Artificial intelligence (AI) is making its way into investing and trading, bringing new tools and approaches for both retail and institutional participants. As financial markets become more complex and data-driven, AI can be seen as a tool that many explore to assist with decision-making, risk management, and portfolio strategies.
“AI offers a strategic advantage during market fluctuations by enabling better, faster decision-making,” says Anand Mahurkar, Founder and CEO at Findability Sciences.
Expanding access to advanced tools
AI technologies are becoming more available across segments of the market.
Some investors use AI-driven platforms to screen stocks, assess fundamentals, or monitor market developments.
“AI effectively bridges the gap between retail investors and fund managers by democratising intelligence,” says Kunal Nandwani, Founder and CEO of uTrade Solutions.
For example, by entering prompts like “find small-cap IT companies with strong fundamentals,” investors can receive curated suggestions, with AI analysing factors like management quality and revenue growth.
Managing information flow
Markets generate constant streams of data. AI can help process this information and highlight relevant signals.
“AI filters and interprets market news, company announcements, and sentiment on personal portfolios, turning information overload into strategic advantage,” Nandwani notes.
Traders may also use AI to automate strategies such as long-short positions, adjusting for set risk levels or timeframes.
Supporting portfolio strategies
Beyond individual trades, AI is being used to support broader portfolio management — from risk assessment to asset allocation and hedging strategies.
“AI systems today are designed to adapt to rapidly changing market conditions, improving resilience during periods of stress,” says Mahurkar.
Recognising challenges
The adoption of AI in financial markets raises questions about transparency and potential market impact. Complex AI models can make it difficult to explain decision-making, while the use of similar algorithms across firms may contribute to synchronised behaviours.
“Techniques like explainable AI are critical to building trust and ensuring transparency in decision-making,” Mahurkar points out.
Regulatory bodies have started to explore the implications of AI in trading, looking at both the opportunities and risks involved.
“Balancing innovation with risk management and regulatory compliance is essential to harness AI’s potential without compromising market integrity,” Mahurkar adds.
