Artificial Intelligence in investment: opportunities, risks and good practices

Artificial Intelligence (AI) ceased to be a novelty to become a cross-cutting tool in the financial ecosystem. It identifies patterns in large volumes of data, summarizes complex information, interprets natural language, and executes tasks in seconds. When investing, it allows accelerating analyses, detecting market signals that would go unnoticed, customizing recommendations to the risk profile, and automating operational processes with a scale and speed that human teams cannot consistently match.

‍

In practical terms, AI helps to monitor portfolios with risk alerts, to support asset allocation with objective data, to answer questions based on reliable documentation, and to detect anomalous patterns. The desired effect is simple: more informed decisions, lower costs, and better investor experience.

‍

There are, however, risks to control. Incomplete or biased data weakens recommendations; lack of transparency makes it difficult to explain decisions; wrong but credible answers can lead to errors; and the use of personal data requires respect for the GDPR. For this reason, human supervision, data quality, periodic tests and clear explanations about the operation of the tool are essential, as well as a critical analysis of each result: comparing with other sources, questioning assumptions and only then integrating it into the decision.

AI encompasses systems capable of “acting rationally” to achieve concrete objectives, from the automatic reading of information to the recommendation or decision. In investment, it applies to the analysis of market data and news, portfolio management, fine-tuning of trading algorithms, customer service via virtual assistants, and robo-advisors, risk assessment and fraud prevention.

‍

In practical terms, It adds speed and depth to analysis, improves execution and reinforces control, without replacing professional judgment. In decisions with a material impact on the investor, human validation remains mandatory. Any use must respect internal policies, conflict of interest management, and clear limits of action.

‍

Where AI adds value to investors

‍

AI allows us to look at more information, in less time, with greater rigor. It translates into more complete analyses, near-real-time risk monitoring, more efficient execution, and clearer and more available customer service. The result is simple: better informed decisions, controlled costs, and consistent processes throughout the investment cycle. In practice, this value materializes on four fronts:

‍

• Personalization: adjusts products and recommendations to the risk profile, objectives and preferences, with proposals that are more consistent with each investor.
• Decision support: Crosses data in (near) real time to identify relevant scenarios, opportunities, and risk alerts.
• Reduced costs and increased availability: automates tasks, speeds up processes and improves customer service, including 24/7.
• Financial inclusion: facilitates access to clear information and quality tools, reducing barriers to entry.

‍

The risks that are important to know

‍

The same technology that accelerates analyses and improves processes also introduces weaknesses that cannot be ignored. To protect the investor and the quality of the decisions, it is important to recognize at an early age where the main risks lie and to treat them with processes, reliable data, and human oversight. In the spotlight:

‍

• Data quality and bias: incorrect conclusions if the data are incomplete or biased.
• “Black box” and limited explicability: difficulty understanding how the system arrived at a particular recommendation.
• Privacy: collection and processing of personal data requires compliance with the GDPR.
• Overdependence and misleading information: plausible but wrong answers can induce inappropriate decisions; fraud can also exploit these tools.

‍

What changes for professional management

‍

For those who manage OIC (funds or corporations), AI opens space for faster and more informed processes. However, it requires Data Governance, clear explicability criteria, robustness tests, and responsible use policies, to protect investors and comply with regulators. The recommendation is clear: Will use AI as support, not as a substitute for critical judgment and the fiduciary framework.

What changes in the regulatory framework (AI Act, ESMA, MiFID II)

‍

The use of AI in investment services ceased to be a technological topic and moved on to the central regulatory issue: who is responsible, how the decision is explained, and what controls exist. There are other relevant frameworks, but for the practical purposes of this article, the focus is on the two pillars that guide decisions today: the AI Act (calendar and nuclear obligations) and the ESMA (accountability, transparency, testing, and human oversight). The objective is to clarify what changes, who has to act and what controls are required in the AI solutions used in the investment.

‍

1. AI Act (European AI Regulation)

•  February 2, 2025 — specific prohibitions and literacy/transparency obligations.
•  Aug 2, 2025 — rules for general-purpose AI models.‍
• Aug 2, 2026 — application Full Of the regulation (with transitions for high risk cases)

‍

Key requirements: mapping of AI use cases; risk assessment; Human Oversight; data quality and governance; records and Logs; clear information whenever the customer interacts with AI; security and Cyber by drawing.

2. ESMA (European Market Supervisor)

• Responsibility of the management body through the use of AI and the results for the customer.
• Customer interest first: “clear, correct and not misleading” information when there is interaction with chatbots/assistants.
• Ongoing testing and validation of the models; bias control; auditable documentation.
• Explanability Proportional to the risk and Human-in-the-Loop in material decisions.
• Outsourcing: using third-party AI does not transfer responsibility; requires due diligence and control.

‍

AI can accelerate analysis, improve service, and support decisions with more information. Real value comes when technology is combined with rigor, transparency and appropriateness to the investor profile. For professional management structures, responsible adoption implies processes, control and oversight - always with the investor's interest first.

At Nexa, AI works as a support tool under human responsibility. The team ensures data and privacy standards, validates models with regular reviews, documents decisions, and communicates clearly with the investor. The objective is simple: better information, more rigor, and protection of the investor's interest.