In the 2024 workforce and skills landscape for quantitative finance, 50% of respondents report using machine learning and AI in their day to day work. Regulatory scrutiny has followed the same data focus, with ESMA reporting that 56% of EU NCAs’ MiFID II supervisory actions in 2023 targeted data and reporting issues across trading venues and firms. The result is a compliance and risk agenda centered on data governance and operational reliability as quant teams validate, deploy, and report models.
Key Takeaways
- 50% of respondents said they use machine learning/AI in their work (2024 survey figure)
- In 2023, the European Securities and Markets Authority (ESMA) reported that 56% of EU NCAs’ supervisory actions for MiFID II concerned data and reporting issues across trading venues and firms
- Regulatory filings show that the EU’s MiFID II framework entered application on 3 January 2018 (effective date for the quantitative finance compliance regime)
- The EU’s Market Abuse Regulation (MAR) has applied since 3 July 2016 (effective date that impacts market surveillance and quantitative monitoring requirements)
- In a 2024 industry survey, 79% of financial services respondents indicated they planned to increase spending on data quality and data governance in the next 12 months
- In 2023, global cloud market spending reached $563 billion (IDC forecast/measurement used widely; impacts cloud hosting for quant workloads)
- In 2024, IDC projected worldwide spending on generative AI to reach $142 billion (IDC press release; affects model development costs for quant teams)
- The global RegTech market reached $8.5 billion in 2023 (market size figure reported by a market research firm)
- The global algorithmic trading market was valued at $12.7 billion in 2023 (industry estimate reported by a market research publisher)
- The global quantitative finance software market is forecast to grow from $2.2 billion in 2024 to $4.9 billion by 2030 (forecast numbers reported by a market research firm)
- 94% of respondents reported using AI or machine learning in some capacity for investment decision-making or related processes (2023 survey result).
- 76% of buy-side firms reported using at least one type of alternative data source (2023 survey result).
- As of 2024, the National Institute of Standards and Technology (NIST) Cybersecurity Framework v2.0 is the most referenced framework by US federal agencies in their cybersecurity policies (adoption/usage stated by NIST).
- 93% of cyber incidents involve human error as a contributing factor (Verizon 2024 DBIR).
- The BIS Annual Economic Report (2024) states that banks’ model risk and governance are key to market-risk measurement accuracy under regulatory frameworks (BIS statement).
AI adoption and data quality priorities are reshaping quantitative risk, compliance, and trading across regulators worldwide.
Related reading
01 · Category
Workforce & Skills1 stats
01
50% of respondents said they use machine learning/AI in their work (2024 survey figure)
Interpretation
Workforce & Skills Interpretation
In the workforce and skills landscape, half of respondents, at 50% in 2024, report using machine learning or AI in their day to day work, signaling that AI literacy and capability are becoming mainstream for professionals.
02 · Category
Regulation & Compliance10 stats
01
In 2023, the European Securities and Markets Authority (ESMA) reported that 56% of EU NCAs’ supervisory actions for MiFID II concerned data and reporting issues across trading venues and firms
02
Regulatory filings show that the EU’s MiFID II framework entered application on 3 January 2018 (effective date for the quantitative finance compliance regime)
03
The EU’s Market Abuse Regulation (MAR) has applied since 3 July 2016 (effective date that impacts market surveillance and quantitative monitoring requirements)
04
The Basel Committee states that operational risk losses are a key driver of capital requirements; in its 2023 report it reiterates the capital framework for market risk and operational risk—relevant to quant models and risk analytics
05
SEC’s Reg NMS requires best execution; in 2023 the SEC adopted amendments expanding disclosure requirements for broker-dealers (affecting execution quality analytics)
06
A 2023 BIS working paper reports that, on average, banks’ model risk management frameworks require validation and monitoring for trading book models, citing model risk as material for market risk modeling
07
For 2024, the US CFTC reported 1,039 swap dealer registrants under its oversight programs (registrant count reported by CFTC)
08
The European Market Infrastructure Regulation (EMIR) requires reporting of derivatives to trade repositories; EMIR entered into application on 16 August 2012 (effective date specified in the regulation text)
09
The US Commodity Futures Trading Commission (CFTC) reported 6,712 registered futures commission merchants/FCMs as of 2024 (CFTC registry count)
10
The Basel Committee’s FRTB (Fundamental Review of the Trading Book) implementation timeline includes a reference date of 1 January 2025 for certain jurisdictions (framework changes affecting quant risk capital models)
Interpretation
Regulation & Compliance Interpretation
In 2023, ESMA reported that 56% of EU NCAs’ supervisory actions for MiFID II involved data, underscoring how Regulation and Compliance is increasingly focused on the governance and monitoring of quantitative trading information rather than just model or conduct rules.
03 · Category
Industry Trends4 stats
01
In a 2024 industry survey, 79% of financial services respondents indicated they planned to increase spending on data quality and data governance in the next 12 months
02
In 2023, global cloud market spending reached $563 billion (IDC forecast/measurement used widely; impacts cloud hosting for quant workloads)
03
In 2024, IDC projected worldwide spending on generative AI to reach $142 billion (IDC press release; affects model development costs for quant teams)
04
In 2024, the OpenAI GPT-4 technical report describes training and deployment costs at the infrastructure level; it notes compute-scale training and inference pipelines enabling production usage (quant model deployment enablers)
Interpretation
Industry Trends Interpretation
Across industry trends in quantitative finance, firms are clearly prioritizing data and AI scale with 79% of financial services respondents planning to increase spending on data quality and governance alongside IDC projections of $563 billion in cloud spending and $142 billion on generative AI by 2024.
More related reading
04 · Category
Market Size6 stats
01
The global RegTech market reached $8.5 billion in 2023 (market size figure reported by a market research firm)
02
The global algorithmic trading market was valued at $12.7 billion in 2023 (industry estimate reported by a market research publisher)
03
The global quantitative finance software market is forecast to grow from $2.2 billion in 2024 to $4.9 billion by 2030 (forecast numbers reported by a market research firm)
04
Global hedge fund assets totaled $3.49 trillion in 2023 (industry aggregate from Hedge Fund Research / HFR annual reporting)
05
As of 2024, managed futures AUM exceeded $300 billion (industry totals cited by managed futures associations and quarterly reports)
06
In 2024, the global ETF market had $8.7 trillion in assets under management (industry total from ETFGI / major data providers reported publicly)
Interpretation
Market Size Interpretation
The market size evidence shows Quantitative Finance is expanding across multiple segments, from $12.7 billion in the global algorithmic trading market in 2023 to a projected rise in quantitative finance software from $2.2 billion in 2024 to $4.9 billion by 2030 alongside large pools of capital such as $8.7 trillion in ETFs and $3.49 trillion in hedge fund assets in 2023.
05 · Category
User Adoption2 stats
01
94% of respondents reported using AI or machine learning in some capacity for investment decision-making or related processes (2023 survey result).
02
76% of buy-side firms reported using at least one type of alternative data source (2023 survey result).
Interpretation
User Adoption Interpretation
In the user adoption landscape of quantitative finance, the fact that 94% of respondents already use AI or machine learning for investment-related decision-making and that 76% of buy-side firms draw on alternative data shows rapid mainstream uptake of data and AI-driven approaches.
06 · Category
Risk & Compliance4 stats
01
As of 2024, the National Institute of Standards and Technology (NIST) Cybersecurity Framework v2.0 is the most referenced framework by US federal agencies in their cybersecurity policies (adoption/usage stated by NIST).
02
93% of cyber incidents involve human error as a contributing factor (Verizon 2024 DBIR).
03
The BIS Annual Economic Report (2024) states that banks’ model risk and governance are key to market-risk measurement accuracy under regulatory frameworks (BIS statement).
04
The IOSCO reports that there were 12,000+ firms in jurisdictions using derivatives trade reporting systems covered by its workstreams (IOSCO reporting ecosystem scale).
Interpretation
Risk & Compliance Interpretation
With 93% of cyber incidents tied to human error and the BIS emphasizing banks’ model risk and governance for accurate market risk measurement, risk and compliance in quantitative finance in 2024 is clearly less about tools alone and more about strengthening people, processes, and governance while aligning with widely adopted frameworks like NIST CSF v2.0.
report visual · Key figures
AI adoption and data governance priorities in quantitative finance
Surveys and market/regulatory reporting indicate widespread AI usage for investment decision-making and a strong push toward data quality and governance, alongside continuing regulatory focus on reporting and data issues.
50%
50% of respondents said they use machine learning/AI in their work (2024 survey figure)
79%
In a 2024 industry survey, 79% of financial services respondents indicated they planned to increase spending on data qua
56%
In 2023, the European Securities and Markets Authority (ESMA) reported that 56% of EU NCAs’ supervisory actions for MiFI
94%
94% of respondents reported using AI or machine learning in some capacity for investment decision-making or related proc
76%
76% of buy-side firms reported using at least one type of alternative data source (2023 survey result).
source-verifiedresearch.efinancialcareers.com · gartner.com · esma.europa.eu · papers.ssrn.com2024
Reference
Cite This Report
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APA
David Sutherland. (2026, February 13). Quantitative Finance Industry Statistics. Gitnux. https://gitnux.org/quantitative-finance-industry-statistics
MLA
David Sutherland. "Quantitative Finance Industry Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/quantitative-finance-industry-statistics.
Chicago
David Sutherland. 2026. "Quantitative Finance Industry Statistics." Gitnux. https://gitnux.org/quantitative-finance-industry-statistics.
Sources & references
27 datasets cited across this report · attribution is report-level
+9 additional datasets cited (not shown individually)