Agentic Automation applied in securities firms can significantly enhance operational efficiency, especially as the industry undergoes a transformation driven by AI and automation technologies. With increasing transaction volumes and compliance demands, traditional automation models are revealing their limitations. Agentic Automation – with its ability to plan, make decisions, and execute toward goals—offers a breakthrough to help securities companies overcome manual operational challenges, improve efficiency, reduce risk, and elevate customer experience.
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Challenges of Manual Operations in Securities Firms
Securities firms operate in a tightly regulated environment, handling massive volumes of data and real-time transactions. Manual operations across many business processes have created significant bottlenecks, hindering growth and cost optimization potential.
| Challenge | Detailed Description |
| Data and Market Insights | Speed and Volume of Processing |
| Tasks such as account opening, fund transfers, transaction reconciliation, and manual document handling are time-consuming, especially during market surges. | |
| Reports show that only 11% of 493 listed companies on HOSE held online shareholder meetings (2024), indicating slow adoption of modern technologies. Delays in processing can lead to missed trading opportunities and reduced customer satisfaction. | |
| Error Risk and Compliance | Manual data entry and reconciliation are prone to human error. In finance, even minor mistakes can lead to significant losses and regulatory violations. |
| Corporate governance in Vietnam still faces limitations. The average governance score of listed companies on HOSE is only 50.60/140 (2024), reflecting weak governance practices and risk control (HOSE). | |
| High Operational Costs | Labor costs for repetitive back-office tasks are substantial. Market growth demands proportional staff increases, making cost optimization difficult. |
| Securities firms must invest heavily in infrastructure modernization, IT, and administrative reform (Financial Magazine), but eliminating manual paperwork remains a persistent challenge. | |
| Limitations in Analysis and Decision-Making | Employees spend most of their time on administrative tasks, leaving little room for high-value work like financial consulting or market analysis. |
| Experts highlight that only 6% of firms publish detailed board evaluations and 28% identify key ESG topics (2024), showing a lack of focus on strategic, high-value factors. |
Agentic Automation: Optimizing Operations and Solving Challenges
Automation is the inevitable answer to these challenges. However, a new leap has emerged: Agentic Automation.
Comparing RPA and Agentic Automation
| Criteria | Robotic Process Automation (RPA) | Autonomous AI Assistant Automation (Agentic Automation) |
| Operating Mechanism | Executes repetitive, rule-based tasks. Mimics human actions on user interfaces. | Executes complex goals with planning, decision-making, and learning capabilities to handle dynamic scenarios and exceptions. |
| Data Handling | Primarily structured data. | Flexibly handles complex unstructured data (reports, emails, legal documents, market sentiment). |
| Adaptability | Low flexibility; requires reprogramming when processes or interfaces change. | Highly adaptive; adjusts task strategies when encountering obstacles or new information. |
| Human Intervention | Requires human oversight for exception handling. | Operates autonomously, with human intervention only at risk thresholds or final approvals. |
| Typical Applications | Copy/paste data, invoice reconciliation, simple report generation. | Risk analysis, personalized investment advisory, multi-step business process automation. |
Agentic Automation leverages Agentic AI—combining the power of Large Language Models (LLMs) and process automation (RPA, IPA)—to create software agents with a “brain” that can think and act toward a holistic goal.
Use Cases of Agentic Automation in Securities
Agentic Automation is not just a speed enhancer—it’s a strategic lever for core activities in securities firms:
| Use Case | Process Overview | Estimated Value |
| eKYC and Onboarding | Autonomous AI agents receive documents (ID photos, signatures, videos), verify and cross-check data with internal systems and external databases, analyze risk, and complete account opening without continuous human intervention. | Reduces processing time by 80–90% (from hours/days to minutes), increases data accuracy to 99.5%, boosts customer conversion speed by 30%. |
| Market Analysis and Portfolio Management | AI agents continuously monitor thousands of news sources, financial reports, and transaction data. They analyze market sentiment, assess portfolio risks, and generate personalized investment recommendations. | Cuts data collection and analysis time by 40% for analysts. Improves short-term forecast accuracy by 15%. |
| Customer Request Handling and Automated Support | Autonomous AI assistants not only answer queries but also make decisions to execute tasks (e.g., info updates, rights registration) by interacting directly with back-office systems. They handle complex complaints and escalate intelligently. | Reduces repetitive workload by 70% for call center staff. Increases customer interaction and satisfaction (CSAT) by 30%. |
| Compliance Reporting and Risk Monitoring | AI agents automatically collect transaction data, cross-check with hundreds of regulatory rules (e.g., insider trading, money laundering), detect suspicious patterns, and generate timely, detailed compliance reports. | Saves hundreds of labor hours monthly for compliance teams. Reduces penalty risk by 25% due to delays or errors in reporting. |
Lessons Learned for Successful Agentic Automation Adoption
To fully unlock Agentic Automation’s potential, securities firms need a structured strategy that goes beyond isolated automation projects:
Start with High-Value, Complex Processes
- Identify the biggest pain points: Focus on multi-step, complex workflows involving unstructured data and requiring decision-making (e.g., Risk Management, Market Analysis, Complex Customer Onboarding).
- Orchestration Strategy: Build a smart Business Process Orchestration platform so Agentic AI can coordinate traditional RPA bots, APIs, and humans to achieve overarching goals.
Prioritize Data Quality and Technology Infrastructure
- “Data is the lifeblood”: Agentic AI performs best with high-quality data. Firms must prioritize standardizing, cleaning, and integrating data systems (ERP, CRM, Core Securities System) to ensure agents can access and reason with accurate, real-time information.
- Focus on Security and Compliance: Firms must ensure platforms comply with strict security and privacy regulations. Regular cybersecurity audits are mandatory.
Build Teams and a Culture of Transformation
- Upskilling: Invest in training current staff to work with and manage AI agents, rather than just performing manual tasks. Teams need skills in data analysis, machine learning, and AI ethics.
- Change Management: Communicate clearly that AI agents are “smart assistants” helping employees focus on high-value work (consulting, creativity), not full replacements.
- Ethical AI Approach: Ensure AI agents operate transparently, fairly, and responsibly—especially in decisions involving customers and investment risks. Establish regular oversight and auditing mechanisms for agent decisions.
Agentic Automation marks a major leap forward, ushering securities firms into a new era of comprehensive automation—where business tasks are not only executed faster but also smarter, more flexibly, and autonomously. It’s the driving force for firms to strengthen their core capabilities, optimize costs, and compete effectively in an increasingly complex market landscape.
