Unveiling the Impact and Future: AI in Finance Statistics for 2024

  • Editor
  • May 16, 2024

Welcome to the dynamic world of finance, where artificial intelligence (AI) is not just a participant but a game changer. With its unparalleled efficiency and precision, AI is reshaping how financial services operate, from automated trading systems to personalized customer interactions.

In today’s time, AI is no longer just a buzzword but a tangible force driving significant changes across various industries. Want to know how it is changing the financial sector and its practices? Hop on!

In this blog, I will provide a clear view of how deep AI has permeated the financial industry, backed by the latest statistics highlighting its growing presence and effectiveness.

Join me as I break down complex data into understandable insights, illustrating how AI is used and why its impact is so significant in the finance sector. Let’s discover together the numbers and narratives that define AI in finance today!

AI Dominance in Finance: Capturing Market Share

Machine learning is extensively integrated across many industries, with particularly strong adoption in sectors where large-scale data analysis and automation are critical.

AI has now taken over routine tasks like writing essays, academic writing, book writing, creating user manuals, etc. In finance, AI is streamlining complex and repetitive operations, including transaction processing, risk assessment, and customer service management.

The varying percentages reflect how essential machine learning has become to increasing operational efficiency, enhancing customer experiences, and driving innovations in these fields.

According to research by Business Fortune Insights, the Financial sector is extensively using AI, with a market share of 18%. The global market share of machine learning by the end-use industry looks like this:


The IT and telecommunications industry has the highest global machine-learning market share.

  1. IT and Telecommunications (19%): This sector is the largest user of machine learning, indicating its pivotal role in developing and applying new technologies, including AI-driven solutions.
  2. Banking, Financial Services, and Insurance (18%): Very close behind IT, this segment strongly utilizes machine learning for a range of applications, from fraud detection to customer service enhancements.
  3. Healthcare (14%): Machine learning in healthcare is significant and often used for diagnostic assistance, patient management, and drug discovery.
  4. Manufacturing (12%): Here, machine learning is employed to improve operational efficiencies, predictive maintenance, and supply chain management.
  5. Retail (12%): Retailers use machine learning for personalizing customer experiences, managing inventory, and optimizing logistics.
  6. Automotive and Transport (10%): Machine learning in this sector focuses on areas like autonomous driving, route optimization, and manufacturing automation.
  7. Advertising and Media (8%): Machine learning aids in content personalization, targeted advertising, and consumer behavior analysis.
  8. Others (8%): This category includes all other industries not specifically listed, showing that machine learning has diverse applications across various fields.

AI in Finance: Surging Market Value and Future Projections

The banking and finance industry is poised for a transformative shift thanks to artificial intelligence (AI), with the AI market expected to skyrocket from $712.4 million in recent years to an estimated $12,337.87 million by 2032.

This remarkable growth, projected at a compound annual growth rate (CAGR) of 33%, is driven by the increasing demand for more efficient and personalized financial services.


The AI market for the banking and finance sector is projected to reach a little above $12 billion by 2032.

Furthermore, the potential financial benefits of AI in banking are substantial. By the end of 2024, AI-driven improvements in front and middle-office operations could save banks up to $487 billion.


These operations, crucial for direct customer engagement and internal processing, are expected to account for over 90% of these savings.

In the latest financial update, SoftBank Group, the giant Japanese conglomerate, reported a significant boost with a net profit of ¥231 billion ($1.5 billion) for the quarter ending in March. – Source

As banks continue to integrate AI into these areas, they unlock new efficiencies and capabilities, setting a new standard in financial services and customer care.

Key AI Statistics in Finance: Unpacking the Numbers

  • The U.S. market for generative AI in banking and finance began at $244.48 million in 2023 and is projected to rise to $3,183.44 million by 2032, with a CAGR of 33%. North America is expected to lead this market growth during this period.
  • NVIDIA’s State of AI in Financial Services Report indicates that over 90% of financial services companies are evaluating or actively using AI.
  • Business Insider reports that 56% of financial services companies have adopted AI for risk management.
  • According to Business Insider, 75% of banks with assets over $100 billion are actively implementing AI strategies.
  • Temenos notes that 91% of U.S. banks use AI to detect fraud.
  • Forbes Advisor predicts that the global AI fintech market will reach $22.6 billion by 2025.
  • Forbes Advisor also states that 70% of financial services firms utilize machine learning for applications like cash flow predictions, credit score adjustments, and fraud detection.
  • Banks are projected to save $1 trillion by 2030 through the use of AI technologies.
  • The adoption of AI in the finance sector is expected to grow by 23% annually until 2025.
  • The costs associated with using AI in banking are estimated to decrease by 22% by 2030.

Strategic Use of AI in Finance: Revolutionizing the Industry

The integration of Artificial Intelligence (AI) in the financial sector is accelerating, transforming traditional practices and enhancing efficiency across various operations.

From consumers to major financial institutions, the usage of AI is widespread and growing. Here is a glimpse of AI usage in finance services:

  • Approximately 77% of consumers regularly use AI technologies for their banking and financial needs.
  • PWC indicates that 85% of financial institutions are implementing AI to enhance their financial operations.
  • Deloitte’s survey highlights that nearly half of the firms in financial services and insurance have fully implemented AI beyond initial trials.
  • Infosys finds that half of the financial services organizations utilize AI for automating processes.
  • McKinsey reports that 66% of banks have achieved performance gains from AI applications in critical areas.


  • AI adoption in the finance sector has seen a dramatic increase, rising from 45% in 2022 to an expected 85% by 2025.
  • Furthermore, 60% of firms are now using AI across multiple business areas.
  • About 36% of financial services executives have successfully utilized AI to reduce operational costs by 10%, highlighting its impact on financial efficiency.
  • Following AI implementation, 46% of financial services firms reported an improved customer experience, indicating AI’s role in enhancing service quality and customer satisfaction.

36% of the Executives were able to reduce 10% of costs using AI.

  • 75% of business leaders report that AI technologies have been instrumental in expanding their market share.
  • 63%of executives acknowledge that AI facilitates the creation of new products and services.
  • On average, companies implementing AI report 15% higher profitability compared to their competitors.
  • AI applications in the finance sector could decrease the demand for lower-skilled positions by more than 50%.


Adoption of AI/ML in Finance Operations

According to a Survey by Gartner in December 2024, there are varying stages of AI/ML adoption within finance operations, from no plans to implement to using AI/ML in production environments.

AI/ML Implementation Status Percentage of Respondents
No Planned AI/ML Implementation in the Finance Function 30%
AI/ML Implementation Is Planned — on Technology Roadmap 29%
Developing AI/ML Pilots 29%
Using AI/ML in Production 8%
Scaling AI/ML Usage to a Larger Group of Users 2%
Don’t Know 2%
  • No Planned AI/ML Implementation in the Finance Function (30%):

This represents the highest segment of respondents. A third of the participants indicated that they do not have any plans to implement AI/ML in their finance operations.

  • AI/ML Implementation Is Planned — on Technology Roadmap (29%):

Almost as many respondents are in the planning stage of implementing AI/ML technologies. This indicates a significant interest in adopting AI/ML in the near future.

  • Developing AI/ML Pilots (29%):

This category also comprises 29% of respondents, showing that a good portion of finance functions are currently testing AI/ML capabilities through pilot projects.

  • Using AI/ML in Production (8%):

A smaller percentage of respondents are at a stage where AI/ML technologies are actively used in production, suggesting that while interest and experimentation are high, full-scale operational integration is still relatively low.

  • Scaling AI/ML Usage to a Larger Group of Users (2%):

This indicates a very nascent stage of expanding the reach of AI/ML tools beyond initial implementations.

  • Don’t Know (2%):

A minimal percentage are unsure of their organization’s status regarding AI/ML implementation.


30% have no planned AI/ML implementation in the finance functions.

Here is a bar chart illustrating the AI/ML implementation status within finance functions. The chart clearly breaks down the percentages, showing the distribution from no planned implementation to active usage and scaling of AI/ML technologies.

These insights provide a clear picture of AI’s transformative impact on the financial sector, highlighting its pivotal role in enhancing operational efficiency, driving growth, and reshaping employment landscapes.

If I look forward, I can definitely see a bright future. In my opinion, the continued integration of AI is going to revolutionize these aspects, making AI an indispensable tool in the financial industry’s evolution.

Impact of AI on Various Aspects of Business Operations in the Financial Services Industry

The impact of AI on various business sectors has been mindblowing. The survey conducted by Statista highlights how AI technologies not only streamline processes but also enhance decision-making and operational efficiencies across different business sectors.

This technological shift is also evident in the development of AI prompts, making innovations in complex financial processes as well as others, like image processing, and writing applications.

Come unpack the numbers with me:

# Business Improvement 2022 (%) 2023 (%)
1 Improved Customer Experience 46 46
2 Created Operational Efficiencies 35 43
3 Created a Competitive Advantage 17 42
4 Yielded More Accurate Models 15 27
5 Opened New Business Opportunities/Revenues 15 23
6 Reduced the Total Cost of Ownership 14 20

The table above shows the results of a survey conducted in two consecutive years (2022 and 2023), showing how AI has improved business operations across various dimensions. Here’s a detailed analysis of each category presented in the graph:

  • Improved Customer Experience

In 2022, 46% of respondents reported improvements in customer experience due to AI, which slightly increased to 46% in 2023. This suggests a consistent recognition of AI’s role in enhancing customer interactions.

  • Created Operational Efficiencies

There was a significant increase in this category, from 35% in 2022 to 43% in 2023. This indicates that more businesses are realizing operational gains from AI each year.

  • Created a Competitive Advantage

This metric saw a substantial rise from 17% in 2022 to 42% in 2023, reflecting that AI’s impact on competitive positioning has more than doubled in perception over the year.

  • Yielded More Accurate Models

There was a significant growth from 15% in 2022 to 27% in 2023. This improvement suggests that AI’s capabilities in predictive analytics and decision-making are becoming more valued.

  • Opened New Business Opportunities/Revenues

An increase from 15% in 2022 to 23% in 2023 shows that AI is being leveraged to create new revenue streams and market opportunities.

  • Reduced the Total Cost of Ownership

Respondents noted a reduction in total cost of ownership due to AI rose from 14% in 2022 to 20% in 2023, indicating growing financial benefits from AI integration.


The impact of AI has increased in most aspects of the business from 2022 to 2023.

After analyzing the stats above, I firmly believe that there is going to be increasing appreciation and adoption of AI across different aspects of business, suggesting that AI’s strategic importance is being recognized more widely year over year.

Key Risks and Challenges: Navigating AI in the Finance Sector

AI in the finance sector unveils significant opportunities but also poses notable risks. Key concerns include data security, regulatory compliance, and the ethical use of AI-driven decision-making processes.

Understanding these risks is crucial for firms aiming to harness AI’s potential without compromising on integrity or customer trust.

According to The Economist Intelligence Unit Survey, the following are the key risks and challenges of implementing AI in financial services:

Risk Category Percentage (%)
Security and Privacy Breaches 46.6%
Failure of AI Systems 40.5%
Legal Responsibility from AI Decisions 32.2%
Workforce/Labor Displacement 19.5%
Losing Customer Trust 17.6%
Ethical Risks 16.6%
Regulatory Noncompliance 16.1%

This table outlines the primary risks identified with AI deployment in the finance sector, highlighting the concerns about security, reliability, legal liability, workforce impact, customer trust, ethics, and regulatory compliance. Here is a further elaboration of these risks:

1. Security and Privacy Breaches (46.6%) – This is the most cited risk, indicating that concerns about data protection and unauthorized access are paramount as organizations rely more heavily on AI technologies.

This high level of concern likely reflects the increasing prevalence of cybersecurity incidents and the challenges of ensuring privacy in the age of big data.

2. Failure of AI Systems (40.5%) – A significant number of respondents view the potential for AI systems to fail—either technically or in delivering expected outcomes—as a major risk.

This could encompass errors due to flawed algorithms, data biases, or system malfunctions that might lead to operational disruptions or poor decision-making.

3. Legal Responsibility from AI Decisions (32.2%) – Concerns about the legal implications of decisions made by AI systems, such as liability for mistakes or ethical misjudgments, are also prominent.

This reflects the ongoing legal and regulatory uncertainty surrounding AI, especially in fields like autonomous vehicles, healthcare, and financial services.

4. Workforce/Labor Displacement (19.5%) – Nearly one-fifth of respondents worry about AI leading to job losses or significant changes in the workforce structure, which underscores the societal and economic impacts of automation and AI integration.

5. Losing Customer Trust (17.6%) – This risk is associated with potential fallout from AI failures or ethical concerns, which could erode customer confidence in an organization’s products or services.

6. Ethical Risks (16.6%) and Regulatory Noncompliance (16.1%) – Both of these concerns reflect the challenges of ensuring that AI systems operate within ethical norms and regulatory frameworks, highlighting the importance of governance in AI deployment.


The highest risks when deploying AI in Finance are associated with security and privacy breaches.

AI adoption is seen as a strategic advantage; it also brings a variety of significant risks.

In my opinion, dominant concerns revolve around security, reliability, and legal accountability, which suggests that as much as organizations are eager to integrate AI into their operations, they are equally cautious about the potential repercussions.

I believe that addressing these risks effectively will likely require robust risk management strategies, including investing in cybersecurity, ensuring AI system reliability, and navigating the evolving legal terrain around AI technologies.

This cautious approach will be crucial for organizations aiming to leverage AI while maintaining trust and compliance.

The future of AI in finance is marked by rapid growth and transformative potential, promising to reshape how financial services operate. AI has become a powerful catalyst for transformation across multiple sectors, including marketing, education, Finance, etc.

By 2030, the AI market in finance is set to reach nearly $2 trillion, driven by advancements in machine learning and predictive analytics.

This technological evolution will not only streamline operations but also enhance customer experiences, risk management, and fraud detection across the sector. Here are the promising stats showing future predictions:



AI market to hit $184 billion in 2024, surging to $826.7 billion by 2030 at a 28.46% CAGR, highlighting its growing influence across industries. 

One major challenge of AI in financial services is ethical and legal. Privacy is a prime example: AI relies on massive amounts of personal data, and securing it with unclear permission structures can be difficult. 

Banks are rapidly adopting AI across their operations, from back-office tasks to customer service. Nearly all institutions are already on board or plan to be within three years, highlighting the widespread influence of AI in the banking sector. 

AI’s entry into finance began in 1982 with Renaissance Technologies, a firm pioneering “expert systems” for analyzing financial data and making investment decisions. 


Nvidia Impact.economist Gartner
Onlinedegrees Salesforce Juniperresearch
Capgemini Finance IDC
Worldbank Rsources.nvidia
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Dave Andre


Digital marketing enthusiast by day, nature wanderer by dusk. Dave Andre blends two decades of AI and SaaS expertise into impactful strategies for SMEs. His weekends? Lost in books on tech trends and rejuvenating on scenic trails.

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