AI and Financial System: The 6 Created Vulnerabilities

The World Economic Forum has identified 6 risks/vulnerabilities in the financial system related to new technologies and AI.

Here they are:

  1. Technological and financial barriers to the production of machine learning algorithms are rapidly diminishing, making the proliferation of deep fakes and synthetic botnets a growing problem. This is exacerbated by the increasing sophistication of deep fake technology, which makes it harder for fraud detection software to identify attacks.
  2. While the cryptocurrency market is largely isolated from traditional markets due to low institutional exposure, if cryptocurrency regulation were to be introduced, it could increase trust levels and lead to a surge of interest from both retail and institutional investors. This is the market most at risk for a deep fake and botnet attack.
  3. Buy now, pay later (BNPL) platforms are becoming increasingly popular, offering consumers easy access to financing with reduced credit approval processes. However, this ease of access also creates opportunities for overindebtedness, exacerbated by limited BNPL debt reporting requirements.
  4. Social media echoes can reinforce speculation and biases, creating market volatility. In recent years, we have witnessed the rise of “meme stocks,” where asset prices have disconnected from a company’s underlying value and were driven by social media speculation. Algorithms on platforms like Twitter and Reddit amplify stock volatility by creating echo chambers where investors communicate with others who share similar opinions, reinforcing speculation.
  5. Compromised data generated by sensors could be used to manipulate investors.
  6. The broader use of distributed ledger technology (DLT) creates more opportunities for attack. Central bank digital currencies (CBDCs) are being explored by numerous countries as a way to improve payment efficiency and expand access to the financial system. One method for implementing CBDCs relies on distributed ledger technology (DLT), which uses cryptographic methods to store transaction data from multiple entities. However, involving multiple parties increases the number of attack points available to malicious actors.


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