Measuring AI's impact on quantitative finance
Artificial intelligence has moved from experimentation to everyday use across quantitative finance. Practitioners now report substantial productivity gains, yet few firms can fully audit the calculations that AI generates.
As adoption expands, accuracy, transparency, and reproducibility remain essential. Firms are increasingly evaluating where AI can be applied responsibly and where human oversight remains necessary.
To understand how the market is responding, Numerix surveyed 95 professionals across banking, insurance, asset management, technology, consulting, and quantitative finance. This white paper breaks down what they said about productivity, trust, adoption, governance, analytics infrastructure, and talent. The results offer a directional view of sentiment across these groups and help quant, risk, and analytics leaders benchmark their own approach.
Read practical insights on:
- Where productivity gains are landing in quantitative finance, and what practitioners’ experiences reveal about uneven AI adoption
- What holds back trust, including the types of incorrect output that concern quant and risk professionals most and why verification remains a central challenge
- How adoption is spreading, from specialist quant teams to firm-wide deployment, and what that means for oversight
- Where auditability stands, including how consistently firms can reproduce and audit AI-generated calculations across different workflows
- How the analytics layer and quant hiring are changing, from the infrastructure beneath AI workflows to the rising weight placed on domain knowledge and market experience
See where AI is delivering value across quantitative workflows, and where gaps in trust, auditability, and analytics infrastructure remain. Download the white paper to learn how practitioners are deciding where AI currently belongs in pricing and risk work.
Frequently Asked Questions
1. What does the Numerix survey on AI in quantitative finance cover?
Numerix surveyed 95 professionals across banking, insurance, asset management, technology, consulting, and quantitative finance. The survey covered six areas. These were productivity, trust and reliability, adoption, governance and auditability, analytics infrastructure, and changing talent requirements. Participation was voluntary, so the number of responses varies by question. Percentages reflect only those who answered each one.
2. Are the productivity gains from AI consistent across firms?
Not entirely. Practitioners report substantial gains in their own work, but results depend on the task and some respondents are still evaluating AI's impact. Adoption remains uneven, with firms at different stages of putting these tools to work. The figures are self-reported, so the white paper presents them as a directional view rather than a precise measure.
3. Why is verification such a focus for firms using AI?
Accuracy, transparency, and reproducibility remain essential in quantitative environments. Practitioners are apprehensive about incorrect or misleading AI outputs, and auditability often varies by workflow. The white paper examines how firms are approaching validation, governance, and oversight as AI moves into more consequential areas of quantitative work.
4. Does AI replace existing quantitative systems?
In most cases, no. Many firms are integrating AI into their existing quantitative technology environments and keeping established analytical infrastructure in place. The survey points to AI working alongside in-house or third-party quantitative libraries rather than serving as the calculation engine. The white paper explores what that means for the analytics layer beneath AI workflows.
5. How are firms deciding where AI belongs in quantitative workflows?
Practitioners are now focused less on whether to use AI and more on where to use it and how to integrate it effectively. Productivity gains depend on the task, while reliability, maintainability, and verification remain critical considerations. The white paper looks at how firms are determining where AI adds value and where human judgment remains essential.
6. How is AI changing the skills quant teams need?
For most firms the shift has not yet reached hiring decisions, but early signs point to the rising value of domain knowledge and market experience. AI may raise the value of quants who can judge whether an output is accurate. The white paper explores what that could mean for the skills quant teams look for.
7. How should the survey results be interpreted?
The findings are directional. They reflect self-reported responses and are not statistically representative of the wider financial services industry. Percentages are calculated from the number of respondents who answered each question. Readers should treat the results as a view of current sentiment and practice across these groups rather than a measure of the whole industry.
8. Who should read this white paper?
The paper speaks to practitioners across quantitative finance, including those in risk, analytics, and model development. It suits anyone weighing where AI belongs in pricing and risk workflows and how its outputs should be tested and controlled. The findings give readers a directional benchmark for how peers are using AI today.
