AI is reshaping how equity investing is researched, executed, and supervised as predictive models, automated document workflows, and alternative data become more common across asset managers and trading teams. Progress in accuracy and speed sits alongside rising governance expectations, including model controls before deployment and structured guidance like NIST’s AI RMF. Regulatory pressure also shapes adoption, from the EU AI Act’s high-risk requirements to MiFID II’s detailed trading-data reporting.
Key Takeaways
- 73% of asset managers reported using alternative data for investment decision-making
- $1.2 billion was invested in AI-focused fintech and financial-services startups in 2023
- $3.8 billion global AI in finance market size in 2023
- $17.3 billion projected global artificial intelligence in banking market size by 2030
- 73% of asset managers reported that AI improves investment decision-making accuracy
- 35% higher prediction accuracy was reported for AI models in a typical trading signal benchmark study
- 1.3 percentage-point reduction in forecast error (MAE) for volatility prediction models compared with baseline models in a peer-reviewed study
- AI accounted for 7% of total IT spend in financial services in 2023
- Financial institutions reported that 67% of AI-related projects require model governance controls before deployment
- NIST’s AI RMF provides guidance across 4 functions: Govern, Map, Measure, Manage
- 35% of banks planned to adopt ‘AI governance’ programs in 2024
- 47% of organizations were using or planning to use LLMs for customer service automation in financial services (survey year 2024)
- 66% of buy-side firms reported using electronic trading venues as their primary execution method
AI is reshaping equity investing as alternative data adoption rises and governance becomes essential.
Related reading
01 · Category
User Adoption1 stats
01
73% of asset managers reported using alternative data for investment decision-making[1]
Interpretation
User Adoption Interpretation
For the user adoption category, the fact that 73% of asset managers already use alternative data for investment decision-making shows that AI-enabled data tools are moving from experimentation to widespread real-world use.
02 · Category
Market Size10 stats
01
$1.2 billion was invested in AI-focused fintech and financial-services startups in 2023[2]
02
$3.8 billion global AI in finance market size in 2023[3]
03
$17.3 billion projected global artificial intelligence in banking market size by 2030[4]
04
$12.1 billion global AI in capital markets market size in 2024[5]
05
$9.2 billion global natural language processing (NLP) in financial services market size in 2024[4]
06
$2.9 billion global robo-advisory market size in 2024[3]
07
$1.6 billion global algorithmic trading systems market size in 2023[6]
08
$4.4 billion global regtech market size in 2023[7]
09
$5.1 billion global AI in cybersecurity market size in financial services in 2024[8]
10
$8.6 billion global big data and analytics in financial services market size in 2024[5]
Interpretation
Market Size Interpretation
The market size signals strong and expanding momentum, with AI in finance reaching $3.8 billion in 2023 and projected to grow to $17.3 billion for banking by 2030, alongside sizable 2024 capital markets AI demand of $12.1 billion, all underscoring that AI is becoming a major and rapidly scaling investment and revenue category for the equity industry.
03 · Category
Performance Metrics6 stats
01
73% of asset managers reported that AI improves investment decision-making accuracy[9]
02
35% higher prediction accuracy was reported for AI models in a typical trading signal benchmark study[10]
03
1.3 percentage-point reduction in forecast error (MAE) for volatility prediction models compared with baseline models in a peer-reviewed study[11]
04
4.2x faster document review (time reduction) when using AI-based machine vision and NLP for prospectus review in a legal ops workflow study[12]
05
0.6 bps lower bid-ask spread attributed to ML-based execution optimization in a quant execution study[12]
06
15% reduction in false positives for credit-risk flagging using gradient-boosted models vs logistic regression in a peer-reviewed evaluation[11]
Interpretation
Performance Metrics Interpretation
Across performance metrics, studies and surveys show AI delivering measurable market and model improvements, including a 73% reported gain in investment decision accuracy, a 0.6 bps tighter bid ask spread, and up to a 35% jump in prediction accuracy.
More related reading
04 · Category
Risk And Governance7 stats
01
AI accounted for 7% of total IT spend in financial services in 2023[13]
02
Financial institutions reported that 67% of AI-related projects require model governance controls before deployment[14]
03
NIST’s AI RMF provides guidance across 4 functions: Govern, Map, Measure, Manage[15]
04
EU AI Act classifies ‘high-risk’ AI systems as subject to strict requirements, affecting many financial services AI use cases[16]
05
The SEC reported 1,294 cybersecurity-related enforcement actions from 2013–2024, underscoring technology and controls scrutiny[17]
06
The Basel Committee requires banks to ensure model risk management processes are appropriate and documented for internally developed models[18]
07
GDPR allows administrative fines up to €20 million or 4% of annual global turnover for certain breaches, including those involving AI personal data processing[16]
Interpretation
Risk And Governance Interpretation
For the Risk and Governance category, the clearest trend is that AI investment is accelerating, yet 67% of AI projects require model governance controls before deployment, meaning regulators and frameworks like NIST’s Govern, Map, Measure, Manage and the EU AI Act’s high risk requirements are increasingly driving enforceable oversight.
05 · Category
Industry Trends5 stats
01
35% of banks planned to adopt ‘AI governance’ programs in 2024[19]
02
47% of organizations were using or planning to use LLMs for customer service automation in financial services (survey year 2024)[13]
03
66% of buy-side firms reported using electronic trading venues as their primary execution method[18]
04
EU MiFID II requires reporting of detailed trading data, enabling greater monitoring of algorithmic trading[16]
05
China’s CSRC algorithmic trading rules (amended 2023) require enhanced controls for AI-assisted trading systems[20]
Interpretation
Industry Trends Interpretation
In the equity industry’s industry trends, banks are moving toward AI governance with 35% planning adoption in 2024, while the 47% of financial services organizations using or planning LLMs for customer service and new regulatory pressure from EU MiFID II and China’s CSRC rules are collectively pushing AI deeper into both front office automation and trading oversight.
Reference
Cite This Report
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APA
David Sutherland. (2026, February 13). AI In The Equity Industry Statistics. Gitnux. https://gitnux.org/ai-in-the-equity-industry-statistics
MLA
David Sutherland. "AI In The Equity Industry Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/ai-in-the-equity-industry-statistics.
Chicago
David Sutherland. 2026. "AI In The Equity Industry Statistics." Gitnux. https://gitnux.org/ai-in-the-equity-industry-statistics.
Sources & references
20 sources cited across this report