AI Revolutionizes Financial Markets: Key Shifts and Strategic Dilemmas

Financial institutions have long relied on the core pillars of mathematics and physics, which is why it is no surprise that so many mathematicians and physicists have traditionally gravitated toward this sector. Brownian-motion-based pricing models, diffusion processes for risk analysis, and statistical and quantitative methods are only a few of the foundational tools.

Today, however, an additional and rapidly accelerating trend is reshaping the talent landscape: the rise of artificial intelligence. With the scale and adaptability of generative models, particularly large language models, firms can now automate and enhance conventional analytical tasks, streamline previously cumbersome processes, and operate far more efficiently. But how exactly is the market benefiting from this shift?

Banks, the traditional cornerstones of the financial system, are navigating this transition with a mix of ambition and uncertainty. Historically, they have relied heavily on institutional experience, relationship insights, and established modelling practices rather than deploying cutting-edge technology. In contrast, a new wave of startups, often founded by former bank employees who understand specific pain points, are leveraging AI to build agile, niche solutions. These firms can achieve overnight acceleration and compete directly with long-standing internal processes.

Take credit analysis as an example. What was once a labor-intensive review of reports, filings, and financial statements is being transformed. LLMs can now extract key insights, highlight trends, and surface relevant facts with remarkable efficiency, enabling analysts to focus on judgment and decision-making rather than manual document review. The central strategic dilemma for banks is therefore clear: how deeply should they integrate such AI-driven solutions, and where should they draw the line between in-house development and selecting best-in-class external tools?

Beyond core banking workflows, advances in AI are enabling new forms of customer engagement and infrastructure. Traditionally, corporate clients interacted with banks primarily through relationship managers and research reports, while retail clients relied on mobile and web apps for basic account information. AI now allows even smaller players to deliver personalized insights, proactive analysis, and tailored recommendations (often underpinned by banking licenses for advisory services). This creates a more dynamic ecosystem in which customers receive richer, more contextual support.

At SEB, we are well positioned compared to many peers in the region. Through SEBx, we are actively working across these emerging domains to empower business units and accelerate the bank’s ability to adopt and leverage AI effectively. We have exciting years ahead, with rapid technological shifts in AI, quantum computing, blockchain, and stablecoins reshaping the financial landscape. Our focus is to stay ahead of these developments, experiment responsibly, and translate emerging technologies into concrete value for our customers, partners, and internal teams. By combining deep domain expertise with modern technological capabilities, we aim to strengthen our role as an innovative and forward-looking financial institution.

Sina Molavipour

AI researcher

SEBx