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
금융 AI 시장 규모는 2024년 383억 6,000만 달러에서 2030년에는 1,903억 3,000만 달러로 성장하며, 예측 기간 동안 CAGR은 30.6%가 될 것으로 예측됩니다.
채팅봇과 가상 어시스턴트는 고객 서비스의 자동화, 사용자 경험 향상, 운영 비용 절감이 가능하기 때문에 AI를 활용한 금융 시장에서 수요가 높아지고 있습니다. 리스크의 식별과 경감을 강화하고, 보다 안전한 금융 관행을 육성하고, 금융에 있어서 AI 시장을 형성하고 있습니다.
| 조사 범위 |
| 조사 대상년도 |
2019-2030년 |
| 기준년 |
2023년 |
| 예측 기간 |
2024-2030년 |
| 검토 단위 |
달러(10억 달러) |
| 부문 |
제품별, 기술별, 용도별, 최종 사용자별, 지역별 |
| 대상 지역 |
북미, 유럽, 아시아태평양, 중동, 아프리카, 라틴아메리카 |
핀테크 기업은 금융 서비스의 자동화, 고객 경험의 향상, 업무 효율의 향상을 위해 AI를 활용하도록 되어 왔습니다. 효과적인 위험 관리에 필수적입니다. 소비자가 더 빠르고 효율적인 서비스를 요구합니다 핀텍 기업은 부정 감지, 신용 스코어링, 채팅봇을 통한 고객 참여 등의 작업에 AI를 활용하고 있습니다. 이 분야를 향후 수년간 크게 성장시킬 것으로 자리잡고 있습니다.
경제 전체의 급속한 디지털 전환과 핀테크 스타트업의 대두가 아시아태평양의 AI 솔루션을 견인하고 있습니다. 기술에 상당한 투자를 하고 있습니다. 싱가포르 금융관리국(MAS)과 중국 사이버공간관리국(CAC) 등의 규제기관은 혁신을 촉진하고 시장의 성장을 더욱 뒷받침합니다. 리스크 관리 솔루션의 필요성은 금융에 AI의 급속한 도입에 기여하고 있으며 아시아태평양을 이 분야의 리더로 자리매김하고 있습니다.
본 보고서에서는 세계 금융의 AI 시장에 대해 조사했으며, 제품별, 기술별, 용도별, 최종 사용자별, 지역별 동향 및 시장 진출기업 프로파일 등을 정리했습니다.
목차
제1장 서론
제2장 조사 방법
제3장 주요 요약
제4장 중요 인사이트
제5장 시장 개요와 업계 동향
- 소개
- 시장 역학
- 금융에 있어서의 AI 시장의 진화
- 공급망 분석
- 생태계 분석
- 사례 연구 분석
- 기술 분석
- 2024-2025년의 주된 회의와 이벤트
- 투자와 자금조달 시나리오
- 규제 상황
- 특허 분석
- 가격 분석
- Porter's Five Forces 분석
- 고객사업에 영향을 주는 동향/혼란
- 주요 이해관계자와 구매 기준
- 금융에 있어서의 AI 시장에 대한 생성형 AI의 영향
제6장 금융에 있어서의 AI 시장(제품별)
제7장 금융에 있어서의 AI 시장(기술별)
제8장 금융에 있어서의 AI 시장(용도별)
- 소개
- 비즈니스 운영으로서의 금융
- 비즈니스 기능으로서의 금융
제9장 금융에 있어서의 AI 시장(최종사용자별)
제10장 금융에 있어서의 AI 시장(지역별)
- 소개
- 북미
- 유럽
- 아시아태평양
- 중동 및 아프리카
- 라틴아메리카
제11장 경쟁 구도
- 개요
- 주요 참가 기업의 전략/비책, 2020-2024년
- 수익 분석, 2019-2023년
- 시장 점유율 분석, 2023년
- 제품 비교
- 주요 벤더의 기업 평가 및 재무 지표
- 기업평가 매트릭스: 주요 진입기업, 2023년
- 기업평가 매트릭스: 스타트업/중소기업, 2023년
- 경쟁 시나리오
제12장 기업 프로파일
- 소개
- 주요 진출기업
- FIS
- FISERV
- GOOGLE
- MICROSOFT
- ZOHO
- IBM
- SOCURE
- WORKIVA
- PLAID
- C3 AI
- HIGHRADIUS
- SAP
- AWS
- HPE
- ORACLE
- SALESFORCE
- INTEL
- NVIDIA
- NETAPP
- DATAROBOT
- ENOVA INTERNATIONAL
- ALPHASENSE
- OCROLUS
- VECTRA AI
- TERADATA
- PEGA
- VENA SOLUTIONS
- AFFIRM
- SYMPHONYAI
- ENVESTNET|YODLEE
- 스타트업/중소기업
- ADDEPTO
- DEEPER INSIGHTS
- H2O.AI
- APP0
- UNDERWRITE.AI
- DEEPGRAM
- EMAGIA
- INDATA LABS
- ZEST AI
- SCIENAPTIC AI
- GRADIENT AI
- KASISTO
- TRUMID
- DATAVISOR
- KAVOUT
- WEALTHBLOCK
제13장 인접 시장과 관련 시장
제14장 부록
JHS
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.
| Scope of the Report |
|---|
| Years Considered for the Study | 2019-2030 |
| Base Year | 2023 |
| Forecast Period | 2024-2030 |
| Units Considered | USD (Billion) |
| Segments | Product type, Technology, Application, End user, and Region |
| Regions covered | North America, Europe, Asia Pacific, Middle East & Africa, Latin America |
"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%.
FIS (US), Fiserv (US), Google (US), Microsoft (US), Zoho (India), IBM (US), Socure (US), Workiva (US), Plaid (US), SAS (US), C3 AI (US); are some of the key players 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:
- 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.
TABLE OF CONTENTS
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
- 5.16.2 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
- 12.2.10 C3 AI
- 12.2.10.1 Business overview
- 12.2.10.2 Products/Solutions/Services offered
- 12.2.10.3 Recent developments
- 12.2.11 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
