AI in Fintech Market is expected to register exponential growth

According to the latest research report published by Market Data Forecast, the size of the global AI in FinTech market is anticipated to reach USD 74.9 billion by 2029 from USD 13.3 billion in 2024, growing at a CAGR of 41.2% from 2024 to 2029.

The AI in Fintech market is quickly developing, owing to advances in machine learning and data analytics. AI technologies are transforming several elements of the financial business, such as fraud detection, customer service, risk management, and investment advisory. Key players are using AI algorithms to improve decision-making, streamline operations, and provide better customer experiences. This industry is distinguished by fierce rivalry and ongoing innovation, with increased investments in AI technology to satisfy the changing needs of the financial sector.

Global AI in Fintech Market Report Scope:
• Market Size (2029): USD 74.9 Billion
• Market Size (2023): USD 13.3 Billion
• CAGR (2024 to 2029): 41.2%
• Base Year: 2023
• Forecast Period: 2024 to 2029
• Segments Analysed: By Component, Application, Technology and Region
• Geographies Covered: North America, Europe, APAC, Latin America, and Middle East & Africa
• Key Market Players Profiled: Ant Financial, Square, Stripe, PayPal, Robinhood, Adyen, Revolut, Klarna, Wealthfront, Coinbase, Affirm, Betterment, SoFi, Plaid, ZestFinance, Kabbage, LendingClub, Fiserv, N26, and others.

Market Drivers:

One of the factors propelling artificial intelligence in the financial sector is improved customer service performance. As artificial intelligence becomes more advanced, chatbots, virtual assistants, and AI interfaces for customer communication are being developed. Additionally, chatbots leverage the potent sentiment analysis that artificial intelligence makes possible. Analysing user interactions with the financial application or service, finding flaws, and fixing them are the objectives of this. AI-powered chatbots make it easy and convenient for banks and their clients to communicate. Similarly, automatic routines address routine complaints. Some financial institutions are increasing their customer base through the use of chatbots.
Banks and fintech have benefited greatly from AI and machine learning, which handle large amounts of consumer data to provide quick service, new offerings, and improved customer relations. With the use of behavioural data, financial institutions and insurance companies are increasingly using machine learning in real-time target marketing to predict their clients' product preferences.
By efficiently evaluating credibility based on financial facts, it helps with quicker and more accurate decision making during the credit appraisal and lending process. Additionally, AI systems that examine historical data and identify patterns and abnormalities have improved risk assessment and fraud detection skills. The use of AI-driven fraud detection systems, which can quickly detect fraudulent activity, can significantly increase security.

Market Restraints:

There are currently no rules governing the use of AI in the FinTech sector, which limits market expansion. Artificial intelligence in the financial services sector is different from traditional, human-powered analytics in that it cannot be used for regulatory reporting. This is due to the fact that the propensity model cannot be shown to the regulator outside of the neural network. Financial institutions may be encouraged to break the law and engage in corruption, whether on intention or by accident, if there is a barrier to legal compliance. This makes it a major obstacle to the advancement of AI in the FinTech industry.
Concerns regarding data security and transparency have been highlighted by AI investments in financial services. AI's handling of private and sensitive data calls for extra security precautions. To guarantee that consumer data is handled properly, fintech organisations are putting strong data protection measures in place, such as certifications, laws, and security guidelines. Furthermore, the lack of skilled consultants in fintech to develop artificial intelligence may restrict the market's growth.
High implementation costs and a lack of awareness in developing countries will further limit expansion. The market growth rate will be further hampered by weak economies, inadequate infrastructure, and a lack of security concerns.

Browse full details of the report @ https://www.marketdataforecast.com/market-reports/ai-in-fintech-market

Key Insights from The Report:
• Based on Component, the Solution segment led the market and accounted for a large share of the global market in 2023.
• Based on Application, the Fraud Detection and Prevention segment captured the largest share of the global market in 2023.
• Based on Technology, Machine Learning is anticipated to dominate the market during the forecast period in 2023.
• North America dominated the smart building market in 2023. Asia-Pacific accounted for the second-highest share of the global market in 2023, and the European region is estimated to grow over the forecast period.

SEGMENTATION INCLUDED IN THIS REPORT:

By Component:
• Solutions
• Services

By Application:
• Robotic Process Automation (RPA)
• Chatbots and Virtual Assistants
• Fraud Detection and Prevention
• Algorithmic Trading
• Credit Scoring and Risk Assessment

By Technology:
• Machine Learning
• Natural Language Processing (NLP)
• Computer Vision
• Blockchain and Cryptocurrency

By Region:
• North America
• Europe
• Asia-Pacific
• Middle East & Africa
• Latin America

TABLE OF CONTENTS:

1. Introduction
1.1 Market Definition
1.2 Scope of the report
1.3 Study Assumptions
1.4 Base Currency, Base Year, and Forecast Periods

2. Research Methodology
2.1 Analysis Design
2.2 Research Phases
2.2.1 Secondary Research
2.2.2 Primary Research
2.2.3 Data Modelling
2.2.4 Expert Validation
2.3 Study Timeline

3. Report Overview
3.1 Executive Summary
3.2 Key Inferences

4. Market Dynamics
4.1 Impact Analysis
4.1.1 Drivers
4.1.2 Restraints
4.1.3 Opportunities
4.2 Regulatory Environment
4.3 Technology Timeline & Recent Trends

5. Competitor Benchmarking Analysis
5.1 Key Player Benchmarking
5.1.1 Market Share Analysis
5.1.2 Products/Service
5.1.3 Regional Presence
5.2 Mergers & Acquisition Landscape
5.3 Joint Ventures & Collaborations

6. Market Segmentation
6.1 AI in FinTech Market, By Component
6.1.1 Solution
6.1.2 Service
6.1.3 Market Size Estimations & Forecasts (2024-2029)
6.1.4 Y-o-Y Growth Rate Analysis
6.1.5 Market Attractiveness Index
6.2 AI in FinTech Market, By Application
6.2.1 Robotic Process Automation (RPA)
6.2.2 Chatbots and Virtual Assistants
6.2.3 Fraud Detection and Prevention
6.2.4 Algorithmic Trading
6.2.5 Credit Scoring and Risk Assessment
6.2.6 Market Size Estimations & Forecasts (2024-2029)
6.2.7 Y-o-Y Growth Rate Analysis
6.2.8 Market Attractiveness Index
6.3 AI in FinTech Market, By Technology
6.3.1 Machine Learning
6.3.2 Natural Language Processing (NLP)
6.3.3 Computer Vision
6.3.4 Blockchain and Cryptocurrency
6.3.5 Market Size Estimations & Forecasts (2024-2029)
6.3.6 Y-o-Y Growth Rate Analysis
6.3.7 Market Attractiveness Index

7. Geographical Landscape
7.1 Global AI in FinTech Market, by Region
7.2 North America - Market Analysis (2024 - 2029)
7.2.1 By Country
7.2.1.1 USA
7.2.1.2 Canada
7.2.2 By Component
7.2.3 By Application
7.2.4 By Technology
7.3 Europe
7.3.1 By Country
7.3.1.1 UK
7.3.1.2 France
7.3.1.3 Germany
7.3.1.4 Spain
7.3.1.5 Italy
7.3.1.6 Rest of Europe
7.3.2 By Component
7.3.3 By Application
7.3.4 By Technology
7.4 Asia Pacific
7.4.1 By Country
7.4.1.1 China
7.4.1.2 India
7.4.1.3 japan
7.4.1.4 South Korea
7.4.1.5 South East Asia
7.4.1.6 Australia & NZ
7.4.1.7 Rest of Asia-Pacific
7.4.2 By Component
7.4.3 By Application
7.4.4 By Technology
7.5 Latin America
7.5.1 By Country
7.5.1.1 brazil
7.5.1.2 Argentina
7.5.1.3 Mexico
7.5.1.4 Rest of Latin America
7.5.2 By Component
7.5.3 By Application
7.5.4 By Technology
7.6 Middle East and Africa
7.6.1 By Country
7.6.1.1 Middle East
7.6.1.2 Africa
7.6.2 By Component
7.6.3 By Application
7.6.4 By Technology

8. Key Player Analysis
8.1 Ant Financial
8.1.1 Business Description
8.1.2 Products/Service
8.1.3 Financials
8.1.4 SWOT Analysis
8.1.5 Recent Developments
8.1.6 Analyst Overview
8.2 square
8.3 Stripe
8.4 PayPal
8.5 Robinhood
8.6 Adyen
8.7 Revolut
8.8 Klarna
8.9 Wealthfront
8.10 Coinbase

9. Market Outlook & Investment Opportunities

10. Appendix
List of Tables
List of Figures

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