AI to Eliminate Poor Human Choices in Trading: Reasons for the Positive Impact

Are you aware that between 70% and 80% of retail traders incur losses?

Regulators in both Europe and the U.S. have verified this statistic so frequently that brokers now routinely display it as a disclaimer on their websites.

The common narrative attributes the failures to the traders themselves. They often lack discipline, pursue losses, and panic at inopportune times. While this perspective holds some truth,

it overlooks the fundamental issue at play. Retail platforms were never intended to assist users in making sound decisions. Instead, they were created to ensure that users made frequent choices.

Every price alert, every red or green signal, and every buy and sell button places the trader in a high-pressure situation where human psychology works against them.

Indeed, retail traders are influenced by emotions. However, it is the platforms that have engineered these emotional triggers and labeled it as market access. Yet, for the first time, there may be a solution to escape this predicament.

Why Losses Are More Painful Than Wins Are Pleasurable

In 1979, Daniel Kahneman and Amos Tversky introduced a theory that ultimately earned Kahneman a Nobel Prize. Prospect theory revealed that individuals do not evaluate gains and losses in the same manner.

A loss is perceived as approximately twice as distressing as an equivalent gain is perceived as gratifying.

Kahneman illustrated this with a coin flip scenario. He would present students with a gamble where tails resulted in a ten-dollar loss. Most students insisted on at least twenty dollars on the winning side before agreeing to the wager.

On paper, a fifty-fifty chance at ten dollars in either direction should be a neutral proposition. However, students would only accept it if the potential gain was double the potential loss.

This imbalance clarifies much of the behavior observed in volatile markets. Following a win, confidence surges dramatically, prompting traders to increase their position sizes and disregard risk limits.

The most concerning aspect, however, is the aftermath of a loss. The discomfort incites a desperate urge to recover, leading to revenge trades, increased positions, and neglected stop-losses.

Witnessing Bitcoin decline by 15% at 3 a.m. will make you feel the essence of Kahneman’s theory. The logical action is to close the app and reassess in the morning. The instinctive reaction, however, is to fixate on the screen, heart racing, finger poised over the sell button, convinced that taking action will alleviate the discomfort.

Established platforms do not attempt to mitigate these impulses. Instead, they amplify them. This explains why 75% of day traders abandon the market within two years (and why the remaining 25% likely should have).

The Superior Use Case Was Always Present

A significant portion of the AI discourse in finance centers around prediction. Can algorithms outperform the market? Can they identify patterns that humans cannot discern?

Moreover, many of these discussions regard AI as a substitute for human judgment.

However, there exists a more effective application for AI in trading. AI can serve as a behavioral infrastructure, acting as a buffer between traders and the precise moments when they (as statistically proven) make poor decisions.

When AI manages execution, the user is not left contemplating whether to hold or sell during a volatile session. Entry conditions, position sizes, and exit rules are predetermined. When events occur, the system adheres to the established rules, and the user learns the outcome later.

The emotional window in which panic or greed could take control simply does not exist.

While market complexity garners attention, the primary source of risk has consistently been human behavior under stress. AI provides a means to mitigate that risk by reconfiguring how and when decisions are made, rather than eliminating people from the process.

The human remains involved, just at an earlier stage. AI shifts judgment upstream, away from the heat of the moment. Users continue to set objectives, define risk tolerance, and select strategies.

What they no longer do is make impulsive decisions at 2 a.m. when the market moves against them and their nervous system urges them to act.

Some platforms already operate in this manner. They allow users to establish intent while the system manages everything else: strict risk protocols, ongoing adaptation, and execution.

2026 Could Finally Equalize the Landscape

Currently, approximately 60–70% of trading volume in major equity markets is algorithmic. Institutional investors have utilized tools like natural language processing since the 1990s to analyze news, filings, and sentiment data before retail traders were even aware of the headlines.

Retail traders have been competing against this for decades without access to similar tools. Only recently has the development of these systems become affordable for individuals outside of trading desks.

Cloud computing, exchange APIs, and accessible machine learning frameworks have significantly reduced the cost of creating advanced execution systems. What once necessitated a team of quantitative analysts and specialized hardware can now function on consumer-grade platforms or even local models.

The pivotal question for 2026 is whether retail platforms will embrace this new trend to develop innovative products or continue to profit from emotional trading alone.

This transition may not feel revolutionary in any sense. On the contrary, it will likely seem obvious in retrospect.

The volatility will persist, and losses will continue to occur. However, the self-inflicted harm resulting from trading under emotional pressure could finally become avoidable.

And that, more than any predictive algorithm, might distinguish the next generation of retail traders from the 75% who exit within two years.

Disclaimer: The views and opinions expressed in this article are those of the author and do not necessarily reflect the views of Cryptonews.com. This article is for informational purposes only and should not be construed as investment or financial advice.

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