Technological disruption and AI: shaping the global economy

April 2025 by André Themudo

The financial industry has undergone a huge transformation, with artificial intelligence (AI) at the forefront of this revolution. AI is improving the ability of investors and investment managers to process and analyze large amounts of data, enabling more informed and accurate decision-making.

Transforming investment

By taking advantage of AI, systematic investment companies, such as BlackRock Systematic, have significantly changed their investment approaches. Over the past two decades, BlackRock has integrated AI and machine learning techniques to move from traditional qualitative analysis to a more quantitative, data-driven approach. This shift is unlocking new opportunities to generate alpha - the excess return over the market benchmark - by discovering patterns and insights that were previously hidden in complex data sets. It also improves the accuracy of qualitative analysis by refining predictive models, for example through sentiment analysis and text extraction, previously limited by manual interpretation.

The Role of Large-Scale Language Models

One of the main advances in AI that has transformed the investment world is the Large Scale Language Model (LLM). LLMs are used to analyze and extract valuable information from analyst reports, earnings conference transcripts, news articles, and social media. Instead of traditional sentiment analysis that counts positive and negative words, modern LLMs process text holistically, accounting for the relationships between words to uncover more nuanced investment insights.

A practical example of this is BlackRock’s AI model, which was trained on more than 400,000 earnings call transcripts and two decades of historical market data. This allows the model to predict market movements more accurately than general AI models, such as those used by public platforms like ChatGPT. The ability to combine information from various data sources and cross-reference it with market trends gives BlackRock's models a distinct advantage when making real-time predictions, which can lead to more effective investment strategies.

Types of Machine Learning

BlackRock Systematic uses several machine learning techniques in its investment strategies. Decision trees divide investment data into structured decision paths, helping to identify patterns. Deep neural networks analyze large data sets to discover relationships, helping to predict market movements. Natural language processing (NLP) and large-scale language models (LLM) are applied to text analysis of analyst reports, corporate earnings calls, news and social media to uncover sentiment and extract actionable investment insights. Predictive models combine fundamental, sentiment, macroeconomic and ESG signals to predict stock returns.

Although BlackRock Systematic's strategies are rooted in quantitative models that rely heavily on machine learning and AI, the investment industry in general also integrates these technologies to improve active and passive investment strategies. In active management, machine learning helps fund managers process large amounts of data – financial statements, economic reports and alternative data – enabling better decision-making. Additionally, AI-based models improve risk management by enabling dynamic portfolio adjustments based on changing market conditions.

Machine learning also plays a key role in algorithmic trading , analyzing market data for high-frequency trading. Robot-advisors use machine learning to create personalized investment strategies tailored to each individual's objectives, risk tolerance and financial situation, continuously adapting to changing conditions.

The combination of machine learning, big data and human expertise allows BlackRock Systematic to create systematic portfolios, optimize investment decisions and balance risk and return. However, technological advances also bring challenges, such as staying ahead of competitors and ensuring the integrity of AI models. We see broader application of AI in the retirement sector through customer service, participant engagement, and plan design and administration. Regardless of a plan fiduciary’s focus, one thing remains clear: ensure that asset managers, recordkeepers and consultants are equipped to help you deliver more value to their participants by integrating AI into their plan.