AI in Finance Market

Report Code TC 9214
Published in Dec, 2024, By MarketsandMarkets™
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AI in Finance Market by Product (Algorithmic Trading, Virtual Assistants, Robo-Advisors, GRC, IDP, Underwriting Tools), Technology, Application (Fraud Detection, Risk Management, Trend Analysis, Financial Planning, Forecasting) - Global Forecast to 2030

 

Overview

The AI in finance market size is projected to grow from USD 38.36 billion in 2024 to USD 190.33 billion by 2030, at a CAGR of 30.6% during the forecast period. AI is crucial in finance for boosting efficiency, enhancing decision-making, strengthening financial stability, automating tasks, and accelerating data processing to improve operations and customer service productivity. Financial institutions leverage AI to process large volumes of data, leading to better market forecasts and refined investment strategies. In asset management, AI algorithms integrate diverse data sources, which allow fund managers to identify trends that enhance traditional methods. According to NVIDIA's 2024 Financial Services Industry Survey, over 70% of financial institutions reported improved operational efficiency due to AI, while 60% noted a reduction in operational costs by up to 30%. Customer satisfaction improved for 75% of firms, and 80% plan to increase AI investments in the next two years, emphasizing the technology's critical role in shaping the future of finance. This trend emphasizes AI's role in shaping finance, driving innovation and fostering competitive advantages across the industry.

Al In Finance Market

Attractive Opportunities in the Al In Finance Market

ASIA PACIFIC

The region is experiencing rapid digital transformation in the finance sector, particularly in China, Japan, South Korea, and India. Financial institutions are adopting AI-powered tools for risk management, fraud detection, driven by a strong push for improved operational efficiency and regulatory compliance.

Robust data security is crucial in the AI finance market to prevent data breaches and regulatory violations, ensuring compliance and protecting sensitive financial information from potential threats.

The rise in AI-based financial tools, such as predictive analytics and automated trading, enabling more accurate decision-making and enhanced customer experiences in the finance sector.

AI-driven algorithms improve risk detection and mitigation, enabling more secure financial practices by accurately identifying potential threats and enhancing proactive measures to prevent issues.

The AI in Finance market in the North American market is expected to be worth 73.83 USD billion by 2030, growing at a CAGR of 28.3% during the forecast period.

Impact of Generative AI on AI in Finance Market

Through increased user engagement and efficiency, generative Al is revolutionizing the finance industry by offering solutions that align with procedures for better customer experiences. Key factors include improved risk management via predictive analytics and automated financial reporting, which reduces errors. The technology also creates individualized financial services by analyzing customer data for communication and guidance. Customer interactions via chatbots are more efficient, and fraud detection ensures legal compliance. Finance professionals can use generative AI to make well-informed investment decisions by using projections based on market trends and historical data.

Al In Finance Market Impact

Global Al In Finance Market Dynamics

Driver: AI-driven algorithms enhance risk identification and mitigation, ensuring safer financial practices

Al-driven algorithms improve risk identification and mitigation in the financial industry for real-time data handling. Large volumes of both structured and unstructured data are analyzed by these algorithms to detect trends and abnormalities, such as fraud or variation in the market. Al can forecast possible risks based on past trends by using predictive analytics, which enables organizations to take proactive measures. Al also automates risk monitoring, ensuring the supervision of risk exposure and sending real-time alerts for unusual activity. Risk management procedures are more accurate and effective as a result of Al integration. Its integration makes risk management procedures more accurate and effective, enabling well-informed decision-making, promoting safer financial practices, and enhancing operational resilience.

Restraints: AI in finance raises concerns about bias and ethical issues in data usage

AI systems often rely on historical data, which may contain inherent biases, leading to discriminatory outcomes in areas like credit scoring and loan approvals. For instance, if past data reflects systemic inequalities, AI algorithms may perpetuate these biases, adversely affecting marginalized groups. Additionally, ethical issues arise regarding transparency and accountability; many AI models function as "black boxes," making it difficult for stakeholders to understand decision-making processes. This lack of clarity can undermine trust and compliance with regulatory standards, necessitating robust frameworks to address these ethical challenges effectively.

 

Opportunity: Rising demand for hyper-personalized financial products and tailored services drives long-term customer engagement

Customers seek specialized financial services and products that meet their specific needs. Al allows organizations to provide individualized experiences by analyzing large datasets. By using data analytics and machine learning, financial institutions can offer tailored services and suggest flexible investment plans and tailored recommendations. Long-term partnerships are fostered by personalization as customers experience a sense of worth and comprehension. Al-driven insights also enable businesses to be proactive and satisfy client needs. The use of hyper-personalization sets financial institutions apart, boosting client retention and attracting new businesses who seek specialized financial solutions.

Challenge: Ensuring data security to prevent breaches or violations

The sensitive nature of financial information makes organizations the prime targets for cyberattacks. Strong security measures are required to stop breaches and illegal access, as Al systems handle enormous volumes of consumer data. Financial companies must take security measures such as encryption and real-time monitoring to protect their data. Balancing data security and innovation is vital to building client trust and guaranteeing the application of Al in financial services.

Global Al In Finance Market Ecosystem Analysis

The AI in finance market ecosystem comprises a diverse range of stakeholders. Key players include fraud detection & prevention providers, risk management providers, customer service & engagement providers, financial compliance & regulatory reporting providers, investment & portfolio management providers, and end users. These entities collaborate to develop, deliver, and utilize social media AI solutions, driving innovation and growth in the market.

Top Companies in Al In Finance Market

Note: The above diagram only shows the representation of the AI in finance market ecosystem; it is not limited to the companies represented above.
Source: Secondary Research, Interviews with Experts, and MarketsandMarkets Analysis

 

By deployment mode, cloud segment will lead the market during the forecast period.

The cloud segment is anticipated to take the lead in the AI in finance market because of its scalable and flexible nature. To increase data accessibility and enhance customer experiences, financial institutions depend on cloud-based Al solutions. Cloud enables integration with Al tools for tasks like risk management, fraud detection, and financial planning and provides strong data storage and security. In the Al-driven finance industry, the cloud segment has a significant market share as a result of its capacity to support real-time analytics and speed up the deployment of Al applications.

Finance as business function: By application, automated bookkeeping & reconciliation segment will register the highest CAGR during the forecast period.

Businesses use Al solutions to automate data entry and other repetitive tasks such as invoice processing and ledger matching, which used to require manual labor. By reducing human error and offering real-time financial insights, AI-driven tools improve accuracy and provide real-time data. These tools allow businesses to allocate resources more efficiently, facilitating groups to prioritize strategic decision-making over daily tasks. Businesses are recognizing the advantages of automated systems for cost reduction and optimizing financial operations. It is anticipated that bookkeeping solutions will increase, propelling their rapid market expansion. As companies increasingly recognize the benefits of automated systems for cost reduction and financial operations optimization, the demand for bookkeeping solutions is expected to grow, driving rapid market expansion.

Finance as business operation: By end user, fintech segment will register the highest CAGR during the forecast period.

Fintech companies increasingly leverage AI to automate financial services, enhance customer experiences, and improve operational efficiency. This technology enables real-time data analysis, which is crucial for personalized financial solutions and effective risk management. As consumers demand faster and more efficient services, fintech firms utilize AI for tasks such as fraud detection, credit scoring, and customer engagement through chatbots. The continuous innovation and competitive landscape in fintech drive the need for sophisticated AI solutions, positioning this segment for substantial growth in the coming years.

Finance as business operation: By end user, fintech segment will register the highest CAGR during the forecast period.

NDue to substantial investment in the Al industry and advanced technological infrastructure, the North American region holds the largest market share in the finance sector. To improve customer experiences, streamline operations across banks and investment firms, and improve financial practices, the US is at the forefront of adopting Al technologies. With its expanding fintech ecosystem and initiatives, Canada also contributes significantly, which encourages innovation in the financial services industry. With the existence of both tech startups and large corporations, Al solutions are being adopted more widely by research institutions in both nations, positioning North America at the top of financial innovation.

HIGHEST CAGR MARKET DURING FORECAST PERIOD
CANADA FASTEST-GROWING MARKET IN THE REGION
Al In Finance Market Size and Share

Recent Developments of Al In Finance Market

  • In August 2024, Datamatics partnered with Microsoft to develop tailored copilot solutions to enhance process automation and drive business transformation. The collaboration has led to the launch of a Partner On-boarding Copilot, available in the Microsoft Teams store, which integrates Azure OpenAI with Datamatics' Intelligent Automation Platform. This partnership focuses on customizing solutions for individual organizations, allowing them to leverage Microsoft 365 and create bespoke copilots.
  • In August 2024, IBM and Intel announced a collaboration to deploy Intel Gaudi 3 AI accelerators as a service on IBM Cloud, expected to launch in early 2025. This initiative aims to enhance the cost-effective scaling of enterprise AI while ensuring security and resiliency. IBM Cloud will be the first to adopt Gaudi 3, supporting both hybrid and on-premises environments. The integration will optimize AI workloads within IBM's Watsonx platform, enabling clients to improve performance and reduce total ownership costs for AI solutions across various industries.
  • In March 2024, FIS announced a collaboration with Stratyfy to enhance its SecurLOCK card fraud management solution. This partnership aims to significantly improve the detection and prevention of fraudulent transactions while reducing false positives, thus creating a safer payment experience for clients and consumers. This collaboration is timely, with fraud projected to cost over USD 40 billion annually by 2027. The initiative promises to minimize disruptions from fraud rules and enhance the overall efficiency of transaction resolutions, benefiting both businesses and their customers.
  • Genesis Bank partnered with Fiserv in February 2024 to support small businesses in low-to-moderate-income neighborhoods by providing customized access to Clover technology (point-of-sale). This initiative aims to empower local businesses with advanced point-of-sale and business management solutions, enhancing their operational capabilities. The partnership focuses on meeting the specific needs of small businesses, particularly those served by Minority Depository Institutions (MDIs).

Key Market Players

List of Top Al In Finance Market Companies

The Al In Finance Market is dominated by a few major players that have a wide regional presence. The major players in the Al In Finance Market are

  • FIS (US)
  • Fiserv (US)
  • Google (US)
  • Microsoft (US)
  • Zoho (India)
  • IBM (US)
  • Socure (US)
  • Workiva (US)
  • Plaid (US)
  • C3 AI (US)
  • HighRadius (US)
  • AWS (US)
  • SAP (US)
  • HPE (US)
  • Oracle (US)
  • Intel (US)
  • NVIDIA (US)
  • Salesforce (US)
  • DataRobot (US)
  • Enova International (US)
  • AlphaSense (US)
  • NetApp (US)
  • Vectra AI (US)

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Scope of the Report

Report Attribute Details
Market size available for years 2019-2030
Base year considered 2023
Forecast period 2023
Forecast units (USD million/billion)
Segments Covered Product type, Technology, Application, End user, and Region
Regions covered North America, Europe, Asia Pacific, Middle East & Africa, Latin America

 

Key Questions Addressed by the Report

What are the opportunities for the AI in finance market?
There are various opportunities in the AI in finance market. AI enables hyper-personalization of financial products, tailoring services to individual customer needs and preferences, enhancing engagement and satisfaction. Financial institutions will harness AI to analyze vast datasets for actionable insights, driving strategic growth and innovation. This technology will also improve risk assessment models, enabling more accurate credit scoring and better management of financial risks.
Define the AI in finance market.
Artificial intelligence (AI) in finance is a set of technologies encompassing machine learning (ML), NLP, generative AI, and predictive analytics, which can replicate human intelligence and decision-making abilities to enhance how organizations analyze, manage, invest, and protect financial processes and systems. AI technology modernizes all finance-based business operations and functions by streamlining traditionally manual processes, unlocking deeper insights from generated data, and better managing delivery outcomes. AI in finance tools supports faster, contactless interactions, including real-time credit approvals and improved fraud protection and risk assessment. Moreover, AI is also changing how financial organizations engage with customers, predicting their behavior and understanding their purchase preferences. This enables more personalized interactions, faster and more accurate customer support, credit scoring refinements, and innovative products and services.
Which region is expected to have the largest share in the AI in finance market?
The North American region will acquire the largest share of the AI in finance market during the forecast period.
Which are the major market players covered in the report?
Some of the key companies in the AI in finance market are FIS (US), Fiserv (US), Google (US), Microsoft (US), Zoho (India), IBM (US), Socure (US), Workiva (US), Plaid (US), SAS (US), C3 AI (US), HighRadius (US), AWS (US), SAP (Germany), HPE (US), Oracle (US), Intel (US), NVIDIA (US), Salesforce (US), DataRobot (US), Enova International (US), AlphaSense (US), NetApp (US), Ocrolus (US), Vectra AI (US), Teradata (US), Pega (US), Vena Insights (US), Affirm (US), Symphony AI (US), Envestnet Yodlee (US), Addepto (Poland), Deeper Insights (UK), H2O.ai (US), App0 (US), Underwrite.ai (US), Deepgram (US), Emagia (US), InData Labs (US), Zest AI (US), Scienaptic AI (US), Gradient AI (US), Kasisto (US), Trumid (US), DataVisor (US), Kavout (US), and WealthBlock (US).
How big is the global AI in finance market today?
The global AI in finance market is projected to grow from USD 38.36 billion in 2024 to USD 190.33 billion by 2030, at a CAGR of 30.6% during the forecast period.

 

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The study involved major activities in estimating the current market size for the AI in Finance market. Exhaustive secondary research was done to collect information on the AI in Finance market. The next step was to validate these findings, assumptions, and sizing with industry experts across the value chain using primary research. Different approaches, such as top-down and bottom-up, were employed to estimate the total market size. After that, the market breakup and data triangulation procedures were used to estimate the market size of the segments and subsegments of the AI in Finance market.

Secondary Research

The market for the companies offering AI in Finance solutions is arrived at by secondary data available through paid and unpaid sources, analyzing the product portfolios of the major companies in the ecosystem, and rating the companies by their performance and quality. Various sources were referred to in the secondary research process to identify and collect information for this study. The secondary sources include annual reports, press releases, investor presentations of companies, white papers, journals, certified publications, and articles from recognized authors, directories, and databases.

In the secondary research process, various secondary sources were referred to for identifying and collecting information related to the study. Secondary sources included annual reports, press releases, and investor presentations of AI in Finance vendors, forums, certified publications, and whitepapers. The secondary research was used to obtain critical information on the industry’s value chain, the total pool of key players, market classification, and segmentation from the market and technology-oriented perspectives.

Primary Research

In the primary research process, various primary sources from both the supply and demand sides were interviewed to obtain qualitative and quantitative information for this report. The primary sources from the supply side included industry experts, such as Chief Executive Officers (CEOs), Vice Presidents (VPs), marketing directors, technology and innovation directors, and related key executives from various key companies and organizations operating in the AI in Finance market. After the complete market engineering (calculations for market statistics, market breakdown, market size estimations, market forecasting, and data triangulation), extensive primary research was conducted to gather information and verify and validate the critical numbers arrived at. Primary research was also conducted to identify the segmentation types, industry trends, competitive landscape of AI in Finance solutions offered by various market players, and key market dynamics, such as drivers, restraints, opportunities, challenges, industry trends, and key player strategies. In the complete market engineering process, the top-down and bottom-up approaches were extensively used, along with several data triangulation methods, to perform the market estimation and market forecasting for the overall market segments and subsegments listed in this report. Extensive qualitative and quantitative analysis was performed on the complete market engineering process to list the key information/insights throughout the report.

Al In Finance Market Size, and Share

Note: Tier 1 companies account for annual revenue of >USD 10 billion; tier 2 companies’ revenue ranges
between USD 1 and 10 billion; and tier 3 companies’ revenue ranges between USD 500 million–USD 1 billion

To know about the assumptions considered for the study, download the pdf brochure

Market Size Estimation

Both top-down and bottom-up approaches were used to estimate and validate the total size of the cell culture market. These methods were also used extensively to estimate the size of various subsegments in the market. The research methodology used to estimate the market size includes the following:

Al In Finance Market : Top-Down and Bottom-Up Approach

Al In Finance Market Top Down and Bottom Up Approach

Data Triangulation

After arriving at the overall market size using the market size estimation processes explained above, the market was split into various segments and subsegments. The data triangulation and market breakup procedures were employed, wherever applicable, to complete the overall market engineering process and arrive at the exact statistics of each market segment and subsegment. The data was triangulated by studying various factors and trends from both the demand and supply sides.

Market Definition

Artificial intelligence (AI) in finance helps drive insights for data analytics, performance measurement, predictions and forecasting, real-time calculations, customer servicing, intelligent data retrieval, and more. It is a set of technologies that enables financial services organizations to better understand markets and customers, analyze and learn from digital journeys, and engage in a way that mimics human intelligence and interactions at scale.

Stakeholders

  • Risk Assessment and Compliance Software Developers
  • AI in Finance Software Vendors
  • Financial Analysts and Managers
  • AI in Finance Service Providers
  • Financial Marketers
  • Business Owners and Executives
  • Distributors and Value-Added Resellers (VARs)
  • Independent Software Vendors (ISVs)
  • Managed Service Providers
  • Support and Maintenance Service Providers
  • System Integrators (SIs)/Migration Service Providers
  • Original Equipment Manufacturers (OEMs)
  • Technology Providers

Report Objectives

  • To define, describe, and predict the AI in Finance market by product (by type and deployment mode), technology, application (by business operation and business function), end user (by business function and business operation) and region
  • To provide detailed information related to major factors (drivers, restraints, opportunities, and industry-specific challenges) influencing the market growth
  • To analyze the micro markets with respect to individual growth trends, prospects, and their contributions to the total market
  • To analyze the opportunities in the market for stakeholders by identifying the high-growth segments of the AI in Finance market
  • To analyze opportunities in the market and provide details of the competitive landscape for stakeholders and market leaders
  • To forecast the market size of five main regions: North America, Europe, Asia Pacific, the Middle East & Africa, and Latin America
  • To profile key players and comprehensively analyze their market rankings and core competencies
  • To analyze competitive developments, such as partnerships, new product launches, and mergers & acquisitions, in the AI in Finance market
  • To analyze the impact of the recession across all regions in the AI in Finance market

Available Customizations

With the given market data, MarketsandMarkets offers customizations as per your company’s specific needs. The following customization options are available for the report:

Product Analysis

  • Product quadrant, which gives a detailed comparison of the product portfolio of each company.

Geographic Analysis as per Feasibility

  • Further breakup of the North American AI in Finance market
  • Further breakup of the European AI in Finance market
  • Further breakup of the Asia Pacific AI in Finance market
  • Further breakup of the Middle Eastern & African AI in Finance market
  • Further breakup of the Latin America AI in Finance market

Company Information

  • Detailed analysis and profiling of additional market players (up to five)

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