How Financial AI Is Revolutionizing Global Capital Markets

The risk-return tradeoff is a basic principle of finance: the lower the risk, the lower the expected rate of return. As artificial intelligence comes to dominate many aspects of life – finance included – the risk-return concept is starting to apply here as well. 

After all, it’s easier to keep using the same investment strategy because that’s how you always did it. But this low commitment approach carries an increasingly greater opportunity cost as investors widen their horizons through financial artificial intelligence.

What Is Financial AI?

According to IBM, financial AI is the “use of technology… to analyze financial data, automate tasks, and improve decision-making.” Just a brief selection of the ever-expanding applications of AI for finance includes transaction automation, customer service, fraud detection, predictive analytics, credit scoring, data and risk analysis, and compliance.

An Investment in Itself

With such a wide range of capabilities, financial AI looks like an exceptional opportunity. Market drivers include:

• Online banking services. AI supports convenient and safe remote banking transactions through functions related to client service, security, and customer experience optimization. 
• Competition. The popularity of AI solutions for finance has created a market of highly innovative players, along with a wide selection of winners and losers for investment consideration. 
• Investment management. With its ability to sift through massive amounts of data in real time, AI empowers financial companies to react quickly, execute trades, balance portfolios, and maximize returns. 

For these and other reasons, the financial AI market is projected to evolve at a CAGR of 17% between 2025 and 2033, at which point its market value will be USD 70 billion. 

However, the financial AI industry also faces its fair share of challenges. These include:

• Regulatory compliance. Any institution that provides buy/sell recommendations must follow certain regulatory standards. However, to date, AI services have not met these requirements.
• AI “hallucinations”. Issues with models and training information can lead to problematic results. Because AI cannot understand contradictions, it might generate mistaken or absurd output. For instance, this Economist article details how ChatGPT answered the question “what is the world record for crossing the English Channel on foot?”
• Data quality. For quality output, AI depends on the data streams that are defined by its programmers. When this data is faulty or biased, results suffer. One famous example of this is the controversy surrounding Google’s Gemini project.  

Using AI to Buy and Sell

A crucial area of specialization for financial AI is in the capital markets. AI for trading was once a complex field reserved for large institutions and their technical staff. However, a new generation of trading platforms expands this technology to the hands of individual financial professionals and retail investors. Some of the central applications of AI for investments are:

• Sentiment analysis. Trading is often affected by factors not related to company performance, such as macroeconomic data, geopolitical issues, and interest rates. AI has the ability to collect and analyze these types of data. 
• Algorithmic trading. This involves high-speed analysis of large datasets combined with rapid trading. AI looks at market trends and patterns, often with a focus on small price discrepancies. This results in gains that are only material in high volumes. 
• Risk management. By analyzing variables such as stock market data and volatility, AI can highlight risk factors in a process that is generally more efficient than traditional methods. 
• Personalized advisory. Generative AI, which learns the patterns of “training data” and generates output with similar characteristics, is being applied to trading platforms. Users can communicate with such platforms through simplified language while executing trades quickly and easily. 
• Portfolio optimization. AI allows fund managers to optimize portfolios through examining diversification, risk, income, and growth. The same technology can recognize when an investor might want to add certain stocks to their portfolio.

A Truly Global Capital Market

These applications are great when it comes to understanding data related to “popular” stocks. But there is literally another world out there when it comes to equities. Here are a few facts to illustrate this point:

• Only 20% of global equities are covered by analysts.
• 40% of global stock market value is accounted for by non-US stocks.
• Some analysts believe that the best area for high yield, low-priced stocks is the international market.

AI finally enables banks, exchanges, advisory firms, trading platforms, and media companies to gain new perspectives on the global market. AI-based analysis platforms allow investment professionals to look at a wider range of diversified stocks from around the world. Language barriers, company size, and location no longer need to limit analysts to covering a restricted range of companies. In turn, financial institutions have a much larger pool of recommendations to present to clients and leverage internally.  

A Must-Have for Capital Markets

This is not the end of spreadsheets and stock reports. Investors tend towards the instruments and industries that they know best, and so final buy/sell approval will still depend on human judgment. 

However, AI for capital markets is a growing technology that will ultimately be as common as an internet connection and a trading platform. But, as with most investments, timing is everything. Those who adopt this technology soon will discover an early-mover competitive advantage. For instance, investors using analysis platforms can be first to find a promising overseas stock offering, become more familiar with new markets, and learn the ins and outs of financial AI before other firms do.   

Find your next great investment with Bridgewise’s AI-driven insights. Contact us to learn more!