AI exposure: Financial Quantitative Analysts

Financial Quantitative Analysts sits in the top 2% of every occupation measured. 65.2% of what this job consists of, weighted by how important each task is to the role, is work current AI systems can produce with little standing in the way. Very few occupations score this high. The ones that do tend to share a trait: the output is a document, a calculation or a message, and nobody has to be in a particular room for it to count.

What holds the line here is context. Across this occupation's 21 tasks it averages 1.71 out of 3, the highest of the five friction dimensions. In plain terms, the work depends on knowledge the model cannot hold. Much of this job runs on things that were never written down: what this particular organisation does, what happened last week, what the person across the table actually meant. That context is the barrier, and it erodes as systems are given more access.

The most exposed thing this job does is Define or recommend model specifications or data collection methods, at 80.0%. The least is Analyze pricing or risks of carbon trading products, at 45.0%. A gap of 35.0% between two parts of the same job is the reason this index publishes at task level. An occupation-wide number would have hidden both.

Within business and financial operations occupations, this one is more exposed than most. The median across the 48 roles in the group is 47.1%, and only 1 of them score higher than this. Occupational families are not uniform, and the spread inside them is often wider than the gap between them.

What would move this score. Of 21 tasks, 21 are currently banded exposed, 0 assisted and 0 untouched. For that distribution to shift materially would take cheaper ways to verify output, since the cost of checking is currently doing more to hold this work in place than the cost of producing it. The score is re-computed every quarter against a fresh capability reference, and the change is published rather than quietly applied.