AI use in investment decision-making

Source article: ROLE OF ARTIFICIAL INTELLIGENCE IN INVESTMENT DECISIONS

Investment behaviour has changed as a result of artificial intelligence (AI), which replaces human judgment with data-driven insights. Investors may analyse big information, spot trends, and optimize portfolios with the help of technologies like machine learning, robo-advisors, and predictive analytics, increasing productivity and lowering emotional biases. Adoption of AI is not without its difficulties, though, such as algorithmic bias, a lack of transparency, expensive costs, and problems with data quality. Au…

By the Truvace deskOctober 19, 20262 min read1 sourcePeer-reviewedTRV-2026-1902· cite & verify

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P 72

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G 72

In brief

This peer-reviewed study examines how artificial intelligence is changing investor behaviour by shifting decisions from human judgment toward data-driven insights. It names machine learning, robo-advisors and predictive analytics as the main tools in use.

The balance of benefits and risks matters because investing decisions increasingly rely on automated systems. The abstract calls for a balanced, responsible approach, but it provides no measured outcomes, so the size of both the gains and the risks stays unquantified.

Main points

AI replaces some human judgment in investing with data-driven insights.
Tools named include machine learning, robo-advisors and predictive analytics.
Claimed benefits are better analysis of big data, trend spotting, portfolio optimization, higher productivity and fewer emotional biases.
Listed difficulties are algorithmic bias, limited transparency, high costs and data quality problems.
Overuse of automation may increase automation bias and market volatility.

The gain

Machine learning, robo-advisors and predictive analytics let investors analyse large data sets, spot trends and optimize portfolios, raising productivity and reducing emotional bias.

The problem

Adopting AI in investing brings algorithmic bias, poor transparency, high costs and data quality problems, and overuse of automated systems may heighten automation bias and market volatility.

The rundown

The study frames AI as a change in how investors behave, with algorithms and analytics taking over tasks once handled by human judgment, such as analysing information and optimizing portfolios.

On the downside, the abstract says adoption is not without difficulty. It lists algorithmic bias, a lack of transparency, expensive costs and data quality issues as obstacles for investors and firms.

The authors warn that excessive reliance on automated systems could amplify automation bias and market volatility, and they argue for a balanced, responsible way to integrate AI into investment decision-making.

What this doesn’t fix

The supplied text is only an abstract-level description and gives no data, methods or measured results, so the claimed benefits and risks are not quantified; volatility effects are stated as possibilities.