State of AI in Financial - Nvidia - 2025 Trends

State of AI in Financial
Services: 2025 Trends
Survey Report

Table of Contents
Key AI Trends for Financial Services in 2025 3
Strategic Infrastructure and Investments
Industry Insights and Future Outlook
AI Use Matures to Deployment of Strategic Use Cases 4
AI and Machine Learning Workloads
How AI is Improving Business Operations
Generative AI Is Driving Industry Transformation 6
Top Generative AI Use Cases
How AI Is Driving Business Results for Financial Services
Investment in AI
AI Challenges on the Decline 10
Energy-Efficient Computing
Looking Forward: AI Opportunities and Expectations 12

State of AI in Financial Services: 2025 Trends | Survey Report | 3
Key AI Trends for Financial Services in 2025
AI is bringing significant new capabilities to financial services. To better
understand how the industry is leveraging AI to transform, NVIDIA has completed
our fifth annual State of AI in Financial Services report. This report is based on
a survey of approximately 600 global financial services professionals about the
trends, challenges, and opportunities for accelerated computing, AI, and machine
learning in the industry.
The latest survey results show generative AI standing out as a pivotal
technology. Building on the momentum from last year’s survey, over half of
the respondents currently use generative AI, up from 40 percent last year.
Generative AI can quickly generate new content—including text, images, 3D
models, video, and more—based on a variety of inputs. This technology is not
only enhancing existing applications but is also driving the creation of innovative
services and solutions across the financial services sector.
Strategic Infrastructure and Investments
To set themselves apart from the competition, banks and financial services
companies are continuing to invest heavily in AI to improve underwriting,
enhance customer experiences, reduce risk, and maximize portfolio returns.
Survey findings reveal that a significant portion of AI investments will go
toward computing infrastructure. These infrastructure investments enable
companies to build AI factories—specially built accelerated computing
platforms for processing, refining, and transforming vast amounts of data into
valuable AI models and tokens. Financial services companies will use these
capabilities to bring greater efficiency to operations and enhance services to
build a competitive edge.
Key findings from this year’s survey underscore the significant
impact of AI:
>Investment in AI infrastructure is seeing a robust increase, with 98 percent
of management saying they will further increase spending in 2025.
>Over half of companies now view AI as crucial to their future success,
indicating growing reliance on AI as a strategic business lever.
>AI challenges are receding, with 50 percent fewer respondents reporting a
lack of AI budget and significantly fewer companies reporting AI data issues.
Leading online payment platform
PayPal turned to accelerated
computing for more efficient data
processing and analytics workloads,
updating its AI infrastructure to
achieve a reduction of up to 70% in
cloud costs and 35% in runtime.
40%
52%


20232024
respondents currently
using generative AI
60
50
40
30
20
10
0

State of AI in Financial Services: 2025 Trends | Survey Report | 4
Industry Insights and Future Outlook
The 2025 outlook reveals a broadening of AI’s role in trading, banking, payments,
and fintech, from improving operational efficiencies to enabling new business
models and revenue streams. Financial institutions are not only focusing on
enhancing customer experiences and cybersecurity but are also increasingly
committed to sustainable practices, as evidenced by substantial growth in AI-driven
environmental, social, and governance (ESG) and sustainable finance initiatives.
Analysis of this year’s survey results highlight four important findings:
>Maturation of AI use cases: While firms continue to expand the number of
generative AI use cases under evaluation, the penetration rates for a few key use
cases indicates maturation in exploration and use of AI.
>Predominance of data analytics and generative AI: Data analytics remains the
top workload, with generative AI showing significant growth, becoming the
second-most utilized workload.
>Transformational impact of AI: A significant portion of respondents view AI
and generative AI as transformational, with increased integration across various
business functions.
>Diversification of AI benefits: More than one-third of respondents cited
operational efficiencies as a top benefit of AI, while competitive advantage and
new business opportunities were also recognized as key benefits.
As the industry embraces AI, it must also navigate energy-efficient computing
and the development of trustworthy AI to unlock its potential in an ethical and
sustainable manner.
AI Use Matures to Deployment of Strategic Use Cases
Over the past 12 months, financial services companies have been consolidating
their AI efforts around several core applications. This indicates a shift from AI
exploration to a focus on successful deployment of high-value, strategic use cases.
Data analytics remains the predominant AI workload, with companies leveraging this
technology to harness vast amounts of data to better detect fraud, personalize services,
predict market trends, and manage investment risk. With the most substantial year-
over-year (YoY) growth, generative AI emerged as the second-most popular workload,
with over 50 percent of respondents using or assessing the technology.
What AI and machine learning workloads is your company
using or assessing (select all that apply)?
56%57%
40%
52%
41%
39%
34%
32%
47%


Data analytics Generative AI Predictive analytics Large language
models (LLMs)
Conversational AI
60
50
40
30
20
10
0
N/A
20232024

State of AI in Financial Services: 2025 Trends | Survey Report | 5
A notable 41 percent of management-level respondents now recognize AI and
generative AI as transformational forces within their organizations. Financial
institutions are integrating AI across business functions, with significant upticks in
AI use for risk and compliance, marketing, sales, cybersecurity, and operations.
The benefits AI is bringing to business operations are diversifying. While fewer
companies are citing operational efficiencies, down slightly from last year, other
areas such as employee productivity and new business opportunities are seeing
substantial gains. This diversification signals a shift in AI’s role, evolving from a tool
for efficiency to a catalyst for comprehensive business transformation.
How has AI improved your business operations (select up to two)?
37%
Created operational
efficiencies
22%
Yielded more
accurate models
32%
Created competitive
advantage
22%
Improved employee
productivity
26%
Improved customer
experience
21%
Opened new business
opportunities
Of more than 20 use cases, cybersecurity experienced the highest YoY growth,
with more than one-third of respondents now assessing or investing in AI for
cybersecurity. The number of respondents expecting to use AI to address spear
phishing attacks more than doubled, jumping from 7 percent to 17 percent,
signaling a shift in the cyberthreat landscape. Similarly, the use of AI to confront
supply chain attacks and Distributed Denial of Service (DDoS) incidents increased,
indicating a heightened awareness and proactive stance against these threats.
What cybersecurity challenges are (or will be) addressed by AI
within your organization (select the top 2)?
34%
Fraud
detection
18%
Zero trust
25%
Credential and
identity attacks
17%
Spear Phishing
attacks
24%
Ransomware and
malware
16%
Supply chain
attacks
14%
Distributed Denial
of Service (DDoS)

Overall, companies are pinning down and implementing their most strategic AI use
cases and using AI to become more resilient in the face of evolving challenges.

State of AI in Financial Services: 2025 Trends | Survey Report | 6
Generative AI Is Driving Industry Transformation
Continuing enthusiasm from last year, generative AI workloads saw the
biggest YoY growth and now ranks as the second-most utilized workload
after data analytics. Half of management respondents indicated that their
first generative AI service or application had already been deployed, with an
additional 28 percent planning deployment within the next six months.
Per NVIDIA’s experts, techniques like domain-adaptive pretraining,
fine-tuning, and retrieval-augmented generation (RAG) are now being
leveraged in combination with open-source foundation models to create
a flywheel for generative AI development, which increases accuracy while
protecting enterprise information.
There’s been a significant rise in the use and assessment of generative AI
applications, particularly for AI agents designed to enhance the efficiency
and accuracy of tasks with automation and data retrieval.
Top Generative AI Use Cases
What use cases is your company using or assessing for
generative AI/LLMs (select all the apply)?
25%
27%
13%
15%
24%
15%
20%
25%
N/A
N/A
32%
38%
44%
46%
53%
43%
43%
38%
0 60503010 20 40
60%
53%
Customer experience and
engagement (including chatbots,
virtual assistants, agent assist)
Pricing, risk management,
and underwriting
Trading and portfolio
optimization
Code assist and
software generation
Synthetic data
generation
Document processing
(e.g. analysis of documents
for investment)
Report generation, synthesis,
and investment research
Creating marketing and sales
assets (ad copy, email copy,
and content generation)
Enterprise search
(e.g., searching through internal
documents, policies)
Compliance and customer onboarding
(Know Your Customer and
document processing)
2023
2024

State of AI in Financial Services: 2025 Trends | Survey Report | 7
Customer experience and report generation were reported as the most common
use cases for generative AI, with the percentage of respondents choosing
customer experience doubling YoY. Given their availability, cost efficiency, and
scalability, the rise in popularity of AI agents for customer experiences is likely
to continue. At the same time, digital avatar and digital human technology
continues to improve and is becoming easier to deploy. NVIDIA Blueprints bring
together nearly 20 AI models in a simplified API that enables organizations to
deploy digital assistant technology trained on proprietary data.
Generative AI agents for document processing were measured for the first
time in 2024 and reached 53 percent adoption. The generation of synthetic
data, particularly useful for financial services companies to test models and
strategies without compromising sensitive information, rose from 25 percent
to 46 percent. Financial services organizations are using synthetic data to train
fraud detection and identity verification models to recognize new threat vectors
and fraud patterns. The use of code assistance and software generation agents
was recorded at 44% in this first year of measurement, demonstrating how
quickly new use cases can catch on when technology can deliver a clear ROI,
either by removing inefficiencies or generating new revenue streams.
Among deployed generative AI use cases, 25 percent of respondents reported
the highest return on investment from AI-based trading and portfolio
optimization. Another 21 percent reported the best ROI for customer
experience and engagement, while 11 percent reported the best ROI for
both report generation and document processing.
What are the top generative AI use cases by ROI?
Half of management
respondents indicated that
their first generative AI
service or application had
already been deployed,
with an additional 28 percent
planning deployment within
the next six months.
25%
Trading and portfolio
optimization
11%
Report
generation
21%
Customer experience
and engagement
11%
Document
processing
NVIDIA Blueprints are
pre-defined, customizable
AI workflows designed
to assist developers in
creating and deploying
generative AI applications.
Build an AI Virtual
Assistant
Intelligent virtual assistant
in financial services redefine
customer experience by
delivering more accurate,
personalized, and sophisticated
responses than traditional
chatbot solutions.
Multimodal PDF Data
Extraction for Financial
Document Processing
Intelligent document processing
with generative AI lets financial
institutions gather insights
from unstructured data,
enabling faster decision-making
and reducing the risk
of financial losses.
Learn more about
NVIDIA Blueprints at
build.nvidia.com/blueprints

State of AI in Financial Services: 2025 Trends | Survey Report | 8
AI Is Driving Business Results for Financial Firms
The primary objective for industry leaders is to increase revenues and reduce
costs with AI. Almost all survey respondents believe that there’s ROI for AI, with
management particularly optimistic. More than 80 percent of management-level
respondents predicted a 2x or more return on AI investments, indicating growing
confidence in AI and a realization of AI’s financial benefits.
AI continues to help increase revenue and reduce costs. Nearly 70 percent
of respondents said that AI increased revenue by 5 percent or more, with 76
percent of managers reporting such gains. There was a dramatic rise in those
reporting a 10–20 percent revenue increase, from 0 percent in 2023 to 16
percent in 2024. Additionally, more than 60 percent of respondents said that
AI helped reduce annual costs by 5 percent or more.


29%Respondents

5–10%
Revenue
increase
Respondents
Reduced
annual
costs
16%
10–20%
23%
More
than 20%

35%

5–10%
17%
10–20%
12%
More
than 20%
How much has AI reduced your annual costs?

How much has AI increased your revenue?
These results underscore the significant impact AI is having on business
performance, demonstrating its potential to drive both top-line growth and
bottom-line savings.
AI Investment Continues Unabated
Forty percent more companies reported increased AI infrastructure spending
compared with the previous year, and 98 percent of management said they’ll
further increase AI infrastructure spending in 2025.
This investment in AI infrastructure suggests that companies are taking steps
to build AI factories with full-stack accelerated computing to support the
shift from AI exploration to deployment and accelerate AI adoption across
the organization. Infrastructure investment in AI factories is also positioning
companies to take advantage of agentic AI—systems that leverage vast
amounts of data from various sources and use sophisticated reasoning to
autonomously solve complex, multi-step problems. There are a multitude of
potential agentic AI applications for banks and investment firms, including
chatbots that offer personalized financial planning advice, automated risk
assessment agents, real-time fraud detection, and more.
”Accelerated computing is
revolutionizing financial services
by enabling faster, personalized
customer experiences driven by
big data insights.”
Rutger van Faassen,
Fintech Influencer,
Founder and CEO
of Informationbanker

State of AI in Financial Services: 2025 Trends | Survey Report | 9
How do you plan to invest in AI technologies over
the next 12 months (select up to two)?
Spend more on
infrastructure
50
40
30
20
10
0


Engage third-party
partners to
accelerate AI
adoption
Identify
additional AI
use cases
Hire more AI
experts
Optimize AI
workflows
Provide AI
training to
staff
31%34% 31%32% 42%32% 19%27% 30%26% 20%25%
20232024
Talent acquisition emerged as a key area of AI investment, with a
42 percent YoY increase in spending to hire more AI experts. Meanwhile,
a quarter of respondents said they’ll provide AI training to their current staff.
Infrastructure and talent acquisition spending are proving to complement one
another, as highly trained AI experts are seeking work environments with the
most advanced tools of their trade. This is a win-win for firms investing in AI
factories built with accelerated computing, as data scientists can train and
deploy more sophisticated, accurate models faster.
Other areas of AI spending include engaging third-party partners to
accelerate AI adoption, optimizing AI workflows, and identifying additional use
cases. However, there’s been a 23 percent decline in efforts to identify new
AI use cases, indicating a potential shift from exploration to deployment
or a saturation of the most promising applications.
In terms of top AI use cases by assessment or investment, customer experience
and engagement remain a priority, although reported investment decreased
slightly. Cybersecurity saw the most substantial YoY increase at 36 percent,
reflecting growing concerns over digital threats. Document processing,
algorithmic trading, and risk management also remain important use cases.
What AI use cases are you assessing or investing in?
42%
38%
29%
36%
34%
31%
36%
31%
N/A
33%


Customer experience
and engagement
Cybersecurity Document
processing
Algorithmic
trading
Risk
management
50
40
30
20
10
0
20232024
“Accelerated computing enables
financial services to achieve
higher operational margins
in key areas such as fraud
detection and compliance,
while enhancing decision-
making in risk management,
credit analysis, and portfolio
management.”
Efi Pylarinou,
Fintech Influencer

State of AI in Financial Services: 2025 Trends | Survey Report | 10
In the realm of ESG and sustainable finance, there’s been a notable transition
from pilot systems to production capabilities, with the number of companies
achieving production capabilities more than doubling. This shift highlights the
growing maturity and integration of AI in sustainable finance initiatives.
Do you have AI and machine learning initiatives for ESG and
sustainable finance in production?
0 3010 20 40
32%
13%
yes
2023
2024
Finally, there’s a marked increase in the commitment to trustworthy, safe,
and explainable AI. The percentage of companies launching pilot systems for
AI/ML governance frameworks rose from 21 percent to 36 percent. With stringent
regulations and the sensitive nature of financial data, finance organizations are
prioritizing the development of reliable AI aligned with ethical and legal standards.
AI Challenges on the Decline
This year’s survey data shows that many of the AI challenges companies
faced in previous years have plateaued or decreased, suggesting a shift from
assessment and testing phases to successful deployment.
Compared to last year, fewer companies reported issues associated with the
early stages of AI assessment and exploration. Fewer companies reported data
issues and privacy concerns, insufficient data for model training, difficulty
recruiting, and insufficient budget.
49%
33%
49%
31% 31%
15%
23%
35%


Data issues and
privacy concerns
Insufficient data
sizes for model
training
Difficulties in
recruiting and
retaining AI experts
and data scientists
Lack of
AI budget
50
40
30
20
10
0
20232024

State of AI in Financial Services: 2025 Trends | Survey Report | 11
These improvements reflect the maturing AI landscape where companies
are becoming more adept at managing the foundational challenges of
AI implementation.
Of course, not all challenges have been overcome. As companies advance in
their AI journey and leverage more computing power, new energy concerns arise.
What areas of energy-efficient computing does your company
find most challenging (select up to two)?
33%
29%
29%
28%

0 60503010 20 40
Managing the cost
of transitioning to
energy-efficient solutions
Measuring and tracking
energy consumption on a
per-workload basis
Deploying the latest
energy-efficient software
and educating new users
Staying updated on the
latest efficient software
integrations
To address energy challenges, companies are taking proactive measures such
as optimizing software to reduce runtime on servers and data centers, utilizing
cloud services with improved power usage effectiveness (PUE), and migrating
to more energy-efficient hardware such as GPUs.
The STAC-A2 benchmark is
designed by quants and
technologists to measure the
performance, scalability, quality and
resource efficiency of technology
stacks running market-risk analysis
for derivatives.
When testing the STAC-A2 options
pricing benchmark, NVIDIA GPUs
performed 16x faster and 3x more
energy efficiently than a CPU-only
system for the same workload.

Looking Forward: AI Opportunities
and Expectations
As financial services organizations navigate the AI-powered landscape,
the role of artificial intelligence in shaping business strategies and operations
continues to expand. Banks, asset managers, payments firms, and insurers
are building hundreds of AI-enabled applications for use cases ranging from
improving identity verification for anti-money laundering and Know Your
Customer (AML/KYC) initiatives, reducing false positives for transaction fraud,
generating new trading strategies to improve market returns, and automating
document management to reduce funding cycles.
Management is increasingly supportive of strategic AI initiatives, with nearly
60 percent of executive leadership acknowledging the value of AI in driving
business success. To realize maximum AI benefits, banking and finance leaders
are dedicating more resources to AI investment and talent acquisition.
There’s been a noticeable shift toward leveraging AI for creating competitive
advantages, improving customer experiences, and enhancing employee
productivity. These areas have seen increased focus as companies strive to harness
AI, not just for cost savings, but as a catalyst for transformation and growth.
One of the most significant trends is the increased focus on opening new
business opportunities and driving revenue, which rose from 17 percent to 24
percent YoY. This suggests a strategic realignment toward revenue-generating
activities and the exploration of new markets through AI.
What are your top goals related to AI?
0 3010 20
24%
17%
Focus on opening new
business opportunities
and driving revenue
2023
2024
Finally, survey data shows growing momentum in generative AI, with over
half of the respondents currently using this technology.
NVIDIA expects the next development in generative AI will be agentic AI,
in which financial institutions will use sophisticated, autonomous AI agents for
tasks such as cybersecurity threat detection, customer service, and accelerated
investment analysis. With AI factories, financial services companies will be
able to build AI applications powered by proprietary data for the best-fit,
most-personalized banking and investment solutions for customers.
The findings of this year’s report highlight AI’s growing importance in the
financial sector, positioning it as a cornerstone of innovation and a vital tool
for achieving strategic business goals.
60%
of executive leadership
acknowledges the
value of AI in driving
business success.
Ready to Get Started?
To learn more about how leading financial institutions are
using AI and generative AI, visit nvidia.com/finance
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