global 2012. Springer India, 2014.
Singu, Santosh Kumar. "Impact of Data Warehousing on Business Intelligence and
Analytics." ESP Journal of Engineering & Technology Advancements 2.2 (2022):
-113.
Santosh Kumar, Singu. "Maximizing financial intelligence-the role of optimized etl in
fintech data warehousing." INTERNATIONAL JOURNAL OF COMPUTER
ENGINEERING AND TECHNOLOGY (IJCET) 15, no. 4 (2024): 464-471.
S. Chatterjee and G. Roy, “Deep learning for performance evaluation in financial
advisory services,” Expert Systems with Applications, vol. 186, no. 3, pp. 115764,
Mar. 2022.
A. Kapoor and A. Sinha, “Automating incentive compensation management:
Leveraging AI for efficiency and transparency,” IEEE Access, vol. 9, pp.
–18049, Feb. 2021.
C. Mollick, The AI-Driven Workplace: Implications for Finance and Compensation,
Cambridge, MA, USA: MIT Press, 2020.
G. N. Kocer, “Ethical AI in financial services: Addressing bias in compensation
algorithms,” Journal of Business Ethics, vol. 176, no. 3, pp. 421–438, Aug. 2022.
R. G. Adams, “Blockchain technology and AI for fraud prevention in financial
compensation,” Financial Studies Journal, vol. 20, no. 2, pp. 112–129, May 2021.
A. Klein and J. Smith, “AI in investment advisory services: Balancing automation and
human decision-making,” Journal of Financial Technology, vol. 15, no. 4, pp.
–298, Dec. 2021.
P. Gupta, “Regulatory challenges in AI-driven incentive compensation models,”
International Journal of Financial Regulation, vol. 18, no. 1, pp. 91–107, Jan. 2022.
H. T. Shin and M. E. Wang, “Optimizing financial advisor incentives through
reinforcement learning,” IEEE Transactions on Computational Finance, vol. 13, no. 5,
pp. 1235–1252, Nov. 2021.
J. D. Linton and R. Zhang, “Performance-based compensation models using AI-
driven workforce analytics,” AI & Society, vol. 36, no. 2, pp. 339–355, Jul. 2021.
L. P. Romano, “AI-based financial advisor productivity forecasting,” Journal of
Financial Planning, vol. 34, no. 6, pp. 44–59, Jun. 2022.
F. Zhou and Y. Chen, “AI-driven personalization in financial compensation planning,”
Decision Support Systems, vol. 157, no. 2, pp. 113456, Apr. 2022.
