For investing, not all AI tools are created equal

ARTIFICIAL intelligence (AI) has become a standard part of daily life in Singapore. Many people now ask chatbots about restaurants, renovation ideas or how to lower their golf handicap.

Extending that convenience into investing is an opportunity, especially in a country where retail participation in the markets has risen and financial literacy is a national priority.

Yet, the rapid adoption of AI masks an essential truth: not all AI is built to be a helpful tool for investors.

Some systems can offer useful signals, provide investing context and even generate sensible stock screens. Others, particularly those that apply superficial changes to large language models (LLMs), are simply not designed to understand how investing actually works. Knowing the difference matters more now than ever.

The issue is not whether AI can support better investment decisions. It absolutely can. The real question is which AI you are relying on.

Where general AI falls short

General-purpose AI excels at conversation, pattern recognition and synthesising information across many domains. It was never trained to weigh investment risk, comply with regulation or understand the structural forces that move markets.

This misalignment shows up in three practical ways.

The first is hallucination, the now-familiar tendency of some models to supply confident but incorrect statements. In everyday life, this may lead to harmless, trivial mistakes. In investing, it can mean citing invented financial ratios, misinterpreting earnings releases or fabricating analyst consensus.

Retail investors have already circulated examples of AI tools producing non-existent cash-flow data or attributing remarks to executives who never made them.

The second weakness is the lack of context of an individual’s portfolio. A general model very often doesn’t leverage a user’s portfolio information to provide answers – an important signal that differentiates publicly available feeds from personalised insights. A general model may also lack the guard rails that can help avoid misuse. For example, it might offer investing advice to a user, even though this is a regulated activity.

The third limitation is structural. Choosing stocks is only a small part of proper investing. True decision-making depends on understanding what is in someone’s overall portfolio, as well as the time-honoured wealth-management principles of diversification, asset allocation, time horizons, tax considerations and risk-adjusted return.

Investing is made better by expertise. You wouldn’t ask financial advice from someone who’s not qualified to give it, so why would we entrust general-purpose LLMs to do so?

What good financial AI looks like

Purpose-built financial AI works differently. These systems are trained on company statements, market data and financial data sets rather than just Internet text. They are designed to produce more consistent, auditable outputs, and they integrate techniques that general models simply do not include.

Strong AI built for investing tasks typically has several defining features. These include incorporating deterministic elements – that is, rule-based engines or optimisation models – that ensure calculations such as position-sizing, market prices and portfolio construction are transparent and repeatable.

AI for investing should also build in explainability. Investors can interrogate why a model produced a particular conclusion, and from what data it drew its outputs. This provides a foundation of trust that general-purpose chatbots cannot match.

And, good financial AI maintains proper data governance. It uses trusted data sources, keeps clear audit trails and behaves in a way that satisfies expectations.

The Monetary Authority of Singapore has spent years shaping the principles of fairness, ethics, accountability and transparency, or Feat, for financial AI. Systems designed for wealth management follow these principles by design.

Human layer matters

AI can elevate investing capability, but the final layer must still be human.

Investors bring context that no model has: family goals, life-stage priorities, risk tolerance and emotional reactions to volatility. A good AI engine acts as a second pair of eyes, providing analytical discipline and clarity. The investor provides judgment, experience and values.

Used well, AI can democratise access to high-quality financial insight. It can reduce behavioural biases, help people understand their own risk appetite, and encourage focus on long-term outcomes rather than market noise. It can also help investors avoid the common pitfalls of chasing momentum or over-concentrating in familiar sectors.

Singapore’s investing landscape is maturing quickly. AI will become a standard part of how individuals and their advisers analyse markets, construct portfolios and plan for the future. The question is not whether to use AI in investing, but which AI, and under what safeguards.

Not all AI is created equal. For investors, understanding that difference may prove to be one of the most important financial skills of the coming decade.

The writer is chief executive officer and founder, Arta