What AI bots promise today almost sound like utopia, personalized strategies, increased efficiency and enhanced risk management. But behind all this efficiency lies a certain fragility.

AI bots has changed the way people trade in the stock market. Instead of humans placing every trade, computers now use special programs to buy and sell stocks very quickly and accurately. These programs can study market data, find good trading chances in seconds, and make trades automatically. This means trading will keep getting faster and more efficient in the future.

All these AI bots are made keeping in mind the same goals, which removes the necessary “slow-down effect” that human emotion used to provide. When the human element is gone, market shocks are no longer slowed down they are accelerated and amplified, creating a very high risk of Algorithmic Systemic Fragility.

Algorithmic Systemic Fragility is not just a problem for individuals or companies but a very big issue for the whole market as millions of system all act the same at the very same instant.

Algorithmic Systemic Fragility is the risk when multiple AI bots who use the same rule book act together in a crisis situation, this causes the entire market to be prone to instant shock or collapse.

Other things which is being discussed is the removal of the human hesitancy or pause In the past, human hesitation (like being slow to sell due to the pain of taking a loss) acted like a natural speed bump in the market. This slowness was inefficient but kept the entire system stable during a panic.

The problem with having the same rulebook

AI advisors all act the same because they follow the same rules. These rules include balancing risk and reward, using risk limits such as Value- at-Risk (VaR) to decide when to sell or buy. Since all AI advisors try to attain the same goal of maximizing profit using similar rules, their investment choices end up being same. For example, if a major investment like a tech index fund falls, every AI advisor programmed to minimize risk will sell, triggering a widespread, synchronized reaction across many accounts.

AI advisors mostly rely on simple online questionnaires to understand an investor’s profile, focusing on basic factors like age, goals, and risk tolerance. Because of this limited input, they often miss important financial details such as existing assets, debts, future financial plans, or income fluctuations. This lack of deeper information can lead to recommendations that don’t fully match an investor’s real financial situation.

The plans suggested by AI bots don’t really change to fit individual needs or unique goals, and the AI advisor doesn’t fully understand why someone makes certain financial choices.

Blind spots in government rules

The existing regulations of the government and financial stability stress tests are established in a manner that verifies whether individual businesses will be able to last through a crisis. Regulatory organizations such as FINRA and the ECB are now adopting the need to ensure that firms possess controls, audit trails and testing to ensure that an individual algorithmic mistake (such as the Knight Capital crash) or an act of manipulation (such as spoofing) does not occur.

They, however, overlook considerably the risk of the all the companies working in the same way. The greatest danger, as the IMF and other central banks have, is the so-called monoculture effect, when all the hundreds of thousands of personal protection mechanisms programmed in these bots, the so-called kill switches, meant to keep them safe, all go off at the same millisecond, in reaction to a shared market shock.

This co-ordinated triggering results in a negative feedback mechanism that can collapse the whole market within a short period of time.

It is necessary to switch attention to not only the checking of the individual firm strength but also the imposition of strategy diversity at the system level.

Financial Firms Future Opportunities.

The greatest opportunity that can be realized by banks and investment firms that hope to succeed in this new environment is to develop Real Unique, Crisis-Proof Strategies. It is probable that the future gains of the next ten years will be rewarded to those companies that have model approaches that are not ready to join the algorithmic waterfall.

Its objective should not be to construct the most effective program based on the previous standard rules. Rather, an investment program intentionally different and unrelated to the popular algorithmic herd in the case of a crisis will be the final competitive edge.

 Three Innovative and Smart Goals For Firms

Go Unique Risk Metrics: Explore how to quantify risk beyond moving prices (such as examining spikes in the trading volume or using state-of- the-art machine learning algorithms to discover non-traditional relationships).

Install Controlled Friction: Have the algorithm intentionally slow down or go random when a crisis occurs, so that people can sell off their assets instantly.

Promote Diversity: Construct algorithms that actively monitor the action of all the other users and intentionally select a opposite or unconventional approach to the market when it has become too homogenous.

Conclusion

The future of innovation is not to halt, but to compel strategy diversity. It is only by making certain that the various investment programs employ a huge variety of logics that the financial system can be re-established at the stability with which it had lost itself in the quest towards perfect, and perfectly dangerous, efficiency.