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The generative AI boom has continued to gather momentum at an impressive rate over the past year and now appears set to become a revolutionary force in the world of institutional investing. 

By 2032, the generative AI market is set to total $1.3 trillion in revenue, representing a CAGR (compound annual growth rate) of 43%. While this growth will be felt across a wide range of industries, its ability to deliver unprecedented levels of automation in trade execution and algorithmic trading could prove transformative for institutional investors. 

Stemming from the technology’s ability to interpret and analyze big data, identify trends, simulate complex scenarios, and generate trade signals, the adaptive learning capabilities of generative AI can help to leverage more complex trading strategies in line with ever-changing market dynamics. 

While this brave new frontier for institutions isn’t entirely risk-free, and the technology itself is still very much in its infancy, it appears likely that generative AI will carry a transformative impact on the investment landscape for players of all scales and ambitions. 

Facilitating growth opportunities in trading

The transformative impact of generative AI in trading can provide plenty of growth opportunities in the world of trading, particularly when it comes to analyzing and executing trades. 

Crucially, generative AI can make a significant impact in the world of algorithmic trading. The technology’s ability to identify patterns and execute trades at a rapid pace has the potential to shape algo trading strategies to take opportunities as and when they emerge with fewer associated risks. 

Algorithmic trading is a complex tool for large institutions and professional traders, and nuances like the volume-weighted average price (VWAP), time-weighted average price (TWAP), and iceberg execution strategies offered by prime brokerage services have the potential to be further optimized by generative AI and its management of large-scale orders. 

Generative AI can also optimize quantitative analysis to interpret high volumes of financial data to spot emerging trends, correlations, or anomalies to convert into actionable insights. These can also help to optimize institutional portfolios without the risk of human error or bias. 

The technology can also facilitate growth through more attentive risk management strategies that assess market volatility, credit risk, and other factors that can effectively help mitigate risk and protect client capital. 

Acting on big data

One of the early use cases for generative AI in the institutional investment landscape will be in the form of augmented data that helps to drive the decision-making process for high-touch traders. 

The large language models (LLMs) that set generative AI apart from other intelligent trading tools offer the ability to gather, condense, and present essential information clearly and briefly. 

At a time when huge volumes of data are being rolled into decision-making for institutions, summarizing information to facilitate positive decisions can be vital in identifying and acting on opportunities that may only exist for a very short time. 

As the technology grows, generative AI will become capable of unlocking new insights and improving institutional investor productivity. Through GenAI data management platforms, it will be possible to capture, curate, validate, and dispatch data sets to AI and machine learning modeling engines. 

Every competent institutional investor understands the value of data. It forms a critical means of differentiation and offers a competitive advantage over rivals battling to act on fine margins. Leveraging data to make well-informed and timely investment decisions will form the cornerstone of the next generation of generative AI trading. 

Transforming compliance

Another way that generative AI can help to improve the performance of institutional investors is in compliance. 

According to Phil Moyer, Global VP of AI & Business Solutions at Google Cloud, “Generative AI can create a more ambient compliance environment, providing a better and faster way to monitor advisors’ and traders’ client communications to ensure they’re not doing or saying things they shouldn’t be.”

“It can also generate sales and marketing content that’s compliant. As well as create and change assets and technology controls to evaluate missing encryption controls, help automate and document code updates when regulations change, and monitor and manage controls easier and at scale,” Moyer added.

This level of automated compliance can be expanded throughout the field of financial services by processing significant volumes of content and creating insights, answers, and datasets that adhere to geographic regulatory expectations or institutional values. 

Applications in FX markets

The world of forex is highly nuanced and dependent on high-quality insights and trading signals delivered by reliable, scalable, and fast-acting algorithmic trading platforms. 

Where generative AI can excel beyond existing algorithmic trading tools and the automation technology prevalent today can be found in synthetic data, and Andrew Bradshaw, Global Head of Prime – Hedge Funds at 26 Degrees Global Markets, believes that the ability of AI to process and analyze large datasets rapidly can further optimize these insights.

This extends to machine learning algorithms that continuously learn from new data and optimize trading strategies for changing market conditions, all while minimizing human bias in the investment process,” Bradshaw added. “These advancements have led to a more dynamic approach to navigating financial markets.”

Based on the characteristics of real-world transactional and market data, generative AI programs can synthesize datasets that are closely related to existing data but can’t be mapped out using available information. 

In particular, this can be applied to creating accurate quotes for currencies where pricing isn’t readily available. For instance, emerging and frontier currencies can offer great opportunities for institutional forex trading but aren’t as liquid as freely traded currencies. 

Generative AI can also help to identify outlier scenarios through more robust risk management strategies. 

Contending with new risks

At this stage, it’s important to highlight that risks are prevalent throughout the generative AI landscape, and could negatively impact its performance in the institutional investing landscape. 

Working with significant volumes of sensitive financial data will come with the necessity of robust security measures and greater adherence to data privacy regulations. 

Algorithmic bias can also enter the fray which may result in unfair practices or unwitting discrimination that’s amplified within datasets and insights. 

As a result, striking the right blend between GenAI automation and human oversight will be key for decision-making for institutions. This will be intensified while ethical considerations and regulatory compliance are even more essential. 

Balancing the great potential of generative AI’s data-driven insights and sustainably utilizing the technology will be key as the boom begins to influence institutional performance. As the technology develops, it will become increasingly clear that GenAI will have a significant impact throughout the world of finance and will grow into an essential component in any institution’s toolkit. 

All views and opinions expressed in this article are the opinions of the author and not FXStreet. Trading cryptocurrencies or related products involves risk. This is not an endorsement to invest in or trade any of the cryptocurrencies, stocks or companies mentioned in this article.

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