ARTIFICIAL intelligence is no longer just a tool that responds to commands. A new generation of systems, often described as agentic AI, can operate autonomously, make decisions and adapt continuously with minimal human intervention.
In retail investing, these systems are increasingly used to analyse market trends, assess portfolio risk and generate investment recommendations at a speed and scale previously available only to large institutions.
For retail investors, this promises wider access to sophisticated analytics. Yet smarter systems do not automatically produce safer decisions. The real issue is not how powerful AI has become but how responsibly it is deployed and used.
Lesson 1: Advanced AI cannot fix poor data
Agentic AI systems depend heavily on the quality of their inputs. Many platforms now combine traditional financial data with alternative sources such as online sentiment, news feeds and other unstructured digital content. When these inputs are incomplete, inconsistent, or biased, even the most advanced models can generate misleading signals.
Retail investors may assume AI outputs are inherently objective. Recommendations are only as reliable as the data behind them.
Platforms that clearly disclose their data sources, validation processes and limitations offer stronger safeguards than those that market AI as an all-knowing solution.
Lesson 2: Transparency matters more than complexity
As models grow more sophisticated, many function as “black boxes”, providing recommendations without explaining how they were derived.
For retail investors, this lack of interpretability creates risk.
Without understanding the reasoning behind an investment suggestion, investors cannot properly assess whether it aligns with their risk tolerance, financial goals or time horizon.
Transparency is therefore not a technical luxury but an investor protection mechanism.
Platforms that prioritise explainability, clear disclosures and bias-mitigation practices are better aligned with responsible investing than those that compete solely on predictive accuracy.
Lesson 3: Regulation is a signal of credibility
The rapid adoption of AI in finance has often outpaced regulatory clarity. For retail investors, this creates uneven levels of protection across platforms.
Compliance with data privacy, auditability and governance standards is not merely a legal requirement; it signals whether a platform is built for long-term reliability.
In Malaysia, oversight by institutions such as Bank Negara Malaysia and the Securities Commission Malaysia plays a critical role in maintaining trust in digital financial services.
As AI-powered tools become more common among retail investors, including those in Sarawak, regulatory alignment will increasingly shape market confidence. Investors should treat documented governance structures and clear model oversight as indicators of platform credibility.
Lesson 4: Full automation still needs human judgment
Agentic AI can process vast amounts of data faster and more consistently than humans.
However, sudden geopolitical developments, regulatory announcements or firm-specific events often require contextual interpretation that automated systems may struggle to capture in real time. Many platforms are therefore moving toward hybrid models, where AI handles data-intensive analysis while human experts provide oversight in complex or high-risk situations.
For retail investors, the practical lesson is straightforward: AI recommendations should be advisory rather than prescriptive.
Retaining human judgement helps balance efficiency with accountability.
Smarter use, not blind trust
The expansion of agentic AI in retail investing reflects a broader shift toward data-driven financial decision-making.
However, successful investors are unlikely to be those who rely most heavily on automation but those who engage with it critically.
Prudent investors should question how AI tools generate recommendations, demand transparency and regulatory alignment, cross-check outputs with independent analysis and maintain oversight in significant investment decisions.
Technology can enhance decision-making but it cannot replace informed judgement.
Accountability as the next competitive edge
For Malaysia’s financial ecosystem, including banks, fintech firms and digital investment platforms, the rise of agentic AI presents both opportunity and responsibility.
As retail participation in digital investing grows, institutions must move beyond deploying faster algorithms and focus instead on building trustworthy AI frameworks.
Strong governance, transparent model design and effective human oversight will be central to sustaining investor confidence.
Looking ahead, the competitive advantage will not go to platforms with the fastest AI but to those with the most accountable AI.
In an increasingly automated investment landscape, trust, transparency and regulatory alignment will define sustainable growth.
For Malaysia’s evolving capital market, responsible adoption, rather than rapid adoption alone, will determine which institutions lead in the next phase of AI-driven finance.
Way forward
Advanced AI cannot compensate for weak or biased data and retail investors should understand that algorithmic outputs are only as reliable as the information feeding them.
Platforms that blend financial statements with sentiment and news analytics can produce distorted signals if those inputs are flawed. Transparency matters more than model complexity; black-box recommendations limit an investor’s ability to judge suitability and risk.
Regulatory oversight, including by Bank Negara Malaysia and Securities Commission Malaysia, signals stronger governance standards.
Ultimately, AI should support but not replace human judgement, especially during volatile or unforeseen market events.
For SMEs and finance companies, advanced AI is only as strong as the data governance behind it. Poor-quality financial records, fragmented customer data or biased alternative inputs can distort credit scoring, risk assessment and forecasting models. Complexity alone does not create value while transparent methodologies, explainable outputs and clear validation processes reduce operational and compliance risk.
Regulatory alignment, particularly with standards set by Bank Negara Malaysia and Securities Commission Malaysia, signals institutional credibility and long-term resilience.
Eventually, hybrid frameworks that combine AI efficiency with experienced human oversight offer SMEs and finance companies stronger, more accountable decision-making in volatile environments.
• Dr Leong Choi Meng, School of Business, Faculty of Business, Design and Arts, Swinburne University of Technology Sarawak Campus
The views expressed here are those of the writer and do not necessarily represent the views of Sarawak Tribune. The writer can be reached at mvoon@swinburne.edu.my.
