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Since then, AI development has accelerated dramatically. Well-known models, from ChatGPT to Gemini, have evolved rapidly, and in January 2025, the launch of DeepSeek, a Chinese AI model, sent shockwaves through global stock markets. What makes DeepSeek particularly remarkable is its efficiency – it requires far fewer resources than other AI systems. Additionally, its underlying algorithms were released as open-source code, offering new opportunities for AI developers to create their own models.

There is no doubt that these advancements are propelling AI adoption within the financial sector. As AI continues to evolve, more companies will be able to develop tailored AI models, unlocking new possibilities to benefit their customers.

The evolution of AI in the financial industry

Around the turn of the millennium, automated or algorithmic trading began transforming securities markets. No longer were brokers or traders solely responsible for buy and sell decisions – specialised computer programmes, powered by increasingly sophisticated algorithms, took their place. The popularity of this digital approach surged, with algorithmic trading transactions quadrupling between 2002 and 2004.

Rather than relying on human judgement to assess securities based on available information, these algorithms calculated probabilities using a wide range of data points. Automated trading systems could analyse market conditions in milliseconds and, in many cases, execute trades without human intervention. However, as this speed and autonomy occasionally led to market distortions, many countries introduced legal regulations to govern the use of algorithmic trading.

These early algorithms operated by analysing both static databases and real-time data. Yet their effectiveness was limited by the constraints of traditional relational database systems. The emergence of big data technologies helped overcome these barriers, enabling the use of new data storage and analysis architectures capable of processing information in parallel across hundreds or even thousands of processors or servers.

Over the past twelve years, cognitive systems have further advanced big data applications in finance. These systems not only extract relevant insights from vast datasets but also make decisions based on predefined triggers. When a cognitive system identifies certain complex conditions within the analysed data, it can autonomously initiate actions – for example, issuing a command to reject specific securities.

AI’s integration into the financial industry

Artificial intelligence has firmly established itself in the financial industry. Early AI models for performance analysis and risk assessment – applied not only to publicly traded securities but also to a wide range of financial instruments – were developed for and implemented by major players in the global financial market.

Today, AI is driving innovation across a variety of financial services. Notable examples include:

Marcus by Goldman Sachs

Marcus uses AI algorithms to evaluate loan applications and deliver personalised financial advice. Additionally, AI-powered chatbots handle routine customer service enquiries, improving response times and efficiency.

Aladdin (Asset, Liability, Debt and Derivative Investment Network) by BlackRock

Aladdin is a comprehensive AI-powered platform that analyses, monitors and manages portfolio risks. It delivers real-time insights into risk exposures, helping asset managers make informed investment decisions.

Betterment Robo-Advisor

Betterment leverages AI to create tailored investment strategies based on each client’s financial goals and risk tolerance. The platform builds and manages personalised portfolios, making sophisticated wealth management accessible to a broader audience.

Other major institutions – including J.P. Morgan, Citibank, IBM (with Watson) and Deutsche Bank – are also deploying proprietary AI solutions.

Example: LIXX collaborates with FinScience

A compelling example of AI’s growing role in finance is the collaboration between LIXX, the index provider of Chartered Investment, and FinScience. In 2024, they jointly launched five innovative indices that showcase how artificial intelligence can accelerate trend detection and enhance stock selection. FinScience operates a powerful big data platform that analyses content from approximately 1.5 million websites across 35,000 domains daily. AI algorithms continuously evaluate this vast data pool to identify emerging trends. LIXX contributes its financial expertise by translating these AI-driven insights into precise index methodologies and professionally calculated indices. These indices form the foundation for investment products, such as index trackers, offered via Chartered Investment’s platforms – providing investors with direct access to AI-based strategies.

What sets FinScience’s web analytics apart is its ability to capture an exceptionally broad spectrum of topics. It collects data from a wide range of sources, including corporate websites, news outlets, blogs and social media. This broad sampling allows its AI models to uncover market-relevant signals and trends that often elude even seasoned financial analysts.

To make this technology more accessible, FinScience launched Edge, a user-friendly platform that empowers clients to explore and analyse specific topics of interest. Currently offering insights into more than 70 themes, ranging from technology and finance to sustainability and digital lifestyle, Edge provides key metrics for each area, along with a list of relevant companies and associated financial data. Users can build personalised portfolios and tailor investment strategies directly within the platform.

The depth and frequency of analysis enabled by FinScience would be impossible without AI. Its AI models are continuously refined with historical data to increase their predictive power, demonstrating the transformative potential of artificial intelligence in the investment industry.
 

AI is shaping the future of the financial industry

Artificial intelligence is poised to transform nearly every facet of the financial industry – from risk analysis and valuation to product development, index design and trading. The future of finance lies in the seamless integration of human expertise and AI-driven technology. Together, they will unlock new levels of precision, efficiency and innovation across the sector.