While AI offers advantages in fund management, it also comes with risks. Here are some of the key risks:

1. Model Risk

• AI models learn from historical data, which may make them ineffective during changing market conditions. 

• Over reliance on AI may lead to distorted predictions and poor investment decisions.  This is especially true during Pandemics, Regional Conflicts, Trade Wars, Geopolitical Turmoil, Bank Failures, Climate Disasters, and more. 

2. Lack of Transparency

• Many AI-driven strategies operate as "black boxes," making it difficult for fund managers to explain investment decisions.

3. Data Dependency

• Incomplete, outdated, or manipulated data can lead to incorrect investment signals.

4. Market Instability

• AI-driven high-frequency trading (HFT) can amplify market swings and trigger flash crashes.
• AI models may reinforce market trends, leading to bubbles or abrupt corrections.

5. Cybersecurity

• AI systems handling sensitive financial data are prime targets for cyberattacks.
• Unauthorized access to AI-driven strategies could lead to market manipulation or insider trading risks.

6. Compliance Challenges

• AI-driven decisions that violate regulations (e.g., unfair trading practices) could result in hefty penalties.

• AI-based models might prioritize profitability over ethical considerations.

7. Dependence on Historical Data & Black Swan Events

• AI struggles to predict rare, unforeseen events (e.g., COVID-19, geopolitical crises) that lack historical precedence.
• AI models trained in past market conditions may fail during unprecedented disruptions.

These risks need to be taken into consideration.  Human oversight is always essential to detect AI-driven errors. 

In addition, Risk Management methods like stress testing, sensitivity analysis and other validation models may be useful in monitoring the effectiveness of AI in fund management.