UBS now requires AI proficiency for all graduates and interns in its Global Banking and Markets division by 2027.

UBS now mandates AI expertise for all new hires in its Global Banking and Markets division, demanding practical skills over theoretical knowledge during assessments.

This shift follows industry shifts, where companies like Santander are already seeking professionals labeled as "advanced AI users." Meanwhile, Morgan Stanley forecasts that over 200,000 banking roles in Europe could vanish within five years, reshaping demand for roles requiring automation, like those handling Excel-based analyses with large language model agents.

To meet these demands, candidates must showcase experience beyond basic tool use. For instance, candidates must deploy a solution—such as automating financial report parsing or constructing workflows that integrate multiple AI tasks—using tools like Python’s PyPDF2 or langchain to build a retrieval-augmented generation (RAG) pipeline.

Here’s how to demonstrate readiness:

1. Automate data extraction from documents like 10-K filings using scripts that summarize reports in seconds. Tools like PyPDF2 or langchain enable building pipelines that transform raw text into actionable insights.

1. Engineer prompts for deeper analysis by structuring queries to demand multi-step reasoning. For example, a prompt like "Analyze Company X’s quarterly revenue growth, identify its three primary drivers, and cross-reference them with macroeconomic trends from the attached PDF" tests ability to derive nuanced insights.

1. Design small-scale agentic workflows, combining agents like a "Researcher" (gathering data) and a "Critic" (evaluating assumptions) to simulate real-world decision-making loops. Frameworks such as CrewAI or AutoGen facilitate this modular approach.

The shift reflects a broader industry push toward roles that integrate AI-driven automation, where candidates must prove they can adapt existing workflows—or build new ones—to meet evolving financial demands.