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  • Generative AI in Finance Market

Generative AI in Finance Market By Deployment Model (Cloud Deployment, On-Premises Deployment, and Hybrid Deployment), By Application, By Technology - Global Industry Outlook, Key Companies (Amazon Web Services Inc., Cisco Systems Inc., Microsoft Corporation, and others), Trends and Forecast 2024-2033

Published on : May-2024  Report Code : RC-923  Pages Count : 275  Report Format : PDF
Overview Table of Content Download Report's Excerpt Request Free Sample

Market Overview

The Global Generative AI in Finance Market size is expected to reach a value of USD 1,362.6 million in 2024 which is further expected to reach USD 19,186.3 in 2033 at a CAGR of 34.2%. 

Generative AI in Finance Market growth Analysis

Generative artificial intelligence (GenAI) has emerged as an effective tool in banking and finance, imparting creative solutions to complicated financial problems. Risk assessment, a crucial aspect of financial operations, can be enhanced through the introduction of artificial data that simulates potential threats, allowing institutions to better determine and mitigate risks. GenAI additionally aids in fraud detection by producing synthetic data that closely resembles fraudulent activity, thereby enhancing the precision of fraud detection systems. Moreover, it facilitates investment portfolio optimization, leading to better efficacy, precision, and adaptability in financial decision-making.

According to a report by KPMG, over 83% of respondents make use of AI for financial planning, together with predictive modeling, situational development, and financial insights. GenAI streamlines workflow and processes keeps precision and pace, and generates new ideas, making it helpful for economic companies seeking automation and efficiency.

Furthermore, the implementation of GenAI fosters personalized consumer responses, improving service suggestions and bolstering security and accountability. By integrating GenAI with human engagement, economic institutions can deliver seamless experiences tailored to consumer needs. As demand for GenAI grows across numerous verticals of the finance sector, its adoption is poised to drive marketplace expansion in the years to come.

Key Takeaways

  • Market Size: The Generative AI in Finance Market size is projected to be worth around USD 19,186.3 Mn by 2033 from USD 1,362.6 Mn in 2024, at a CAGR of 34.2%.
  • Market Definition: The Global Generative AI in Finance Market refers to the global industry centered on the development, implementation, and usage of artificial intelligence strategies for creative problem-solving in financial services.
  • Technology Segment Analysis: Natural Language Processing technology is projected to dominate the Generative AI in the Finance market with a 28.4% market share in 2024.
  • Deployment Model Segment Analysis: Cloud-based deployment is projected to dominate the generative AI in the finance market in the deployment model with 32.5% due to its high scalability and cost-efficiency.
  • Application Segment Analysis: Risk management is projected to dominate the application segment in this market with a 32.4% market share in 2024.
  • Regional Analysis: North America is projected to lead the generative artificial intelligence in the finance market with the highest market share of 38.1% in 2024.

Use Cases

  • Risk Assessment: Generative AI simulates potential threats, helping establishments in higher assessing and mitigating dangers through the introduction of artificial data closely resembling real-world scenarios.
  • Fraud Detection: By generating artificial data mimicking fraudulent activity patterns, Generative AI enhances the precision and effectiveness of fraud detection systems.
  • Investment Portfolio Optimization: Generative AI enables the optimization of investment portfolios, permitting economic companies to make more knowledgeable choices with better efficacy and adaptability.
  • Financial Planning: Through predictive modeling and situational improvement, Generative AI provides treasured insights for financial planning, which includes budgeting and forecasting.
  • Market Intelligence: Generative AI collects and analyzes market data, offering economic companies actionable insights and strategic records for informed decision-making and competitive advantage.

Market Dynamic

Drivers

Automation of Complex Financial Tasks
The adoption of generative AI is swiftly increasing across financial institutions, primarily driven by means of its capacity to automate complex financial tasks which include threat evaluation, compliance tracking, and fraud detection. By leveraging AI technology, monetary groups streamline operations, improve accuracy, and reduce costs related to manual procedures. This trend is fueled by the need for performance and agility in the face of growing data volumes and regulatory requirements.

Enhanced Customer Experience
Generative AI applications are revolutionizing the consumer experience inside the finance sector with the aid of presenting personalized services, tailor-made economic advice, and custom-designed product offerings specific to consumer demand. This consumer-centric approach now not only fosters loyalty but also attracts new consumers. As consumers demand more customized and intuitive interactions with financial services, the adoption of AI-pushed solutions will become imperative for keeping a competitive edge in the market.

Restraints

Technical Complexities
The implementation of generative AI models for banking and finance is a major technical challenge that involves possessing the technical expertise, large workforce, and enormous computing resources that are required. Tackling the mentioned complexities would need substantial investment in the infrastructure, the required competence in artificial intelligence, and wide access to big data. Moreover, taking it from the existing systems and workflows can be stressful and time-consuming, thus prohibiting the advancement of AI generative technologies in the financial market.

Data Privacy and Security Concerns
The security and privacy of economic data used by generative AI solutions make it even more challenging to apply them to sensitive data. Financial institutions must adhere to rigorous regulations and establish sturdy security measures to protect customer information from data breaches and unauthorized access. This matter is only met by continuous vigilance, compliance with the industry standards, as well as investment in new cybersecurity technologies.

Opportunities

Market Expansion through Chatbots and Virtual Assistants
Chatbots and virtual assistants can play a vital role in making the finance sector grow continually within the financial market. Such AI-sponsored devices can work 24/7 to provide customers with round-the-clock assistance, customized interactions, and improved operational efficiency. Banks can utilize chatbots and virtual assistants which help them to improve consumer relationships, accelerate processes optimize the offerings they put, in the hyper-competitive environment.

Forecasting and Decision-Making Capabilities
Generative AI enables extraordinary accuracy in financial forecasting and decision-making with the ability to process historical data and create future scenarios by generating them. Such predictive function enables financial institutes to make knowledge-based investment decisions, foresee market trends with accuracy, and manage risks timely. With AI-based customized information, companies can seize the competitive advantage, allocate resources rationale, and trigger business growth.

Trends

Increased Utilization in Fraud Detection
Generative AI is witnessing whispered adoption in fraud detection inside the banking and financial services sector. The dynamic nature of fraudulent activities and the sheer extent of financial transactions pose significant solutions for traditional detection techniques. AI-driven solutions offer enhanced capabilities for detecting fraudulent patterns, minimizing fake positives, and improving overall safety posture.

Personalization and Customization
Financial institutions are increasingly leveraging generative AI to personalize experience through tailored financial advice, customized product recommendations, and responsive service interfaces. This trend reflects the growing demand for customized interactions and intuitive user experiences within the finance market. By harnessing AI-driven personalization, organizations can give a boost to customer relationships, drive loyalty, and differentiate their offering in a competitive landscape.

Research Scope and Analysis

By Technology

Natural language processing is projected to dominate the global generative AI in the finance market with 28.4% of the market share in 2024. Natural Language Processing (NLP) dominates this segment because of its versatility and applicability throughout diverse financial tasks. NLP enables machines to recognize, interpret, and generate human language, making it critical for responsibilities together with sentiment analysis, customer service chatbots, and report summarization. In finance, NLP-powered systems can examine wide amounts of textual information from resources like news articles, financial reports, and social media to extract treasured insights, determine market sentiment, and make data-driven decisions. 

Moreover, NLP enables regulatory compliance by automating the extraction of key information from legal documents and financial statements. Its vast adoption in regions inclusive of automatic trading and selling, risk evaluation, and fraud detection underscores its importance in driving efficiency and innovation inside the finance sector. As monetary establishments increasingly include AI technologies to optimize operations and enhance customer experience, the dominance of NLP is expected to persist and evolve further.

By Deployment Model

In the global generative AI in finance marketplace, cloud-based deployment is projected to dominate this segment with highest market share in 2024 because of the numerous key advantages it gives to financial establishments. Firstly, cloud deployment provides scalability, permitting companies to effortlessly scale their AI infrastructure in step with fluctuating computational demands. This scalability is especially useful for managing massive volumes of financial data and complex AI algorithms in generative AI applications. 

Secondly, cloud-based solutions provide flexibility and accessibility, enabling remote access to AI sources from any location with internet connectivity. This enhances collaboration among geographically dispersed teams and helps seamless integration with existing workflows. Additionally, cloud deployment frequently includes decreased upfront costs compared to on-premises infrastructure, as it eliminates the need for costly hardware investments and maintenance. 

Furthermore, cloud providers typically provide robust security measures and compliance certifications, addressing issues associated with data privacy and regulatory necessities in the finance sector. Overall, these elements contribute to the dominance of cloud-based deployment in using the adoption of generative AI technology within the finance enterprise.

By Application

In the global generative AI in finance market, risk management is expected to dominate the application segment with 32.5% of the market share in 2024 due to its vital role in ensuring financial stability and regulatory compliance. Generative AI technologies offer advanced abilities for analyzing complicated datasets, identifying patterns, and predicting potential risks across numerous financial instruments and markets. This technology enables financial establishments to evaluate and mitigate risks extra efficiently, thereby minimizing potential losses and improving overall risk management strategies.

Generative AI in Finance Market Application Share Analysis

Moreover, the dynamic and interconnected nature of financial markets gives unique challenges for risk management, along with marketplace volatility, regulatory changes, and emerging threats. Generative AI solutions provide real-time insights and predictive analytics, allowing companies to adopt quickly to converting market situations and regulatory requirements. By leveraging AI-driven threat control equipment, economic institutions can optimize capital allocation, enhance decision-making processes, and hold a competitive edge in the market.

Additionally, the increasing complexity of financial products and transactions necessitates sophisticated risk management strategies, which generative AI can efficiently cope with. From credit risk assessment to operational risk modeling, AI-powered risk management solutions offer comprehensive coverage across diverse threat categories, making them critical for financial establishments working in the modern-day dynamic and competitive panorama. 

The Generative AI in Finance Market Report is segmented on the basis of the following:

By Technology

  • Natural Language Processing
  • Deep Learning
  • Reinforcement Learning
  • Generative Adversarial Networks
  • Computer Vision
  • Predictive Analytics
  • Other

By Deployment Model

  • Cloud Deployment
  • On-Premises Deployment
  • Hybrid Deployment

 By Application

  • Risk Management
  • Fraud Detection
  • Trading and Portfolio Management
  • Investment Research
  • Other

Regional Analysis

North America is projected to dominate the global generative AI in finance market with 38.1% of the market share in 2024 due to numerous key elements. First of all, this region has a significant number of major financial institutions which include banks, investment firms, and insurance groups that were the early adopters of AI to stay ahead and to get their competitive edge. The North American finance sector has access to the robust financial infrastructure and technological innovation environment, which enable the production of generative AI solutions specific to the finance sector's needs.

Generative AI in Finance Market Regional Analysis

Consequently, this continent is also characterized by having an eye on AI talent, research institutions, and tech corporations that utilize AI applications in the field of finance through ongoing innovation and improvement. The fact that there are famous universities and AI research facilities that are located in the area and the political environment that is favorable when it comes to investments and research and development in artificial intelligence all make this region a leader in artificial intelligence.

Furthermore, North America functions not only as an innovator but also as a destination for venture capital funding and startup incubation that flourish thus creating an ardent AI startup community and rising technological companies to focus on finance. This entrepreneurial subculture promotes experimentation and adoption of cutting-edge AI solutions, contributing to North America's dominance in the global generative AI in the finance market. Furthermore, the region's mature regulatory environment and adherence to data privacy standards instill belief and self-assurance amongst financial establishments in deploying AI technologies, further fueling market growth. 

By Region

North America
  • The U.S.
  • Canada
Europe
  • Germany
  • The U.K.
  • France
  • Italy
  • Russia
  • Spain
  • Benelux
  • Nordic
  • Rest of Europe
Asia-Pacific
  • China
  • Japan
  • South Korea
  • India
  • ANZ
  • ASEAN
  • Rest of Asia-Pacific
Latin America
  • Brazil
  • Mexico
  • Argentina
  • Colombia
  • Rest of Latin America
Middle East & Africa
  • Saudi Arabia
  • UAE
  • South Africa
  • Israel
  • Egypt
  • Rest of MEA

Competitive Landscape

The aggressive landscape of the global generative AI in the finance marketplace is characterized by intense competition amongst key players striving to innovate and seize the highest market share. Established tech companies along with IBM, Microsoft, and Google leverage their sizeable assets and understanding of AI to develop advanced generative AI solutions tailored for financial applications. These industry giants regularly collaborate with financial establishments to deploy modern AI technology and gain a competitive edge.

In addition to already established players, a vibrant ecosystem of startups and emerging technology corporations contributes to the aggressive landscape. Startups consisting of Data Robot, H2O.Ai, and Numerai focus on niche segments inside the finance sector, providing specialized generative AI solutions for risk control, fraud detection, and trading strategies.

Furthermore, consulting firms and system integrators play a significant function in the market, giving advisory services, implementation help, and customized solutions to financial institutions looking to undertake generative AI technology. As the market keeps evolving, partnerships, mergers, and acquisitions are probable to reshape the competitive landscape, with businesses vying for leadership positions in this rapidly growing sector.

Some of the prominent players in the Global Generative AI in Finance Market are:
  • Amazon Web Services Inc.
  • Cisco Systems Inc.
  • Microsoft Corporation
  • SAP SE
  • BigML Inc.
  • Fair Isaac Corporation
  • IBM Corporation
  • Google LLC
  • Accenture
  • Oracle
  • Other Key Players

Recent Development

  • In March 2024, Federal Bank integrates AI, including Google's Generative AI Platform, to enhance the client experience. It pioneers AI in website search, voice search, graphics creation, and personalized communication, prioritizing human-centricity for improved efficiency.
  • In November 2023, Goldman Sachs is developing twelve projects integrating generative AI into its operations, addressing both the opportunities and challenges this technology presents, according to a senior executive at the Reuters NEXT conference.
  • In July 2023, Jumpsuit Finance reported the public unveiling of Genesis J2T-F1 for the Financial Services Industry. This tool, covering banking, asset management, exchanges, fintech, and wealth management, offers scenario forecasting to expedite insight generation.
  • In June 2023, Agora Services unveiled the 'AIFinTech100' for 2023, showcasing the globe's most innovative solution providers. As a premier provider of next-gen bank-as-a-service and integrated fintech services, Agora curated this list to spotlight firms revolutionizing financial services through artificial intelligence.
  • In June 2023, Google Cloud launched an anti-money laundering tool powered by artificial intelligence. This tool aims to assist international financial institutions in identifying money laundering with greater precision and effectiveness.

Report Details

Report Characteristics
Market Size (2024) USD 1,362.6 Mn
Forecast Value (2033) USD 19,186.3 Mn
CAGR (2024-2033) 34.2%
Historical Data 2018 – 2023
Forecast Data 2024 – 2033
Base Year 2023
Estimate Year 2024
Report Coverage Market Revenue Estimation, Market Dynamics, Competitive Landscape, Growth Factors and etc.
Segments Covered By Deployment Model (Cloud Deployment, On-Premises Deployment, and Hybrid Deployment), By Application (Risk Management, Fraud Detection, Trading and Portfolio Management, Investment Research, and Others), By Technology (Natural Language Processing, Deep Learning, Reinforcement Learning, Generative Adversarial Networks, Computer Vision, Predictive Analytics, and Other)
Regional Coverage North America – The US and Canada; Europe – Germany, The UK, France, Russia, Spain, Italy, Benelux, Nordic, & Rest of Europe; Asia- Pacific– China, Japan, South Korea, India, ANZ, ASEAN, Rest of APAC; Latin America – Brazil, Mexico, Argentina, Colombia, Rest of Latin America; Middle East & Africa – Saudi Arabia, UAE, South Africa, Turkey, Egypt, Israel, & Rest of MEA
Prominent Players Amazon Web Services Inc., Cisco Systems Inc., Microsoft Corporation, SAP SE, BigML Inc., Fair Isaac Corporation, IBM Corporation, Google LLC, Accenture, Oracle, and Other Key Players
Purchase Options Physio-Control Inc., Schiller, Medtronic, Abbott, Boston Scientific Corporation, Koninklijke Philips N.V., Zoll Medical Corporation, BIOTRONIK, Progetti Srl, LivaNova Plc, and Other Key Players

 

Frequently Asked Questions

  • How big is the Global Generative AI in Finance Market?

    The Global Generative AI in Finance Market size is estimated to have a value of USD 1,362.6 million in 2024 and is expected to reach USD 19,186.3 million by the end of 2033.

  • Which region accounted for the largest Global Generative AI in Finance Market?

    North America is expected to be the largest market share for the Global Generative AI in Finance Market with a share of about 38.1% in 2024.

  • Who are the key players in the Global Generative AI in Finance Market?

    Some of the major key players in the Global Generative AI in Finance Market are Amazon Web Services Inc., Cisco Systems Inc., Microsoft Corporation, SAP SE, and many others.

  • What is the growth rate of the Global Generative AI in Finance Market?

    The market is growing at a CAGR of 34.2 percent over the forecasted period.

  • Contents

      1.Introduction
        1.1.Objectives of the Study
        1.2.Market Scope
        1.3.Market Definition and Scope
      2.Mobile Clinics Market Overview
        2.1.Global Mobile Clinics Market Overview by Type
        2.2.Global Mobile Clinics Market Overview by Application
      3.Mobile Clinics Market Dynamics, Opportunity, Regulations, and Trends Analysis
        3.1.Market Dynamics
          3.1.1.Mobile Clinics Market Drivers
          3.1.2.Mobile Clinics Market Opportunities
          3.1.3.Mobile Clinics Market Restraints
          3.1.4.Mobile Clinics Market Challenges
        3.2.Emerging Trend/Technology
        3.3.PESTLE Analysis
        3.4.PORTER'S Five Forces Analysis
        3.5.Technology Roadmap
        3.6.Opportunity Map Analysis
        3.7.Case Studies
        3.8.Opportunity Orbits
        3.9.Pricing Analysis
        3.10.Ecosystem Analysis
        3.11.Supply/Value Chain Analysis
        3.12.Covid-19 & Recession Impact Analysis
        3.13.Product/Brand Comparison
      4.Global Mobile Clinics Market Value ((US$ Mn)), Share (%), and Growth Rate (%) Comparison by By Technology, 2017-2032
        4.1.Global Mobile Clinics Market Analysis by By Technology: Introduction
        4.2.Market Size and Forecast by Region
        4.3.Natural Language Processing
        4.4.Deep Learning
        4.5.Reinforcement Learning
        4.6.Generative Adversarial Networks
        4.7.Computer Vision
        4.8.Predictive Analytics
        4.9.Other
      5.Global Mobile Clinics Market Value ((US$ Mn)), Share (%), and Growth Rate (%) Comparison by By Deployment Model, 2017-2032
        5.1.Global Mobile Clinics Market Analysis by By Deployment Model: Introduction
        5.2.Market Size and Forecast by Region
        5.3.Cloud Deployment
        5.4.On-Premises Deployment
        5.5.Hybrid Deployment
      6.Global Mobile Clinics Market Value ((US$ Mn)), Share (%), and Growth Rate (%) Comparison by By Application, 2017-2032
        6.1.Global Mobile Clinics Market Analysis by By Application: Introduction
        6.2.Market Size and Forecast by Region
        6.3.Risk Management
        6.4.Fraud Detection
        6.5.Trading and Portfolio Management
        6.6.Investment Research
        6.7.Other
      10.Global Mobile Clinics Market Value ((US$ Mn)), Share (%), and Growth Rate (%) Comparison by Region, 2017-2032
        10.1.North America
          10.1.1.North America Mobile Clinics Market: Regional Analysis, 2017-2032
            10.1.1.1.The US
            10.1.1.2.Canada
        10.2.1.Europe
          10.2.1.Europe Mobile Clinics Market: Regional Trend Analysis
            10.2.1.1.Germany
            10.2.1.2.France
            10.2.1.3.UK
            10.2.1.4.Russia
            10.2.1.5.Italy
            10.2.1.6.Spain
            10.2.1.7.Nordic
            10.2.1.8.Benelux
            10.2.1.9.Rest of Europe
        10.3.Asia-Pacific
          10.3.1.Asia-Pacific Mobile Clinics Market: Regional Analysis, 2017-2032
            10.3.1.1.China
            10.3.1.2.Japan
            10.3.1.3.South Korea
            10.3.1.4.India
            10.3.1.5.ANZ
            10.3.1.6.ASEAN
            10.3.1.7.Rest of Asia-Pacifc
        10.4.Latin America
          10.4.1.Latin America Mobile Clinics Market: Regional Analysis, 2017-2032
            10.4.1.1.Brazil
            10.4.1.2.Mexico
            10.4.1.3.Argentina
            10.4.1.4.Colombia
            10.4.1.5.Rest of Latin America
        10.5.Middle East and Africa
          10.5.1.Middle East and Africa Mobile Clinics Market: Regional Analysis, 2017-2032
            10.5.1.1.Saudi Arabia
            10.5.1.2.UAE
            10.5.1.3.South Africa
            10.5.1.4.Israel
            10.5.1.5.Egypt
            10.5.1.6.Turkey
            10.5.1.7.Rest of MEA
      11.Global Mobile Clinics Market Company Evaluation Matrix, Competitive Landscape, Market Share Analysis, and Company Profiles
        11.1.Market Share Analysis
        11.2.Company Profiles
          11.3.1.Company Overview
          11.3.2.Financial Highlights
          11.3.3.Product Portfolio
          11.3.4.SWOT Analysis
          11.3.5.Key Strategies and Developments
        11.4.Amazon Web Services Inc.
          11.4.1.Company Overview
          11.4.2.Financial Highlights
          11.4.3.Product Portfolio
          11.4.4.SWOT Analysis
          11.4.5.Key Strategies and Developments
        11.5.Cisco Systems Inc.
          11.5.1.Company Overview
          11.5.2.Financial Highlights
          11.5.3.Product Portfolio
          11.5.4.SWOT Analysis
          11.5.5.Key Strategies and Developments
        11.6.Microsoft Corporation
          11.6.1.Company Overview
          11.6.2.Financial Highlights
          11.6.3.Product Portfolio
          11.6.4.SWOT Analysis
          11.6.5.Key Strategies and Developments
        11.7.SAP SE
          11.7.1.Company Overview
          11.7.2.Financial Highlights
          11.7.3.Product Portfolio
          11.7.4.SWOT Analysis
          11.7.5.Key Strategies and Developments
        11.8.BigML Inc.
          11.8.1.Company Overview
          11.8.2.Financial Highlights
          11.8.3.Product Portfolio
          11.8.4.SWOT Analysis
          11.8.5.Key Strategies and Developments
        11.9.Fair Isaac Corporation
          11.9.1.Company Overview
          11.9.2.Financial Highlights
          11.9.3.Product Portfolio
          11.9.4.SWOT Analysis
          11.9.5.Key Strategies and Developments
        11.10.IBM Corporation
          11.10.1.Company Overview
          11.10.2.Financial Highlights
          11.10.3.Product Portfolio
          11.10.4.SWOT Analysis
          11.10.5.Key Strategies and Developments
        11.11.Google LLC
          11.11.1.Company Overview
          11.11.2.Financial Highlights
          11.11.3.Product Portfolio
          11.11.4.SWOT Analysis
          11.11.5.Key Strategies and Developments
        11.12.Accenture
          11.12.1.Company Overview
          11.12.2.Financial Highlights
          11.12.3.Product Portfolio
          11.12.4.SWOT Analysis
          11.12.5.Key Strategies and Developments
        11.13.Oracle
          11.13.1.Company Overview
          11.13.2.Financial Highlights
          11.13.3.Product Portfolio
          11.13.4.SWOT Analysis
          11.13.5.Key Strategies and Developments
        11.14.Other Key Players
          11.14.1.Company Overview
          11.14.2.Financial Highlights
          11.14.3.Product Portfolio
          11.14.4.SWOT Analysis
          11.14.5.Key Strategies and Developments
      12.Assumptions and Acronyms
      13.Research Methodology
      14.Contact
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