Does the development of autonomous decision-making systems based on artificial intelligence and big data analytics in financial institutions improve transaction security and market stability, or does it increase the risk of unpredictable crashes?
Financial institutions are increasingly implementing technologies based on artificial intelligence and big data analytics for real-time decision-making, trade automation and anomaly detection. However, the growing dependence on autonomous decision-making systems raises concerns about possible algorithmic errors, lack of human control and the risk of stock market crashes caused by AI malfunctions.
The use of artificial intelligence in finance can result in significant improvements in the efficiency and transparency of transactions, but these systems are not without their flaws. Errors in AI models, unforeseen interactions of algorithms on stock exchanges or market manipulation can lead to destabilisation. Well-known cases such as the so-called flash crashes show that even small algorithmic errors can have catastrophic consequences. The question is therefore whether the development of autonomous financial systems should be strictly regulated or whether they should be given more freedom to optimise the markets.
Autonomous decision-making systems may improve transaction security and minimise the risk of financial fraud. However, over-reliance on algorithms can lead to unpredictable market fluctuations and increase the risk of financial crises. Therefore, appropriate regulations and supervisory systems can enable the safe use of autonomous technologies in finance.
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And what do you think about this?
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Please answer,
I invite everyone to the discussion,
Thank you very much,
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I invite you to scientific cooperation,
Dariusz Prokopowicz
Yes, the development of autonomous decision-making systems in financial institutions enhances transaction security by improving fraud detection, reducing human errors, and enabling real-time risk assessment. These systems use machine learning algorithms to identify suspicious activities, preventing unauthorized transactions more effectively than traditional methods. Additionally, automation ensures consistency in security protocols, minimizing vulnerabilities. However, challenges such as cybersecurity threats, regulatory compliance, and potential algorithmic biases must be addressed. Overall, while autonomous systems strengthen financial security, continuous monitoring and updates are essential to mitigate emerging risks.
L'IA est une arme à double tranchant. C'est un outil puissant qui peut être utilisé pour faciliter le processus de prise de décision en le rendant autonome ce qui améliore la transparence et limite le risque d'erreur humain. Toutefois, il ne faut pas oublier que la machine est programmée en fin de compte par des êtres humains qui lui inculque ses convictions ce qui peut engendrer des catastrophes au niveau des marchés financiers. Une surveillance et un contrôle rigoureux des algorithmes semble nécessaire voire même indispensable pour l'utilisation de l'IA
While autonomous decision-making systems can enhance transaction security by improving fraud detection, reducing human error, and enabling faster responses, they are not without risks. Financial institutions must carefully design, implement, and monitor these systems to ensure they are secure, transparent, and compliant with regulations. A balanced approach that combines automation with human oversight is likely the most effective way to maximize security while minimizing potential downsides.
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