Arseam

International Journal of Marketing & Financial Management

TRANSFORMING PRIME BROKERAGE: THE IMPACT OF AI ON EFFICIENCY AND CLIENT EXPERIENCE

Gaurav Jindal

Gaurav Jindal

Sr Leader in Prime Brokerage & Risk Management India.

DOI : https://doi.org/10.53882/IJMFM.2024.1201003 Page No : 51-69

Published Online : 2024-06-30

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The prime brokerage industry is presently undergoing a profound transformation from the use of Artificial Intelligence (AI), Machine Learning (ML), and analytics. With high-end technology, prime brokers can work through enormous data volumes containing client and market information with greater operational efficiency and further enhance client experience. As firms use AI and ML capabilities, they optimize trade executions, automate recurring tasks and simplify complex work flows by doing things quicker and at a faster speed with little error. Predictive capabilities of the AI-driven tools allow for pre-empting needs among prime brokers' clients with tailored investment strategies provided by firms to the clients. There is also improved management in real time about potential anomalies and threats with appropriate means of mitigation for respective risks. There is improvement even in decision-making by allowing firms to make fast-moving decisions based on real conditions pertaining to the market. This paper shows that AI affects the prime brokerage space trans formatively and is intended to support client relationships that enable a competitive advantage. It will explain how it enables the operational bottlenecks that reduce costs with better services. This work also introduces best practices about the integration of AI and ML technologies; a client-centric approach where technological innovation matches personalized service is encouraged. In doing so, prime brokerage firms can position themselves to thrive in an increasingly competitive and rapidly evolving financial landscape, ensuring sustainable growth and long-term success while delivering exceptional client value.

Keywords: Prime Brokerage, Artificial Intelligence, Machine Learning, Client Experience, Operational Efficiency and Personalized Services.

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Kopalle, P. K., et. al(2020). How legacy firms can embrace the digital ecosystem via digital customer orientation. Journal of the Academy of Marketing Science, 48, 114-131. Retrieved From: https://link.springer.com/article/10.1007/s11747-019-00694-2
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Vegesna, V. V. (2018). Analysis of Artificial Intelligence Techniques for Network Intrusion Detection and Intrusion Prevention for Enhanced User Privacy. Asian Journal of Applied Science and Technology (AJAST) Volume, 2, 315-330. Retrieved From: https://www.researchgate.net/publication/374949463_Analysis_of_Artificial_Intelligence_Techniques_for_Network_Intrusion_Detection_and_Intrusion_Prevention_for_Enhanced_User_Privacy
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Jindal G (2024), “Transforming Prime Brokerage: The Impact Of Ai On Efficiency And Client Experience”, International Journal of Marketing & Financial Management, Volume 12, Issue 1, 2024, pp 51-69. DOI : https://doi.org/10.53882/IJMFM.2024.1201003

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REFERENCES

Aghera, S. (2021). Securing Ci/Cd Pipelines Using Automated Endpoint Security Hardening. Journal Of Basic Science And Engineering, 18(1). Retrieved From: https://www.researchgate.net/publication/383985386_Securing_CICD_pipelines_using_Automated_Endpoint_Security_Hardening
Aghera, S. (2022). Implementing Zero Trust Security Model In Devops Environments. Journal of Basic Science And Engineering, 19(1). Retrieved From: https://yigkx.org.cn/index.php/jbse/article/view/237
Borejdo, J. et. al(2021). Surface plasmon assisted microscopy: Reverse kretschmann fluorescence analysis of kinetics of hypertrophic cardiomyopathy heart. Retrieved From: https://www.semanticscholar.org/paper/Surface-plasmon-assisted-microscopy%3A-Reverse-of-of-Borejdo-Mettikolla/e5e992b2a8ac20fde4c4eb9fbf718eea80316366
Choudhary, S. K et. al(2023). Detecting Malware Attacks Based on Machine Learning Techniques for Improve Cyber security. International Journal of Core Engineering & Management, 7(8), 88. ISSN 2348-9510. Retrieved From: https://ijcem.in/wp-content/uploads/DETECTING-MALWARE-ATTACKS-BASED-ON-MACHINE-LEARNING-TECHNIQUES-FOR-IMPROVE-CYBERSECURITY.pdf
Dhiman, V. (2019). Dynamic Analysis Techniques for Web Application Vulnerability Detection. Journal of Basic Science and Engineering, 16(1). Retrieved From: https://yigkx.org.cn/index.php/jbse/article/view/302
Dhiman, V. (2022). Intelligent Risk Assessment Framework For Software Security Compliance Using Ai. International Journal of Innovation Studies, 6(3). Retrieved From: https://yigkx.org.cn/index.php/jbse/article/view/237
EleonoraViganò, et. al(2021). Critical Infrastructure Cyber security: A Reviewof Policies and Practices. Washington, DC: Government Printing Office. Retrieved From: https://canvas-project.eu/assets/pdf/book/p157-177.pdf
European Union Agency for Cyber security. (2020). Machine Learning for Anomaly Detection in Critical Infrastructure: Challenges and Opportunities. Brussels, Belgium: Publications Office of the European Union.
Jajodia, S., Subrahmanian, (2016). Handbook of SCADA/Control Systems Security. Boca Raton, FL: CRC Press. Retrieved From: https://www.amazon.in/Cyber-Deception-Building-Scientific-Foundation-ebook/dp/B01IK5JBEC
Jones, T., & O’Neill, J. (2018). Cyber security Challenges in Critical Infrastructure Protection: A Case Study of the Energy Sector. Journal of Cyber security, 3(2), 189-203. DOI: https://doi.org/10.1016/j.hrmr.2017.06.002
Kopalle, P. K., et. al(2020). How legacy firms can embrace the digital ecosystem via digital customer orientation. Journal of the Academy of Marketing Science, 48, 114-131. Retrieved From: https://link.springer.com/article/10.1007/s11747-019-00694-2
Mettikolla, P., et. al(2011). Cross-bridge kinetics in myofibrils containing familial hypertrophic cardiomyopathy R58Q mutation in the regulatory light chain of myosin. Journal of theoretical biology, 284(1), 71-81. DOI: https://doi.org/10.1016/j.jtbi.2011.06.014
Mettikolla, Pet. al(2010). Kinetics of a single cross-bridge in familial hypertrophic cardiomyopathy heart muscle measured by reverse Kretschmann fluorescence. Journal of Biomedical Optics, 15(1), 017011-017011. DOI: https://doi.org/10.1117/1.3324871
Mettikolla, P. et. al(2010). Observing cycling of a few cross bridges during isometric contraction of skeletal muscle. Cytoskeleton, 67(6), 400-411. DOI: https://doi.org/10.1002/cm.20453
Mettikolla, P. et. al (2009). Fluorescence lifetime of acting in the familial hypertrophic cardiomyopathy transgenic heart. Biochemistry, 48(6), 1264-1271. Retrieved From: https://pubs.acs.org/doi/10.1021/bi801629d
Vegesna, V. V. (2018). Analysis of Artificial Intelligence Techniques for Network Intrusion Detection and Intrusion Prevention for Enhanced User Privacy. Asian Journal of Applied Science and Technology (AJAST) Volume, 2, 315-330. Retrieved From: https://www.researchgate.net/publication/374949463_Analysis_of_Artificial_Intelligence_Techniques_for_Network_Intrusion_Detection_and_Intrusion_Prevention_for_Enhanced_User_Privacy
Vegesna, V. V. (2019). Investigations on Different Security Techniques for Data Protection in Cloud Computing using Cryptography Schemes. Indo-Iranian Journal of Scientific Research (IIJSR) Volume, 3, 69-84. Retrieved From: https://www.researchgate.net/publication/374949515_Investigations_on_Different_Security_Techniques_for_Data_Protection_in_Cloud_Computing_using_Cryptography_Schemes

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