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
The AI in Finance market is projected to grow from USD 38.36 billion in 2024 to USD 190.33 billion by 2030, at a compound annual growth rate (CAGR) of 30.6% during the forecast period. Chatbots and virtual assistants are in demand in the AI-driven finance market due to the ability to automate customer service, enhance user experience, and reduce operational costs. The rising demand of AI-powered algorithms enhance risk identification and mitigation, fostering safer financial practices is shaping the AI in Finance market.
“By end user as business operation, Fintech segment registers the highest CAGR during the forecast period.”
Fintech companies are increasingly leveraging 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 are utilizing 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.
“By region, Asia Pacific to register the highest CAGR market during the forecast period.” Rapid digital transformation across economies and the rise of fintech startups are driving AI solutions in Asia Pacific. Countries like China and India are investing heavily in AI technologies to enhance financial services and improve customer experiences. The region's vast consumer base presents major opportunities of customized financial products and services. Regulatory bodies such as Monetary Authority of Singapore (MAS) and Cyberspace Administration of China (CAC) promote innovation and further boost market growth. The increasing focus on data-driven decision-making and the need for efficient risk management solutions also contribute to the rapid adoption of AI in finance, positioning Asia-Pacific as a leader in this sector.
Breakdown of primaries
In-depth interviews were conducted with Chief Executive Officers (CEOs), innovation and technology directors, system integrators, and executives from various key organizations operating in the AI in Finance market.
The study includes an in-depth competitive analysis of these key players in the AI in Finance market, including their company profiles, recent developments, and key market strategies.
Research Coverage
This research report categorizes the AI in Finance market by product type (ERP and financial services, chatbots and virtual assistants, automated reconciliation solutions, intelligent document processing, governance, risk and compliance (GRC) software, accounts payable/receivable automation software, robo-advisors, expense management systems, compliance automation platforms, algorithmic trading platforms, underwriting engines/platforms), by deployment mode (cloud and on-premises), by technology (generative AI, NLP and predictive analytics), by application (Business operation (fraud detection and prevention, risk management, customer service & engagement, financial compliance & regulatory reporting, investment & portfolio management) Business function (financial planning & forecasting, automated bookkeeping & reconciliation, procurement & supply chain finance, revenue cycle management), by End user (Enterprise as business function (government & public sectors, retail & ecommerce, real estate, manufacturing, telecom & media, healthcare & pharma, utilities, technology & software) Enterprise as business operation (banking, insurance, investment & asset management, fintech, accounting & auditing firms, capital markets/regtech, payments & cards/payment processing) and by region (North America, Europe, Asia Pacific, Middle East & Africa, and Latin America). The scope of the report covers detailed information regarding the major factors, such as drivers, restraints, challenges, and opportunities, influencing the growth of the AI in Finance market. A detailed analysis of the key industry players has been done to provide insights into their business overview, solutions and services, key strategies, Contracts, partnerships, and agreements. new product & service launches, mergers and acquisitions, and recent developments associated with the AI in Finance market. Competitive analysis of upcoming startups in the AI in Finance market ecosystem is covered in this report.
Key Benefits of Buying the Report
The report will help the market leaders/new entrants in this market with information on the closest approximations of the revenue numbers for the overall AI in Finance market and the subsegments. This report will help stakeholders understand the competitive landscape and gain more insights to position their businesses better and to plan suitable go-to-market strategies. The report also helps stakeholders understand the pulse of the market and provides them with information on key market drivers, restraints, challenges, and opportunities.
The report provides insights on the following pointers:
“By end user as business operation, Fintech segment registers the highest CAGR during the forecast period.”
Fintech companies are increasingly leveraging 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 are utilizing 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.
“By region, Asia Pacific to register the highest CAGR market during the forecast period.” Rapid digital transformation across economies and the rise of fintech startups are driving AI solutions in Asia Pacific. Countries like China and India are investing heavily in AI technologies to enhance financial services and improve customer experiences. The region's vast consumer base presents major opportunities of customized financial products and services. Regulatory bodies such as Monetary Authority of Singapore (MAS) and Cyberspace Administration of China (CAC) promote innovation and further boost market growth. The increasing focus on data-driven decision-making and the need for efficient risk management solutions also contribute to the rapid adoption of AI in finance, positioning Asia-Pacific as a leader in this sector.
Breakdown of primaries
In-depth interviews were conducted with Chief Executive Officers (CEOs), innovation and technology directors, system integrators, and executives from various key organizations operating in the AI in Finance market.
- By Company: Tier I: 35%, Tier II: 45%, and Tier III: 20%
- By Designation: C-Level: 35%, Director Level: 25%, and Others: 40%
- By Region: North America: 40%, Europe: 25%, Asia Pacific: 20%, Middle East & Africa: 10%, and Latin America: 5%.
The study includes an in-depth competitive analysis of these key players in the AI in Finance market, including their company profiles, recent developments, and key market strategies.
Research Coverage
This research report categorizes the AI in Finance market by product type (ERP and financial services, chatbots and virtual assistants, automated reconciliation solutions, intelligent document processing, governance, risk and compliance (GRC) software, accounts payable/receivable automation software, robo-advisors, expense management systems, compliance automation platforms, algorithmic trading platforms, underwriting engines/platforms), by deployment mode (cloud and on-premises), by technology (generative AI, NLP and predictive analytics), by application (Business operation (fraud detection and prevention, risk management, customer service & engagement, financial compliance & regulatory reporting, investment & portfolio management) Business function (financial planning & forecasting, automated bookkeeping & reconciliation, procurement & supply chain finance, revenue cycle management), by End user (Enterprise as business function (government & public sectors, retail & ecommerce, real estate, manufacturing, telecom & media, healthcare & pharma, utilities, technology & software) Enterprise as business operation (banking, insurance, investment & asset management, fintech, accounting & auditing firms, capital markets/regtech, payments & cards/payment processing) and by region (North America, Europe, Asia Pacific, Middle East & Africa, and Latin America). The scope of the report covers detailed information regarding the major factors, such as drivers, restraints, challenges, and opportunities, influencing the growth of the AI in Finance market. A detailed analysis of the key industry players has been done to provide insights into their business overview, solutions and services, key strategies, Contracts, partnerships, and agreements. new product & service launches, mergers and acquisitions, and recent developments associated with the AI in Finance market. Competitive analysis of upcoming startups in the AI in Finance market ecosystem is covered in this report.
Key Benefits of Buying the Report
The report will help the market leaders/new entrants in this market with information on the closest approximations of the revenue numbers for the overall AI in Finance market and the subsegments. This report will help stakeholders understand the competitive landscape and gain more insights to position their businesses better and to plan suitable go-to-market strategies. The report also helps stakeholders understand the pulse of the market and provides them with information on key market drivers, restraints, challenges, and opportunities.
The report provides insights on the following pointers:
- Analysis of key drivers (AI-powered algorithms enhance risk identification and mitigation, fostering safer financial practices, AI-driven chatbots and virtual assistants enhance customer service experiences, making financial advice more accessible, machine learning models provide accurate forecasts which help in strategic planning and investment decisions), restraints (the possibility of bias and issues related to the ethical use of data), opportunities (rise in demand for hyper-personalization of financial products and tailoring services to individual customer needs and preferences for long-term engagement), and challenges (Safeguarding data to prevent breaches and regulatory violations) influencing the growth of the AI in Finance market.
- Product Development/Innovation: Detailed insights on upcoming technologies, research & development activities, and new product & service launches in the AI in Finance market
- Market Development: Comprehensive information about lucrative markets – the report analyses the AI in Finance market across varied regions.
- Market Diversification: Exhaustive information about new products & services, untapped geographies, recent developments, and investments in the AI in Finance market
- Competitive Assessment: In-depth assessment of market shares, growth strategies and service offerings of leading players FIS (US), Fiserv (US), Google (US), Microsoft (US), Zoho (India), IBM (US), Socure (US), Workiva (US), Plaid (US), SAS (US), C3 AI (US) among others in AI in Finance market.
1 INTRODUCTION
1.1 STUDY OBJECTIVES
1.2 MARKET DEFINITION
1.2.1 INCLUSIONS AND EXCLUSIONS
1.3 MARKET SCOPE
1.3.1 MARKET SEGMENTATION
1.3.2 YEARS CONSIDERED
1.4 CURRENCY CONSIDERED
1.5 STAKEHOLDERS
2 RESEARCH METHODOLOGY
2.1 RESEARCH DATA
2.1.1 SECONDARY DATA
2.1.2 PRIMARY DATA
2.1.2.1 Breakup of primary profiles
2.1.2.2 Key industry insights
2.2 DATA TRIANGULATION
2.3 MARKET SIZE ESTIMATION
2.3.1 TOP-DOWN APPROACH
2.3.2 BOTTOM-UP APPROACH
2.4 MARKET FORECAST
2.5 RESEARCH ASSUMPTIONS
2.6 RISK ASSESSMENT
2.7 RESEARCH LIMITATIONS
3 EXECUTIVE SUMMARY
4 PREMIUM INSIGHTS
4.1 ATTRACTIVE OPPORTUNITIES FOR PLAYERS IN AI IN FINANCE MARKET
4.2 AI IN FINANCE MARKET: TOP THREE APPLICATIONS
4.3 NORTH AMERICA: AI IN FINANCE MARKET, BY DEPLOYMENT MODE AND END USER
4.4 AI IN FINANCE MARKET, BY REGION
5 MARKET OVERVIEW AND INDUSTRY TRENDS
5.1 INTRODUCTION
5.2 MARKET DYNAMICS
5.2.1 DRIVERS
5.2.1.1 Increasing demand for precise forecasts for strategic planning and investment
5.2.1.2 Growing adoption of AI algorithms to enhance risk detection and mitigation
5.2.1.3 Rising popularity of personalized financial services
5.2.2 RESTRAINTS
5.2.2.1 Concerns regarding bias and ethical data use
5.2.3 OPPORTUNITIES
5.2.3.1 Growing need for hyper-personalized financial products for long-term customer engagement and tailored services
5.2.3.2 Rising demand for accurate credit scoring and better risk management
5.2.4 CHALLENGES
5.2.4.1 Ensuring data security to prevent breaches or violations
5.2.4.2 AI model complexity in finance
5.3 EVOLUTION OF AI IN FINANCE MARKET
5.4 SUPPLY CHAIN ANALYSIS
5.5 ECOSYSTEM ANALYSIS
5.5.1 FRAUD DETECTION & PREVENTION PROVIDERS
5.5.2 RISK MANAGEMENT PROVIDERS
5.5.3 CUSTOMER SERVICE & ENGAGEMENT PROVIDERS
5.5.4 FINANCIAL COMPLIANCE & REGULATORY REPORTING PROVIDERS
5.5.5 INVESTMENT & PORTFOLIO MANAGEMENT PROVIDERS
5.5.6 END USERS
5.6 CASE STUDY ANALYSIS
5.6.1 PAYPAL ENHANCES FRAUD DETECTION CAPABILITIES WITH H2O.AI'S DRIVERLESS AI SOLUTION
5.6.2 VENA SOLUTIONS TRANSFORMING FINANCIAL REPORTING AND PLANNING AT SHIFT4 PAYMENTS
5.6.3 INVESTA ENHANCES FUND REPORTING EFFICIENCY WITH WORKIVA’S STREAMLINED SOLUTIONS
5.6.4 DATAVISOR AND MICROSOFT AZURE COLLABORATE TO ENHANCE REAL-TIME FRAUD DETECTION
5.6.5 ZOHO EMPOWERS PLENTI WITH UNIFIED CRM SOLUTION TO ENHANCE CUSTOMER ENGAGEMENT AND OPERATIONAL EFFICIENCY
5.7 TECHNOLOGY ANALYSIS
5.7.1 KEY TECHNOLOGIES
5.7.1.1 NLP & deep learning
5.7.1.2 Computer vision
5.7.1.3 Predictive analytics
5.7.1.4 Robotic process automation (RPA)
5.7.1.5 Reinforcement learning
5.7.1.6 Explainable AI (XAI)
5.7.1.7 Anomaly detection
5.7.2 ADJACENT TECHNOLOGIES
5.7.2.1 Cybersecurity
5.7.2.2 IoT
5.7.2.3 AR/VR
5.7.2.4 Digital identity verification
5.7.3 COMPLEMENTARY TECHNOLOGIES
5.7.3.1 Cloud computing
5.7.3.2 Edge computing
5.7.3.3 Quantum computing
5.7.3.4 Big data analytics
5.7.3.5 Blockchain
5.8 KEY CONFERENCES AND EVENTS, 2024–2025
5.9 INVESTMENT AND FUNDING SCENARIO
5.10 REGULATORY LANDSCAPE
5.10.1 REGULATORY BODIES, GOVERNMENT AGENCIES, FRAMEWORKS, AND OTHER ORGANIZATIONS
5.10.2 REGULATORY LANDSCAPE, BY REGION
5.10.2.1 North America
5.10.2.1.1 US
5.10.2.1.2 Canada
5.10.2.2 Europe
5.10.2.2.1 EU
5.10.2.2.2 UK
5.10.2.3 Asia Pacific
5.10.2.3.1 Singapore
5.10.2.3.2 Hong Kong
5.10.2.3.3 China
5.10.2.3.4 South Korea
5.10.2.3.5 Taiwan
5.10.2.4 Middle East & Africa
5.10.2.4.1 UAE
5.10.2.4.2 South Africa
5.10.2.4.3 Israel
5.10.2.4.4 Saudi Arabia
5.10.2.5 Latin America
5.10.2.5.1 Brazil
5.10.2.5.2 Mexico
5.10.2.5.3 Chile
5.11 PATENT ANALYSIS
5.11.1 METHODOLOGY
5.11.2 PATENTS FILED, BY DOCUMENT TYPE
5.11.3 INNOVATIONS AND PATENT APPLICATIONS
5.12 PRICING ANALYSIS
5.12.1 AVERAGE SELLING PRICE TREND OF KEY PLAYERS, BY APPLICATION
5.12.2 INDICATIVE PRICING ANALYSIS, BY PRODUCT TYPE
5.13 PORTER’S FIVE FORCES ANALYSIS
5.13.1 THREAT OF NEW ENTRANTS
5.13.2 THREAT OF SUBSTITUTES
5.13.3 BARGAINING POWER OF SUPPLIERS
5.13.4 BARGAINING POWER OF BUYERS
5.13.5 INTENSITY OF COMPETITIVE RIVALRY
5.14 TRENDS/DISRUPTIONS IMPACTING CUSTOMER BUSINESS
5.15 KEY STAKEHOLDERS AND BUYING CRITERIA
5.15.1 KEY STAKEHOLDERS IN BUYING PROCESS
5.15.2 BUYING CRITERIA
5.16 IMPACT OF GENERATIVE AI ON AI IN FINANCE MARKET
5.16.1 TOP USE CASES & MARKET POTENTIAL
5.16.1.1 Key use cases
5.16.2 AUTOMATED FINANCIAL REPORTING
5.16.3 ENHANCED RISK MANAGEMENT
5.16.4 PERSONALIZED FINANCIAL SERVICES
5.16.5 STREAMLINED CUSTOMER INTERACTIONS
5.16.6 FRAUD DETECTION AND COMPLIANCE
5.16.7 INNOVATIVE FINANCIAL PLANNING
6 AI IN FINANCE MARKET, BY PRODUCT
6.1 INTRODUCTION
6.1.1 PRODUCT: AI IN FINANCE MARKET DRIVERS
6.2 TYPE
6.2.1 ERP AND FINANCIAL SYSTEMS
6.2.1.1 Real-time analytics and automated reporting for improved financial management
6.2.2 CHATBOTS & VIRTUAL ASSISTANTS
6.2.2.1 Enhancing operational efficiency and customer engagement in financial services
6.2.3 AUTOMATED RECONCILIATION SOLUTIONS
6.2.3.1 Boosting operational agility for swift transaction processing
6.2.4 INTELLIGENT DOCUMENT PROCESSING
6.2.4.1 Reducing manual errors, enabling quick decision-making, and accelerating processing time
6.2.5 GOVERNANCE, RISK, AND COMPLIANCE (GRC) SOFTWARE
6.2.5.1 Facilitating seamless collaboration across departments
6.2.6 ACCOUNTS PAYABLE/RECEIVABLE AUTOMATION SOFTWARE
6.2.6.1 Providing real-time insights for informed financial decisions
6.2.7 ROBO-ADVISORS
6.2.7.1 Providing automated investment management and financial advisory services
6.2.8 EXPENSE MANAGEMENT SYSTEMS
6.2.8.1 Streamlining financial operations and controlling costs
6.2.9 COMPLIANCE AUTOMATION PLATFORMS
6.2.9.1 Identifying compliance risks and enabling real-time alerts
6.2.10 ALGORITHMIC TRADING PLATFORMS
6.2.10.1 Automating trade execution and responding to market fluctuations
6.2.11 UNDERWRITING ENGINES/PLATFORMS
6.2.11.1 Expediting loan approvals and promoting fair lending
6.2.12 OTHER PRODUCT TYPES
6.3 DEPLOYMENT MODE
6.3.1 CLOUD
6.3.1.1 Cloud deployment offers scalability, flexibility, and cost-efficiency
6.3.2 ON-PREMISES
6.3.2.1 On-premises deployment provides fast data processing and real-time analytics
7 AI IN FINANCE MARKET, BY TECHNOLOGY
7.1 INTRODUCTION
7.1.1 TECHNOLOGY: AI IN FINANCE MARKET DRIVERS
7.2 GENERATIVE AI
7.2.1 ENHANCES CUSTOMER ENGAGEMENT AND PROCESS AUTOMATION IN FINANCE
7.3 OTHER AI TECHNOLOGIES
7.3.1 NLP
7.3.1.1 NLP boosts data analysis, automates interactions, and enhances compliance
7.3.2 PREDICTIVE ANALYTICS
7.3.2.1 AI-driven predictive analytics enables accurate forecasting
8 AI IN FINANCE MARKET, BY APPLICATION
8.1 INTRODUCTION
8.1.1 APPLICATION: AI IN FINANCE MARKET DRIVERS
8.2 FINANCE AS BUSINESS OPERATIONS
8.2.1 FRAUD DETECTION & PREVENTION
8.2.1.1 AI-driven fraud detection enhances security and reduces financial losses
8.2.1.2 Real-time transaction monitoring
8.2.1.3 Customer data security
8.2.1.4 Customer behavior analysis
8.2.1.5 Trend analysis
8.2.1.6 Others
8.2.2 RISK MANAGEMENT
8.2.2.1 AI-driven risk management enhances decision-making in finance
8.2.2.2 Credit risk scoring
8.2.2.3 Market volatility prediction
8.2.2.4 Stress testing
8.2.2.5 Others
8.2.3 CUSTOMER SERVICE & ENGAGEMENT
8.2.3.1 Customer service and engagement enhance personalization, leading to improved client satisfaction
8.2.3.2 Chatbots/Virtual assistants for customer support
8.2.3.3 Personalized financial product recommendations
8.2.3.4 Market segmentation
8.2.3.5 Personalized marketing messaging
8.2.3.6 New customer acquisition
8.2.3.7 Data-driven decision making
8.2.3.8 Customer retention management
8.2.3.9 Others
8.2.4 FINANCIAL COMPLIANCE & REGULATORY REPORTING
8.2.4.1 Financial compliance streamlines accuracy and efficiency in meeting standards
8.2.4.2 Risk & compliance management
8.2.4.3 Audit & reporting
8.2.4.4 Others
8.2.5 INVESTMENT & PORTFOLIO MANAGEMENT
8.2.5.1 AI optimizes investment and portfolio management for smarter decision-making and improved returns
8.2.5.2 Robo-advisors for wealth management
8.2.5.3 Portfolio rebalancing
8.2.5.4 Others
8.3 FINANCE AS BUSINESS FUNCTIONS
8.3.1 FINANCIAL PLANNING & FORECASTING
8.3.1.1 Financial planning enhances accuracy and decision-making in finance
8.3.1.2 Demand forecasting (CAPEX/OPEX)
8.3.1.3 Cash flow forecasting
8.3.1.4 Budgeting & expense management
8.3.1.5 Scenario planning
8.3.1.6 Others
8.3.2 AUTOMATED BOOKKEEPING & RECONCILIATION
8.3.2.1 Automated bookkeeping and reconciliation streamline financial processes and enhance accuracy
8.3.2.2 Real-time ledger matching
8.3.2.3 Invoice processing
8.3.2.4 Variance detection
8.3.2.5 Others
8.3.3 PROCUREMENT & SUPPLY CHAIN FINANCE
8.3.3.1 AI optimizes supply chain management by boosting efficiency and reducing costs
8.3.3.2 Invoice discounting
8.3.3.3 Supplier risk scoring
8.3.3.4 Dynamic payments
8.3.3.5 Payment automation
8.3.3.6 Others
8.3.4 REVENUE CYCLE MANAGEMENT
8.3.4.1 Revenue cycle management automates processes and improves cash flow through enhanced accuracy in billing
8.3.4.2 Payment optimization
8.3.4.3 Subscription billing management
8.3.4.4 Invoice settlements/Automated invoice processing
8.3.4.5 Churn management
8.3.4.6 Others
9 AI IN FINANCE MARKET, BY END USER
9.1 INTRODUCTION
9.1.1 END USER: AI IN FINANCE MARKET DRIVERS
9.2 END USER
9.2.1 FINANCE AS BUSINESS FUNCTIONS
9.2.1.1 Government & public sector
9.2.1.1.1 Strengthening governance and trust in AI in finance
9.2.1.2 Retail & e-commerce
9.2.1.2.1 Driving sales and satisfaction with AI-enhanced retail
9.2.1.3 Real estate
9.2.1.3.1 Revolutionizing real estate with AI-driven finance solutions
9.2.1.4 Manufacturing
9.2.1.4.1 Transforming financial processes of manufacturing sector for enhanced efficiency and growth
9.2.1.5 Telecom & media
9.2.1.5.1 Leveraging AI in telecom & media for optimized network management and enhanced service quality
9.2.1.6 Healthcare & pharma
9.2.1.6.1 AI provides enhanced and patient-centric solutions in finance
9.2.1.7 Utilities
9.2.1.7.1 AI transforms utilities sector by enhancing operational efficiency and improving predictive maintenance
9.2.1.8 Education
9.2.1.8.1 Harnessing AI to transform education finance by streamlining operations and enhancing financial literacy
9.2.1.9 Technology & software
9.2.1.9.1 Technology and software enable automation and improve decision-making processes
9.2.1.10 Other end users
9.3 FINANCE AS BUSINESS OPERATIONS
9.3.1 BANKING
9.3.1.1 AI enables better risk management and improves fraud detection
9.3.1.2 Corporate & commercial banking
9.3.1.3 Retail banking
9.3.1.4 Investment banking
9.3.2 INSURANCE
9.3.2.1 AI automates claim processing, reduces fraud, and personalizes policies
9.3.3 INVESTMENT & ASSET MANAGEMENT
9.3.3.1 AI enhances decision-making and optimizes portfolio management
9.3.3.2 Hedge funds
9.3.3.3 Private equity
9.3.3.4 Wealth management
9.3.4 FINTECH
9.3.4.1 AI in fintech automates tasks, improves data analysis, and provides real-time insights
9.3.4.2 Blockchain & cryptocurrency providers
9.3.4.3 Lending platform providers/specialty lenders
9.3.5 CAPITAL MARKETS/REGTECH
9.3.5.1 AI increases efficiency and reduces operational costs in capital markets
10 AI IN FINANCE MARKET, BY REGION
10.1 INTRODUCTION
10.2 NORTH AMERICA
10.2.1 NORTH AMERICA: AI IN FINANCE MARKET DRIVERS
10.2.2 NORTH AMERICA: MACROECONOMIC IMPACT
10.2.3 US
10.2.3.1 Transforming brands with AI-driven personalization in social media
10.2.4 CANADA
10.2.4.1 Accelerating AI adoption in finance through automation and digital transformation
10.3 EUROPE
10.3.1 EUROPE: AI IN FINANCE MARKET DRIVERS
10.3.2 EUROPE: MACROECONOMIC IMPACT
10.3.3 UK
10.3.3.1 Leveraging automation and data analytics for enhanced decision-making and compliance
10.3.4 GERMANY
10.3.4.1 Focus on automation in risk management and personalized banking services to improve operational efficiency
10.3.5 FRANCE
10.3.5.1 Robust government initiatives promote innovation and establish frameworks encouraging AI technology adoption
10.3.6 ITALY
10.3.6.1 Promotion of digital transformation and rising investment in AI technologies across financial institutions
10.3.7 SPAIN
10.3.7.1 Increased funding and strategic partnerships to enhance AI collaboration in financial services
10.3.8 REST OF EUROPE
10.4 ASIA PACIFIC
10.4.1 ASIA PACIFIC: AI IN FINANCE MARKET DRIVERS
10.4.2 ASIA PACIFIC: MACROECONOMIC IMPACT
10.4.3 CHINA
10.4.3.1 Increasing focus on AI innovation for operational efficiency in financial sector to boost market
10.4.4 JAPAN
10.4.4.1 Partnerships between financial institutions and tech firms accelerate AI integration for improved financial solutions
10.4.5 INDIA
10.4.5.1 Increasing adoption of AI-powered solutions by financial institutions for risk management to drive market
10.4.6 SOUTH KOREA
10.4.6.1 Government support enhances financial services and boosts competitiveness in fintech sector
10.4.7 AUSTRALIA & NEW ZEALAND
10.4.7.1 Increasing adoption of AI by growing fintech companies to drive market
10.4.8 ASEAN
10.4.8.1 Increasing digitalization of banking services to drive market
10.4.9 REST OF ASIA PACIFIC
10.5 MIDDLE EAST & AFRICA
10.5.1 MIDDLE EAST & AFRICA: AI IN FINANCE MARKET DRIVERS
10.5.2 MIDDLE EAST & AFRICA: MACROECONOMIC IMPACT
10.5.3 MIDDLE EAST
10.5.3.1 KSA
10.5.3.1.1 Government Vision 2030 initiative promoting digital transformation and AI adoption in financial services to drive market
10.5.3.2 UAE
10.5.3.2.1 Increased investments in AI-powered financial technologies to drive market
10.5.3.3 Kuwait
10.5.3.3.1 Growing focus on digital transformation to fuel AI adoption in finance market
10.5.3.4 Bahrain
10.5.3.4.1 Increasing adoption of AI technologies in banking sector to drive market
10.5.4 AFRICA
10.5.4.1 Increasing adoption of AI to enhance financial services to drive market
10.6 LATIN AMERICA
10.6.1 LATIN AMERICA: AI IN FINANCE MARKET DRIVERS
10.6.2 LATIN AMERICA: MACROECONOMIC IMPACT
10.6.3 BRAZIL
10.6.3.1 Government support and investments in AI to drive market
10.6.4 MEXICO
10.6.4.1 Increased investment in fintech to drive AI adoption in finance market
10.6.5 ARGENTINA
10.6.5.1 Fintech expansion and innovation to propel market growth
10.6.6 REST OF LATIN AMERICA
11 COMPETITIVE LANDSCAPE
11.1 OVERVIEW
11.2 KEY PLAYER STRATEGIES/RIGHT TO WIN, 2020–2024
11.3 REVENUE ANALYSIS, 2019–2023
11.4 MARKET SHARE ANALYSIS, 2023
11.4.1 MARKET SHARE ANALYSIS OF KEY PLAYERS (FINANCE AS BUSINESS FUNCTIONS)
11.4.2 MARKET RANKING ANALYSIS (FINANCE AS BUSINESS FUNCTIONS)
11.4.3 MARKET SHARE ANALYSIS OF KEY PLAYERS (FINANCE AS BUSINESS OPERATIONS)
11.4.4 MARKET RANKING ANALYSIS (FINANCE AS BUSINESS OPERATIONS)
11.5 PRODUCT COMPARISON
11.5.1 PRODUCT COMPARATIVE ANALYSIS, BY RISK ASSESSMENT
11.5.1.1 ZAML (Zest Automated Machine Learning) (Zest AI)
11.5.1.2 Kensho Risk (Kensho)
11.5.1.3 C3 AI Risk Management (C3 AI)
11.5.1.4 Finacle Treasury and Risk Management Solution (Infosys)
11.5.2 PRODUCT COMPARATIVE ANALYSIS, BY FRAUD DETECTION & PREVENTION
11.5.2.1 Socure ID+ (Socure)
11.5.2.2 Dataminr Real-Time Risk Detection (Dataminr)
11.5.2.3 Google Cloud (Google)
11.5.2.4 Vectra Cognito (Vectra AI)
11.5.3 PRODUCT COMPARATIVE ANALYSIS, BY CHATBOTS & PERSONAL ASSISTANTS
11.5.3.1 AlphaSense Search Chatbot (AlphaSense)
11.5.3.2 Oracle Digital Assistant (Oracle)
11.5.3.3 Watson Assistant (IBM)
11.6 COMPANY VALUATION AND FINANCIAL METRICS OF KEY VENDORS
11.7 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2023
11.7.1 COMPANY EVALUATION MATRIX: KEY PLAYERS
(FINANCE AS BUSINESS FUNCTIONS)
11.7.1.1 Stars
11.7.1.2 Emerging Leaders
11.7.1.3 Pervasive Players
11.7.1.4 Participants
11.7.2 COMPANY EVALUATION MATRIX: KEY PLAYERS
(FINANCE AS BUSINESS OPERATIONS)
11.7.2.1 Stars
11.7.2.2 Emerging Leaders
11.7.2.3 Pervasive Players
11.7.2.4 Participants
11.7.3 COMPANY FOOTPRINT: KEY PLAYERS
11.7.3.1 Company footprint
11.7.3.2 Region footprint
11.7.3.3 Product footprint
11.7.3.4 Application footprint
11.7.3.5 End user footprint
11.8 COMPANY EVALUATION MATRIX: STARTUPS/SMES, 2023
11.8.1 COMPANY EVALUATION MATRIX: STARTUPS/SMES
(FINANCE AS BUSINESS OPERATIONS)
11.8.1.1 Progressive companies
11.8.1.2 Responsive companies
11.8.1.3 Dynamic companies
11.8.1.4 Starting blocks
11.8.2 COMPANY EVALUATION MATRIX: STARTUPS/SMES
(FINANCE AS BUSINESS FUNCTIONS)
11.8.2.1 Progressive companies
11.8.2.2 Responsive companies
11.8.2.3 Dynamic companies
11.8.2.4 Starting blocks
11.8.3 COMPETITIVE BENCHMARKING: STARTUPS/SMES, 2023
11.8.3.1 Detailed list of key startups/SMEs
11.8.3.2 Competitive benchmarking of key startups/SMEs
11.9 COMPETITIVE SCENARIO
11.9.1 PRODUCT LAUNCHES AND ENHANCEMENTS
11.9.2 DEALS
12 COMPANY PROFILES
12.1 INTRODUCTION
12.2 KEY PLAYERS
12.2.1 FIS
12.2.1.1 Business overview
12.2.1.2 Products/Solutions/Services offered
12.2.1.3 Recent developments
12.2.1.3.1 Product launches and enhancements
12.2.1.3.2 Deals
12.2.1.4 MnM view
12.2.1.4.1 Key strengths
12.2.1.4.2 Strategic choices
12.2.1.4.3 Weaknesses and competitive threats
12.2.2 FISERV
12.2.2.1 Business overview
12.2.2.2 Products/Solutions/Services offered
12.2.2.3 Recent developments
12.2.2.3.1 Product launches and enhancements
12.2.2.3.2 Deals
12.2.2.4 MnM view
12.2.2.4.1 Key strengths
12.2.2.4.2 Strategic choices
12.2.2.4.3 Weaknesses and competitive threats
12.2.3 GOOGLE
12.2.3.1 Business overview
12.2.3.2 Products/Solutions/Services offered
12.2.3.3 Recent developments
12.2.3.3.1 Product launches and enhancements
12.2.3.3.2 Deals
12.2.3.4 MnM view
12.2.3.4.1 Key strengths
12.2.3.4.2 Strategic choices
12.2.3.4.3 Weaknesses and competitive threats
12.2.4 MICROSOFT
12.2.4.1 Business overview
12.2.4.2 Products/Solutions/Services offered
12.2.4.3 Recent developments
12.2.4.3.1 Product launches and enhancements
12.2.4.3.2 Deals
12.2.4.4 MnM view
12.2.4.4.1 Key strengths
12.2.4.4.2 Strategic choices
12.2.4.4.3 Weaknesses and competitive threats
12.2.5 ZOHO
12.2.5.1 Business overview
12.2.5.2 Products/Solutions/Services offered
12.2.5.3 Recent developments
12.2.5.3.1 Product launches and enhancements
12.2.5.3.2 Deals
12.2.5.4 MnM view
12.2.5.4.1 Key strengths
12.2.5.4.2 Strategic choices
12.2.5.4.3 Weaknesses and competitive threats
12.2.6 IBM
12.2.6.1 Business overview
12.2.6.2 Products/Solutions/Services offered
12.2.6.3 Recent developments
12.2.6.3.1 Product launches and enhancements
12.2.6.3.2 Deals
12.2.7 SOCURE
12.2.7.1 Business overview
12.2.7.2 Products/Solutions/Services offered
12.2.7.3 Recent developments
12.2.7.3.1 Deals
12.2.7.3.2 Expansions
12.2.8 WORKIVA
12.2.8.1 Business overview
12.2.8.2 Products/Solutions/Services offered
12.2.8.3 Recent developments
12.2.8.3.1 Deals
12.2.8.3.2 Expansions
12.2.9 PLAID
12.2.9.1 Business overview
12.2.9.2 Products/Solutions/Services offered
12.2.9.3 Recent developments
12.2.9.3.1 Deals
12.2.10 C3 AI
12.2.10.1 Business overview
12.2.10.2 Products/Solutions/Services offered
12.2.10.3 Recent developments
12.2.10.3.1 Deals
12.2.11 HIGHRADIUS
12.2.11.1 Business overview
12.2.11.2 Products/Solutions/Services offered
12.2.11.3 Recent developments
12.2.11.3.1 Product launches and enhancements
12.2.11.3.2 Deals
12.2.12 SAP
12.2.13 AWS
12.2.14 HPE
12.2.15 ORACLE
12.2.16 SALESFORCE
12.2.17 INTEL
12.2.18 NVIDIA
12.2.19 NETAPP
12.2.20 DATAROBOT
12.2.21 ENOVA INTERNATIONAL
12.2.22 ALPHASENSE
12.2.23 OCROLUS
12.2.24 VECTRA AI
12.2.25 TERADATA
12.2.26 PEGA
12.2.27 VENA SOLUTIONS
12.2.28 AFFIRM
12.2.29 SYMPHONYAI
12.2.30 ENVESTNET | YODLEE
12.3 STARTUPS/SMES
12.3.1 ADDEPTO
12.3.2 DEEPER INSIGHTS
12.3.3 H2O.AI
12.3.4 APP0
12.3.5 UNDERWRITE.AI
12.3.6 DEEPGRAM
12.3.7 EMAGIA
12.3.8 INDATA LABS
12.3.9 ZEST AI
12.3.10 SCIENAPTIC AI
12.3.11 GRADIENT AI
12.3.12 KASISTO
12.3.13 TRUMID
12.3.14 DATAVISOR
12.3.15 KAVOUT
12.3.16 WEALTHBLOCK
13 ADJACENT AND RELATED MARKETS
13.1 INTRODUCTION
13.2 ARTIFICIAL INTELLIGENCE (AI) MARKET – GLOBAL FORECAST TO 2030
13.2.1 MARKET DEFINITION
13.2.2 MARKET OVERVIEW
13.2.2.1 Artificial intelligence market, by offering
13.2.2.2 Artificial intelligence market, by business function
13.2.2.3 Artificial intelligence market, by technology
13.2.2.4 Artificial intelligence market, by vertical
13.2.2.5 Artificial intelligence market, by region
13.3 NLP IN FINANCE MARKET – GLOBAL FORECAST TO 2028
13.3.1 MARKET DEFINITION
13.3.2 MARKET OVERVIEW
13.3.2.1 NLP in finance market, by offering
13.3.2.2 NLP in finance market, by application
13.3.2.3 NLP in finance market, by technology
13.3.2.4 NLP in finance market, by vertical
13.3.2.5 NLP in finance market, by region
14 APPENDIX
14.1 DISCUSSION GUIDE
14.2 KNOWLEDGESTORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL
14.3 CUSTOMIZATION OPTIONS
14.4 RELATED REPORTS
14.5 AUTHOR DETAILS
1.1 STUDY OBJECTIVES
1.2 MARKET DEFINITION
1.2.1 INCLUSIONS AND EXCLUSIONS
1.3 MARKET SCOPE
1.3.1 MARKET SEGMENTATION
1.3.2 YEARS CONSIDERED
1.4 CURRENCY CONSIDERED
1.5 STAKEHOLDERS
2 RESEARCH METHODOLOGY
2.1 RESEARCH DATA
2.1.1 SECONDARY DATA
2.1.2 PRIMARY DATA
2.1.2.1 Breakup of primary profiles
2.1.2.2 Key industry insights
2.2 DATA TRIANGULATION
2.3 MARKET SIZE ESTIMATION
2.3.1 TOP-DOWN APPROACH
2.3.2 BOTTOM-UP APPROACH
2.4 MARKET FORECAST
2.5 RESEARCH ASSUMPTIONS
2.6 RISK ASSESSMENT
2.7 RESEARCH LIMITATIONS
3 EXECUTIVE SUMMARY
4 PREMIUM INSIGHTS
4.1 ATTRACTIVE OPPORTUNITIES FOR PLAYERS IN AI IN FINANCE MARKET
4.2 AI IN FINANCE MARKET: TOP THREE APPLICATIONS
4.3 NORTH AMERICA: AI IN FINANCE MARKET, BY DEPLOYMENT MODE AND END USER
4.4 AI IN FINANCE MARKET, BY REGION
5 MARKET OVERVIEW AND INDUSTRY TRENDS
5.1 INTRODUCTION
5.2 MARKET DYNAMICS
5.2.1 DRIVERS
5.2.1.1 Increasing demand for precise forecasts for strategic planning and investment
5.2.1.2 Growing adoption of AI algorithms to enhance risk detection and mitigation
5.2.1.3 Rising popularity of personalized financial services
5.2.2 RESTRAINTS
5.2.2.1 Concerns regarding bias and ethical data use
5.2.3 OPPORTUNITIES
5.2.3.1 Growing need for hyper-personalized financial products for long-term customer engagement and tailored services
5.2.3.2 Rising demand for accurate credit scoring and better risk management
5.2.4 CHALLENGES
5.2.4.1 Ensuring data security to prevent breaches or violations
5.2.4.2 AI model complexity in finance
5.3 EVOLUTION OF AI IN FINANCE MARKET
5.4 SUPPLY CHAIN ANALYSIS
5.5 ECOSYSTEM ANALYSIS
5.5.1 FRAUD DETECTION & PREVENTION PROVIDERS
5.5.2 RISK MANAGEMENT PROVIDERS
5.5.3 CUSTOMER SERVICE & ENGAGEMENT PROVIDERS
5.5.4 FINANCIAL COMPLIANCE & REGULATORY REPORTING PROVIDERS
5.5.5 INVESTMENT & PORTFOLIO MANAGEMENT PROVIDERS
5.5.6 END USERS
5.6 CASE STUDY ANALYSIS
5.6.1 PAYPAL ENHANCES FRAUD DETECTION CAPABILITIES WITH H2O.AI'S DRIVERLESS AI SOLUTION
5.6.2 VENA SOLUTIONS TRANSFORMING FINANCIAL REPORTING AND PLANNING AT SHIFT4 PAYMENTS
5.6.3 INVESTA ENHANCES FUND REPORTING EFFICIENCY WITH WORKIVA’S STREAMLINED SOLUTIONS
5.6.4 DATAVISOR AND MICROSOFT AZURE COLLABORATE TO ENHANCE REAL-TIME FRAUD DETECTION
5.6.5 ZOHO EMPOWERS PLENTI WITH UNIFIED CRM SOLUTION TO ENHANCE CUSTOMER ENGAGEMENT AND OPERATIONAL EFFICIENCY
5.7 TECHNOLOGY ANALYSIS
5.7.1 KEY TECHNOLOGIES
5.7.1.1 NLP & deep learning
5.7.1.2 Computer vision
5.7.1.3 Predictive analytics
5.7.1.4 Robotic process automation (RPA)
5.7.1.5 Reinforcement learning
5.7.1.6 Explainable AI (XAI)
5.7.1.7 Anomaly detection
5.7.2 ADJACENT TECHNOLOGIES
5.7.2.1 Cybersecurity
5.7.2.2 IoT
5.7.2.3 AR/VR
5.7.2.4 Digital identity verification
5.7.3 COMPLEMENTARY TECHNOLOGIES
5.7.3.1 Cloud computing
5.7.3.2 Edge computing
5.7.3.3 Quantum computing
5.7.3.4 Big data analytics
5.7.3.5 Blockchain
5.8 KEY CONFERENCES AND EVENTS, 2024–2025
5.9 INVESTMENT AND FUNDING SCENARIO
5.10 REGULATORY LANDSCAPE
5.10.1 REGULATORY BODIES, GOVERNMENT AGENCIES, FRAMEWORKS, AND OTHER ORGANIZATIONS
5.10.2 REGULATORY LANDSCAPE, BY REGION
5.10.2.1 North America
5.10.2.1.1 US
5.10.2.1.2 Canada
5.10.2.2 Europe
5.10.2.2.1 EU
5.10.2.2.2 UK
5.10.2.3 Asia Pacific
5.10.2.3.1 Singapore
5.10.2.3.2 Hong Kong
5.10.2.3.3 China
5.10.2.3.4 South Korea
5.10.2.3.5 Taiwan
5.10.2.4 Middle East & Africa
5.10.2.4.1 UAE
5.10.2.4.2 South Africa
5.10.2.4.3 Israel
5.10.2.4.4 Saudi Arabia
5.10.2.5 Latin America
5.10.2.5.1 Brazil
5.10.2.5.2 Mexico
5.10.2.5.3 Chile
5.11 PATENT ANALYSIS
5.11.1 METHODOLOGY
5.11.2 PATENTS FILED, BY DOCUMENT TYPE
5.11.3 INNOVATIONS AND PATENT APPLICATIONS
5.12 PRICING ANALYSIS
5.12.1 AVERAGE SELLING PRICE TREND OF KEY PLAYERS, BY APPLICATION
5.12.2 INDICATIVE PRICING ANALYSIS, BY PRODUCT TYPE
5.13 PORTER’S FIVE FORCES ANALYSIS
5.13.1 THREAT OF NEW ENTRANTS
5.13.2 THREAT OF SUBSTITUTES
5.13.3 BARGAINING POWER OF SUPPLIERS
5.13.4 BARGAINING POWER OF BUYERS
5.13.5 INTENSITY OF COMPETITIVE RIVALRY
5.14 TRENDS/DISRUPTIONS IMPACTING CUSTOMER BUSINESS
5.15 KEY STAKEHOLDERS AND BUYING CRITERIA
5.15.1 KEY STAKEHOLDERS IN BUYING PROCESS
5.15.2 BUYING CRITERIA
5.16 IMPACT OF GENERATIVE AI ON AI IN FINANCE MARKET
5.16.1 TOP USE CASES & MARKET POTENTIAL
5.16.1.1 Key use cases
5.16.2 AUTOMATED FINANCIAL REPORTING
5.16.3 ENHANCED RISK MANAGEMENT
5.16.4 PERSONALIZED FINANCIAL SERVICES
5.16.5 STREAMLINED CUSTOMER INTERACTIONS
5.16.6 FRAUD DETECTION AND COMPLIANCE
5.16.7 INNOVATIVE FINANCIAL PLANNING
6 AI IN FINANCE MARKET, BY PRODUCT
6.1 INTRODUCTION
6.1.1 PRODUCT: AI IN FINANCE MARKET DRIVERS
6.2 TYPE
6.2.1 ERP AND FINANCIAL SYSTEMS
6.2.1.1 Real-time analytics and automated reporting for improved financial management
6.2.2 CHATBOTS & VIRTUAL ASSISTANTS
6.2.2.1 Enhancing operational efficiency and customer engagement in financial services
6.2.3 AUTOMATED RECONCILIATION SOLUTIONS
6.2.3.1 Boosting operational agility for swift transaction processing
6.2.4 INTELLIGENT DOCUMENT PROCESSING
6.2.4.1 Reducing manual errors, enabling quick decision-making, and accelerating processing time
6.2.5 GOVERNANCE, RISK, AND COMPLIANCE (GRC) SOFTWARE
6.2.5.1 Facilitating seamless collaboration across departments
6.2.6 ACCOUNTS PAYABLE/RECEIVABLE AUTOMATION SOFTWARE
6.2.6.1 Providing real-time insights for informed financial decisions
6.2.7 ROBO-ADVISORS
6.2.7.1 Providing automated investment management and financial advisory services
6.2.8 EXPENSE MANAGEMENT SYSTEMS
6.2.8.1 Streamlining financial operations and controlling costs
6.2.9 COMPLIANCE AUTOMATION PLATFORMS
6.2.9.1 Identifying compliance risks and enabling real-time alerts
6.2.10 ALGORITHMIC TRADING PLATFORMS
6.2.10.1 Automating trade execution and responding to market fluctuations
6.2.11 UNDERWRITING ENGINES/PLATFORMS
6.2.11.1 Expediting loan approvals and promoting fair lending
6.2.12 OTHER PRODUCT TYPES
6.3 DEPLOYMENT MODE
6.3.1 CLOUD
6.3.1.1 Cloud deployment offers scalability, flexibility, and cost-efficiency
6.3.2 ON-PREMISES
6.3.2.1 On-premises deployment provides fast data processing and real-time analytics
7 AI IN FINANCE MARKET, BY TECHNOLOGY
7.1 INTRODUCTION
7.1.1 TECHNOLOGY: AI IN FINANCE MARKET DRIVERS
7.2 GENERATIVE AI
7.2.1 ENHANCES CUSTOMER ENGAGEMENT AND PROCESS AUTOMATION IN FINANCE
7.3 OTHER AI TECHNOLOGIES
7.3.1 NLP
7.3.1.1 NLP boosts data analysis, automates interactions, and enhances compliance
7.3.2 PREDICTIVE ANALYTICS
7.3.2.1 AI-driven predictive analytics enables accurate forecasting
8 AI IN FINANCE MARKET, BY APPLICATION
8.1 INTRODUCTION
8.1.1 APPLICATION: AI IN FINANCE MARKET DRIVERS
8.2 FINANCE AS BUSINESS OPERATIONS
8.2.1 FRAUD DETECTION & PREVENTION
8.2.1.1 AI-driven fraud detection enhances security and reduces financial losses
8.2.1.2 Real-time transaction monitoring
8.2.1.3 Customer data security
8.2.1.4 Customer behavior analysis
8.2.1.5 Trend analysis
8.2.1.6 Others
8.2.2 RISK MANAGEMENT
8.2.2.1 AI-driven risk management enhances decision-making in finance
8.2.2.2 Credit risk scoring
8.2.2.3 Market volatility prediction
8.2.2.4 Stress testing
8.2.2.5 Others
8.2.3 CUSTOMER SERVICE & ENGAGEMENT
8.2.3.1 Customer service and engagement enhance personalization, leading to improved client satisfaction
8.2.3.2 Chatbots/Virtual assistants for customer support
8.2.3.3 Personalized financial product recommendations
8.2.3.4 Market segmentation
8.2.3.5 Personalized marketing messaging
8.2.3.6 New customer acquisition
8.2.3.7 Data-driven decision making
8.2.3.8 Customer retention management
8.2.3.9 Others
8.2.4 FINANCIAL COMPLIANCE & REGULATORY REPORTING
8.2.4.1 Financial compliance streamlines accuracy and efficiency in meeting standards
8.2.4.2 Risk & compliance management
8.2.4.3 Audit & reporting
8.2.4.4 Others
8.2.5 INVESTMENT & PORTFOLIO MANAGEMENT
8.2.5.1 AI optimizes investment and portfolio management for smarter decision-making and improved returns
8.2.5.2 Robo-advisors for wealth management
8.2.5.3 Portfolio rebalancing
8.2.5.4 Others
8.3 FINANCE AS BUSINESS FUNCTIONS
8.3.1 FINANCIAL PLANNING & FORECASTING
8.3.1.1 Financial planning enhances accuracy and decision-making in finance
8.3.1.2 Demand forecasting (CAPEX/OPEX)
8.3.1.3 Cash flow forecasting
8.3.1.4 Budgeting & expense management
8.3.1.5 Scenario planning
8.3.1.6 Others
8.3.2 AUTOMATED BOOKKEEPING & RECONCILIATION
8.3.2.1 Automated bookkeeping and reconciliation streamline financial processes and enhance accuracy
8.3.2.2 Real-time ledger matching
8.3.2.3 Invoice processing
8.3.2.4 Variance detection
8.3.2.5 Others
8.3.3 PROCUREMENT & SUPPLY CHAIN FINANCE
8.3.3.1 AI optimizes supply chain management by boosting efficiency and reducing costs
8.3.3.2 Invoice discounting
8.3.3.3 Supplier risk scoring
8.3.3.4 Dynamic payments
8.3.3.5 Payment automation
8.3.3.6 Others
8.3.4 REVENUE CYCLE MANAGEMENT
8.3.4.1 Revenue cycle management automates processes and improves cash flow through enhanced accuracy in billing
8.3.4.2 Payment optimization
8.3.4.3 Subscription billing management
8.3.4.4 Invoice settlements/Automated invoice processing
8.3.4.5 Churn management
8.3.4.6 Others
9 AI IN FINANCE MARKET, BY END USER
9.1 INTRODUCTION
9.1.1 END USER: AI IN FINANCE MARKET DRIVERS
9.2 END USER
9.2.1 FINANCE AS BUSINESS FUNCTIONS
9.2.1.1 Government & public sector
9.2.1.1.1 Strengthening governance and trust in AI in finance
9.2.1.2 Retail & e-commerce
9.2.1.2.1 Driving sales and satisfaction with AI-enhanced retail
9.2.1.3 Real estate
9.2.1.3.1 Revolutionizing real estate with AI-driven finance solutions
9.2.1.4 Manufacturing
9.2.1.4.1 Transforming financial processes of manufacturing sector for enhanced efficiency and growth
9.2.1.5 Telecom & media
9.2.1.5.1 Leveraging AI in telecom & media for optimized network management and enhanced service quality
9.2.1.6 Healthcare & pharma
9.2.1.6.1 AI provides enhanced and patient-centric solutions in finance
9.2.1.7 Utilities
9.2.1.7.1 AI transforms utilities sector by enhancing operational efficiency and improving predictive maintenance
9.2.1.8 Education
9.2.1.8.1 Harnessing AI to transform education finance by streamlining operations and enhancing financial literacy
9.2.1.9 Technology & software
9.2.1.9.1 Technology and software enable automation and improve decision-making processes
9.2.1.10 Other end users
9.3 FINANCE AS BUSINESS OPERATIONS
9.3.1 BANKING
9.3.1.1 AI enables better risk management and improves fraud detection
9.3.1.2 Corporate & commercial banking
9.3.1.3 Retail banking
9.3.1.4 Investment banking
9.3.2 INSURANCE
9.3.2.1 AI automates claim processing, reduces fraud, and personalizes policies
9.3.3 INVESTMENT & ASSET MANAGEMENT
9.3.3.1 AI enhances decision-making and optimizes portfolio management
9.3.3.2 Hedge funds
9.3.3.3 Private equity
9.3.3.4 Wealth management
9.3.4 FINTECH
9.3.4.1 AI in fintech automates tasks, improves data analysis, and provides real-time insights
9.3.4.2 Blockchain & cryptocurrency providers
9.3.4.3 Lending platform providers/specialty lenders
9.3.5 CAPITAL MARKETS/REGTECH
9.3.5.1 AI increases efficiency and reduces operational costs in capital markets
10 AI IN FINANCE MARKET, BY REGION
10.1 INTRODUCTION
10.2 NORTH AMERICA
10.2.1 NORTH AMERICA: AI IN FINANCE MARKET DRIVERS
10.2.2 NORTH AMERICA: MACROECONOMIC IMPACT
10.2.3 US
10.2.3.1 Transforming brands with AI-driven personalization in social media
10.2.4 CANADA
10.2.4.1 Accelerating AI adoption in finance through automation and digital transformation
10.3 EUROPE
10.3.1 EUROPE: AI IN FINANCE MARKET DRIVERS
10.3.2 EUROPE: MACROECONOMIC IMPACT
10.3.3 UK
10.3.3.1 Leveraging automation and data analytics for enhanced decision-making and compliance
10.3.4 GERMANY
10.3.4.1 Focus on automation in risk management and personalized banking services to improve operational efficiency
10.3.5 FRANCE
10.3.5.1 Robust government initiatives promote innovation and establish frameworks encouraging AI technology adoption
10.3.6 ITALY
10.3.6.1 Promotion of digital transformation and rising investment in AI technologies across financial institutions
10.3.7 SPAIN
10.3.7.1 Increased funding and strategic partnerships to enhance AI collaboration in financial services
10.3.8 REST OF EUROPE
10.4 ASIA PACIFIC
10.4.1 ASIA PACIFIC: AI IN FINANCE MARKET DRIVERS
10.4.2 ASIA PACIFIC: MACROECONOMIC IMPACT
10.4.3 CHINA
10.4.3.1 Increasing focus on AI innovation for operational efficiency in financial sector to boost market
10.4.4 JAPAN
10.4.4.1 Partnerships between financial institutions and tech firms accelerate AI integration for improved financial solutions
10.4.5 INDIA
10.4.5.1 Increasing adoption of AI-powered solutions by financial institutions for risk management to drive market
10.4.6 SOUTH KOREA
10.4.6.1 Government support enhances financial services and boosts competitiveness in fintech sector
10.4.7 AUSTRALIA & NEW ZEALAND
10.4.7.1 Increasing adoption of AI by growing fintech companies to drive market
10.4.8 ASEAN
10.4.8.1 Increasing digitalization of banking services to drive market
10.4.9 REST OF ASIA PACIFIC
10.5 MIDDLE EAST & AFRICA
10.5.1 MIDDLE EAST & AFRICA: AI IN FINANCE MARKET DRIVERS
10.5.2 MIDDLE EAST & AFRICA: MACROECONOMIC IMPACT
10.5.3 MIDDLE EAST
10.5.3.1 KSA
10.5.3.1.1 Government Vision 2030 initiative promoting digital transformation and AI adoption in financial services to drive market
10.5.3.2 UAE
10.5.3.2.1 Increased investments in AI-powered financial technologies to drive market
10.5.3.3 Kuwait
10.5.3.3.1 Growing focus on digital transformation to fuel AI adoption in finance market
10.5.3.4 Bahrain
10.5.3.4.1 Increasing adoption of AI technologies in banking sector to drive market
10.5.4 AFRICA
10.5.4.1 Increasing adoption of AI to enhance financial services to drive market
10.6 LATIN AMERICA
10.6.1 LATIN AMERICA: AI IN FINANCE MARKET DRIVERS
10.6.2 LATIN AMERICA: MACROECONOMIC IMPACT
10.6.3 BRAZIL
10.6.3.1 Government support and investments in AI to drive market
10.6.4 MEXICO
10.6.4.1 Increased investment in fintech to drive AI adoption in finance market
10.6.5 ARGENTINA
10.6.5.1 Fintech expansion and innovation to propel market growth
10.6.6 REST OF LATIN AMERICA
11 COMPETITIVE LANDSCAPE
11.1 OVERVIEW
11.2 KEY PLAYER STRATEGIES/RIGHT TO WIN, 2020–2024
11.3 REVENUE ANALYSIS, 2019–2023
11.4 MARKET SHARE ANALYSIS, 2023
11.4.1 MARKET SHARE ANALYSIS OF KEY PLAYERS (FINANCE AS BUSINESS FUNCTIONS)
11.4.2 MARKET RANKING ANALYSIS (FINANCE AS BUSINESS FUNCTIONS)
11.4.3 MARKET SHARE ANALYSIS OF KEY PLAYERS (FINANCE AS BUSINESS OPERATIONS)
11.4.4 MARKET RANKING ANALYSIS (FINANCE AS BUSINESS OPERATIONS)
11.5 PRODUCT COMPARISON
11.5.1 PRODUCT COMPARATIVE ANALYSIS, BY RISK ASSESSMENT
11.5.1.1 ZAML (Zest Automated Machine Learning) (Zest AI)
11.5.1.2 Kensho Risk (Kensho)
11.5.1.3 C3 AI Risk Management (C3 AI)
11.5.1.4 Finacle Treasury and Risk Management Solution (Infosys)
11.5.2 PRODUCT COMPARATIVE ANALYSIS, BY FRAUD DETECTION & PREVENTION
11.5.2.1 Socure ID+ (Socure)
11.5.2.2 Dataminr Real-Time Risk Detection (Dataminr)
11.5.2.3 Google Cloud (Google)
11.5.2.4 Vectra Cognito (Vectra AI)
11.5.3 PRODUCT COMPARATIVE ANALYSIS, BY CHATBOTS & PERSONAL ASSISTANTS
11.5.3.1 AlphaSense Search Chatbot (AlphaSense)
11.5.3.2 Oracle Digital Assistant (Oracle)
11.5.3.3 Watson Assistant (IBM)
11.6 COMPANY VALUATION AND FINANCIAL METRICS OF KEY VENDORS
11.7 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2023
11.7.1 COMPANY EVALUATION MATRIX: KEY PLAYERS
(FINANCE AS BUSINESS FUNCTIONS)
11.7.1.1 Stars
11.7.1.2 Emerging Leaders
11.7.1.3 Pervasive Players
11.7.1.4 Participants
11.7.2 COMPANY EVALUATION MATRIX: KEY PLAYERS
(FINANCE AS BUSINESS OPERATIONS)
11.7.2.1 Stars
11.7.2.2 Emerging Leaders
11.7.2.3 Pervasive Players
11.7.2.4 Participants
11.7.3 COMPANY FOOTPRINT: KEY PLAYERS
11.7.3.1 Company footprint
11.7.3.2 Region footprint
11.7.3.3 Product footprint
11.7.3.4 Application footprint
11.7.3.5 End user footprint
11.8 COMPANY EVALUATION MATRIX: STARTUPS/SMES, 2023
11.8.1 COMPANY EVALUATION MATRIX: STARTUPS/SMES
(FINANCE AS BUSINESS OPERATIONS)
11.8.1.1 Progressive companies
11.8.1.2 Responsive companies
11.8.1.3 Dynamic companies
11.8.1.4 Starting blocks
11.8.2 COMPANY EVALUATION MATRIX: STARTUPS/SMES
(FINANCE AS BUSINESS FUNCTIONS)
11.8.2.1 Progressive companies
11.8.2.2 Responsive companies
11.8.2.3 Dynamic companies
11.8.2.4 Starting blocks
11.8.3 COMPETITIVE BENCHMARKING: STARTUPS/SMES, 2023
11.8.3.1 Detailed list of key startups/SMEs
11.8.3.2 Competitive benchmarking of key startups/SMEs
11.9 COMPETITIVE SCENARIO
11.9.1 PRODUCT LAUNCHES AND ENHANCEMENTS
11.9.2 DEALS
12 COMPANY PROFILES
12.1 INTRODUCTION
12.2 KEY PLAYERS
12.2.1 FIS
12.2.1.1 Business overview
12.2.1.2 Products/Solutions/Services offered
12.2.1.3 Recent developments
12.2.1.3.1 Product launches and enhancements
12.2.1.3.2 Deals
12.2.1.4 MnM view
12.2.1.4.1 Key strengths
12.2.1.4.2 Strategic choices
12.2.1.4.3 Weaknesses and competitive threats
12.2.2 FISERV
12.2.2.1 Business overview
12.2.2.2 Products/Solutions/Services offered
12.2.2.3 Recent developments
12.2.2.3.1 Product launches and enhancements
12.2.2.3.2 Deals
12.2.2.4 MnM view
12.2.2.4.1 Key strengths
12.2.2.4.2 Strategic choices
12.2.2.4.3 Weaknesses and competitive threats
12.2.3 GOOGLE
12.2.3.1 Business overview
12.2.3.2 Products/Solutions/Services offered
12.2.3.3 Recent developments
12.2.3.3.1 Product launches and enhancements
12.2.3.3.2 Deals
12.2.3.4 MnM view
12.2.3.4.1 Key strengths
12.2.3.4.2 Strategic choices
12.2.3.4.3 Weaknesses and competitive threats
12.2.4 MICROSOFT
12.2.4.1 Business overview
12.2.4.2 Products/Solutions/Services offered
12.2.4.3 Recent developments
12.2.4.3.1 Product launches and enhancements
12.2.4.3.2 Deals
12.2.4.4 MnM view
12.2.4.4.1 Key strengths
12.2.4.4.2 Strategic choices
12.2.4.4.3 Weaknesses and competitive threats
12.2.5 ZOHO
12.2.5.1 Business overview
12.2.5.2 Products/Solutions/Services offered
12.2.5.3 Recent developments
12.2.5.3.1 Product launches and enhancements
12.2.5.3.2 Deals
12.2.5.4 MnM view
12.2.5.4.1 Key strengths
12.2.5.4.2 Strategic choices
12.2.5.4.3 Weaknesses and competitive threats
12.2.6 IBM
12.2.6.1 Business overview
12.2.6.2 Products/Solutions/Services offered
12.2.6.3 Recent developments
12.2.6.3.1 Product launches and enhancements
12.2.6.3.2 Deals
12.2.7 SOCURE
12.2.7.1 Business overview
12.2.7.2 Products/Solutions/Services offered
12.2.7.3 Recent developments
12.2.7.3.1 Deals
12.2.7.3.2 Expansions
12.2.8 WORKIVA
12.2.8.1 Business overview
12.2.8.2 Products/Solutions/Services offered
12.2.8.3 Recent developments
12.2.8.3.1 Deals
12.2.8.3.2 Expansions
12.2.9 PLAID
12.2.9.1 Business overview
12.2.9.2 Products/Solutions/Services offered
12.2.9.3 Recent developments
12.2.9.3.1 Deals
12.2.10 C3 AI
12.2.10.1 Business overview
12.2.10.2 Products/Solutions/Services offered
12.2.10.3 Recent developments
12.2.10.3.1 Deals
12.2.11 HIGHRADIUS
12.2.11.1 Business overview
12.2.11.2 Products/Solutions/Services offered
12.2.11.3 Recent developments
12.2.11.3.1 Product launches and enhancements
12.2.11.3.2 Deals
12.2.12 SAP
12.2.13 AWS
12.2.14 HPE
12.2.15 ORACLE
12.2.16 SALESFORCE
12.2.17 INTEL
12.2.18 NVIDIA
12.2.19 NETAPP
12.2.20 DATAROBOT
12.2.21 ENOVA INTERNATIONAL
12.2.22 ALPHASENSE
12.2.23 OCROLUS
12.2.24 VECTRA AI
12.2.25 TERADATA
12.2.26 PEGA
12.2.27 VENA SOLUTIONS
12.2.28 AFFIRM
12.2.29 SYMPHONYAI
12.2.30 ENVESTNET | YODLEE
12.3 STARTUPS/SMES
12.3.1 ADDEPTO
12.3.2 DEEPER INSIGHTS
12.3.3 H2O.AI
12.3.4 APP0
12.3.5 UNDERWRITE.AI
12.3.6 DEEPGRAM
12.3.7 EMAGIA
12.3.8 INDATA LABS
12.3.9 ZEST AI
12.3.10 SCIENAPTIC AI
12.3.11 GRADIENT AI
12.3.12 KASISTO
12.3.13 TRUMID
12.3.14 DATAVISOR
12.3.15 KAVOUT
12.3.16 WEALTHBLOCK
13 ADJACENT AND RELATED MARKETS
13.1 INTRODUCTION
13.2 ARTIFICIAL INTELLIGENCE (AI) MARKET – GLOBAL FORECAST TO 2030
13.2.1 MARKET DEFINITION
13.2.2 MARKET OVERVIEW
13.2.2.1 Artificial intelligence market, by offering
13.2.2.2 Artificial intelligence market, by business function
13.2.2.3 Artificial intelligence market, by technology
13.2.2.4 Artificial intelligence market, by vertical
13.2.2.5 Artificial intelligence market, by region
13.3 NLP IN FINANCE MARKET – GLOBAL FORECAST TO 2028
13.3.1 MARKET DEFINITION
13.3.2 MARKET OVERVIEW
13.3.2.1 NLP in finance market, by offering
13.3.2.2 NLP in finance market, by application
13.3.2.3 NLP in finance market, by technology
13.3.2.4 NLP in finance market, by vertical
13.3.2.5 NLP in finance market, by region
14 APPENDIX
14.1 DISCUSSION GUIDE
14.2 KNOWLEDGESTORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL
14.3 CUSTOMIZATION OPTIONS
14.4 RELATED REPORTS
14.5 AUTHOR DETAILS
