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Leveraging AI for Real-Time Compliance Monitoring in Brokerage and Asset Management Platforms
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence
DOI: https://doi.org/10.64388/IREV10I1-1720027
Abstract
As financial markets have grown more complex over time, and regulators' requirements have changed more quickly, there is a growing need for effective compliance monitoring in brokerage and asset management platforms. Many compliance controls are still based on manual compliance checks, rule-based monitoring and periodic audits, but these may fail to identify more advanced financial irregularities and ensure compliance on the fly. Artificial Intelligence (AI) is a technology that has the potential to revolutionize compliance processes, powered by constant transaction monitoring, detecting anomalies in transactions, predictive analytics, and automated reporting. AI-based solutions utilize devices such as device learning, natural language handling, and deep learning to recognize dubious trading activity, uncover insider trading, avoid market manipulation, monitor AML compliance, and comply with Know Your Consumer (KYC) rules. This article explores how AI can be used in real-time compliance monitoring in brokerage and asset management platforms, highlighting the technologies being implemented, frameworks, regulatory implications, the advantages, and the barriers to use. In addition, it recommends an AI-based compliance architecture that unifies data ingestion, risk analytics, intelligent alerts detection and regulatory reporting into a single monitoring system. The review concludes that AI can be a powerful tool to enhance compliance efficiency, mitigate operational risks, and boost investor trust, while emphasizing the need for explainable AI, data management, cybersecurity, and human oversight in responsible AI adoption.
Keywords
Artificial Intelligence, Compliance Monitoring, Brokerage Platforms, Asset Management, Machine Learning, Regulatory Technology, RegTech, Financial Services
How to cite this paper
@article{1720027,
author = {Awogbade Kehinde, Asiyanbola Olaoluwa Ebenezer, Iyiola Ifeoluwa Johnson, Samson Precious Loveth, Omisope Abiodun Oluwasegun},
title = {Leveraging AI for Real-Time Compliance Monitoring in Brokerage and Asset Management Platforms},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {1},
pages = {2533-2545},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1720027.pdf},
abstract = {As financial markets have grown more complex over time, and regulators' requirements have changed more quickly, there is a growing need for effective compliance monitoring in brokerage and asset management platforms. Many compliance controls are still based on manual compliance checks, rule-based monitoring and periodic audits, but these may fail to identify more advanced financial irregularities and ensure compliance on the fly. Artificial Intelligence (AI) is a technology that has the potential to revolutionize compliance processes, powered by constant transaction monitoring, detecting anomalies in transactions, predictive analytics, and automated reporting. AI-based solutions utilize devices such as device learning, natural language handling, and deep learning to recognize dubious trading activity, uncover insider trading, avoid market manipulation, monitor AML compliance, and comply with Know Your Consumer (KYC) rules. This article explores how AI can be used in real-time compliance monitoring in brokerage and asset management platforms, highlighting the technologies being implemented, frameworks, regulatory implications, the advantages, and the barriers to use. In addition, it recommends an AI-based compliance architecture that unifies data ingestion, risk analytics, intelligent alerts detection and regulatory reporting into a single monitoring system. The review concludes that AI can be a powerful tool to enhance compliance efficiency, mitigate operational risks, and boost investor trust, while emphasizing the need for explainable AI, data management, cybersecurity, and human oversight in responsible AI adoption.},
keywords = {Artificial Intelligence, Compliance Monitoring, Brokerage Platforms, Asset Management, Machine Learning, Regulatory Technology, RegTech, Financial Services},
month = {July},
doi = {https://doi.org/10.64388/IREV10I1-1720027}
}