Report Id: SNS/ICT/1259 | May 2022 | Region: Global | 120 Pages
Report Scope & Overview:
The AI in Fintech Market was valued at USD 13.23 billion in 2022 and is predicted to increase at a CAGR of 42.3% from 2023 to 2030, to reach USD 222.49 billion by 2030.
Due to evolving technology, which is enhancing financial service providers' business operations, AI in the Fintech sector is thought to have potential for development in the next years. The market is being driven by factors such as increasing internet penetration and the availability of geographical data. The study's base year is 2016, and the prediction period is between 2017 and 2022.
Artificial intelligence improves outcomes by employing approaches borrowed from human intellect but applied at a scale that is not human. Fintech firms have been transformed in recent years by the computational arms race. Furthermore, data and near-endless volumes of data are altering AI to new heights, and smart contracts may just be a continuation of the current market trend.
In the banking business, AI is used to look at a person's entire financial health, keep up with real-time changes, and offer tailored advice based on fresh incoming data by examining cash accounts, credit accounts, and investment accounts. Banks and fintech have profited from AI and machine learning because they can handle large volumes of data on clients. This data and information is then compared to arrive at conclusions about what services/products clients desire, which has benefited in the development of customer relationships
MARKET DYNAMICS:
KEY DRIVERS:
The growing number of significant market partnerships has resulted in a rise in financing for the growth and development of advanced and automated technology/machinery to combat fraudulent activities.
The rising integration of artificial intelligence and machine learning technologies will assist the market even more.
RESTRAINTS:
An increase in the number of restrictions to limit the scope of long-term growth.
The market growth rate will be hampered by the complexity of deploying cloud-based deployment strategies.
OPPORTUNITIES:
Cloud-based firewalls provide several advantages.
The growing requirement to protect businesses' networks from unwanted and unprecedented attacks, as well as increased utilization of services due to smooth scalability, will drive the market's future growth.
CHALLENGES:
A scarcity of competent consultants to create artificial intelligence in fintech
Inadequate infrastructure in developing countries
IMPACT OF COVID-19:
The latest coronavirus epidemic has been beneficial to the market. The coronavirus pandemic has caused a halt in commercial activity, as well as interruptions in global supply chains, border restrictions, and travel restrictions imposed by government agencies. This has led in a change in bank and fintech culture toward working from home. Furthermore, the market is expected to develop due to the growing usage of artificial intelligence and machine learning tools in banking companies throughout the world for completing vital tasks. Furthermore, by the end of 2020, worldwide corporations were investing more in cloud technologies to make remote working easier.
KEY PLAYERS:
The major players in AI in Fintech is Microsoft (Washington, US), Google (California, US), Salesforce.com (California, US), IBM (New York, US), Intel (California, US), Amazon Web Services (Washington, US), Inbenta Technologies (California, US), IPsoft (New York, US), Nuance Communications (Massachusetts, US), and ComplyAdvantage.com (New York, US).
MARKET ESTIMATION:
The worldwide AI in fintech market has been divided into cloud and on-premises deployment models based on the deployment methodology. Because it lowers the total cost of ownership, the on-premises segment led the worldwide AI in fintech market. During the projection year, however, the cloud sector of AI in the fintech industry is expected to increase significantly. The expansion of cloud deployment can be linked to benefits such as high levels of security and affordability.
Based on applications, the market has been divided into three categories: businesses, people, and others. The enterprise sector led the market in 2021, accounting for more than 52% of global sales. These platforms offer services such as credit management, transaction management, and asset management. To better serve their consumers, many neo bank service providers for SMEs are aiming to extend their product lines through acquisitions. The AI in the fintech sector has been divided into three categories: virtual assistants, business analytics and reporting, and customer behavioural analytics. Because it analyses all of the risks connected with clients, customer behavioural analytics in AI in the fintech industry is predicted to develop at the fastest CAGR throughout the forecast period. The need for AI in the fintech sector will be driven by the ability of business analytics and reporting to assist in regulatory and compliance management, as well as the ability to analyze consumer behaviour.
KEY MARKET SEGMENTATION:
By Component:
Solutions
Software Tools
Platforms
Services
Managed
Professional
By Deployment Mode:
Cloud
On-premises
AI in Fintech Market by Application Area:
Virtual Assistant (Chatbots)
Business Analytics and Reporting
Customer Behavioral Analytics
Others (includes market research, advertising, and marketing campaign)
REGIONAL ANALYSIS:
In 2021, North America dominated the AI market in the financial industry. The robust economy, considerable investment in research and development, and the presence of major businesses in the region are all factors contributing to the region's prosperity. Furthermore, one of the major drivers driving AI use in the financial business is fast digitization.
Due to the rapid acceptance of digital payment and rising prevalence of internet services in the area, the Asia Pacific region in the AI in fintech market is predicted to develop at the fastest CAGR during the forecast period. The government measures that assist AI growth in the fintech business give sufficient opportunities. According to secondary sources, the application of AI in the banking sector in the Asia Pacific area alone is estimated to produce over USD 99 billion by 2030.
REGIONAL COVERAGE:
North America
The USA
Canada
Mexico
Europe
Germany
The UK
France
Italy
Spain
The Netherlands
Rest of Europe
Asia-Pacific
Japan
South Korea
China
India
Australia
Rest of Asia-Pacific
The Middle East & Africa
Israel
UAE
South Africa
Rest of the Middle East & Africa
Latin America
Brazil
Argentina
Rest of Latin America
Report Attributes | Details |
Market Size in 2022 | US$ 13.23 Bn |
Market Size by 2030 | US$ 222.49 Bn |
CAGR | CAGR of 42.3% From 2023 to 2030 |
Base Year | 2022 |
Forecast Period | 2023-2030 |
Historical Data | 2020-2021 |
Report Scope & Coverage | Market Size, Segments Analysis, Competitive Landscape, Regional Analysis, DROC & SWOT Analysis, Forecast Outlook |
Key Segments | • by Component (Solution, Service) • by Application Area (Virtual Assistant, Business Analytics & Reporting, Customer Behavioral Analytics) • by Deployment Mode (Cloud, On-Premises) |
Regional Analysis/Coverage | North America (USA, Canada, Mexico), Europe (Germany, UK, France, Italy, Spain, Netherlands, Rest of Europe), Asia-Pacific (Japan, South Korea, China, India, Australia, Rest of Asia-Pacific), The Middle East & Africa (Israel, UAE, South Africa, Rest of Middle East & Africa), Latin America (Brazil, Argentina, Rest of Latin America) |
Company Profiles | Microsoft, Google, Salesforce.com, IBM, Intel , Amazon Web Services, Inbenta Technologies, IPsoft, Nuance Communications, and ComplyAdvantage.com |
Key Drivers | • The rising integration of artificial intelligence and machine learning technologies will assist the market even more. |
Market Opportunities | • The growing requirement to protect businesses' networks from unwanted and unprecedented attacks, as well as increased utilization of services due to smooth scalability, will drive the market's future growth. |
Frequently Asked Questions (FAQ) :
Table of Contents
1. Introduction
1.1 Market Definition
1.2 Scope
1.3 Research Assumptions
2. Research Methodology
3. Market Dynamics
3.1 Drivers
3.2 Restraints
3.3 Opportunities
3.4 Challenges
4. Impact Analysis
4.1 COVID-19 Impact Analysis
4.2 Impact of Ukraine- Russia war
4.3 Impact of ongoing Recession
4.3.1 Introduction
4.3.2 Impact on major economies
4.3.2.1 US
4.3.2.2 Canada
4.3.2.3 Germany
4.3.2.4 France
4.3.2.5 United Kingdom
4.3.2.6 China
4.3.2.7 Japan
4.3.2.8 South Korea
4.3.2.9 Rest of the World
5. Value Chain Analysis
6. Porter’s 5 forces model
7. PEST Analysis
8. AI in Fintech market Segmentation, by Service Type by Component:
8.1 Solutions
8.1.1 Software Tools
8.1.2 Platforms
8.2 Services
8.2.1 Managed
8.2.2 Professional
9. AI in Fintech market By Deployment Mode:
9.1 Cloud
9.2 On-premises
10. AI in Fintech Market by Application Area:
10.1 Virtual Assistant (Chatbots)
10.2 Business Analytics and Reporting
10.3 Customer Behavioral Analytics
10.4 Others (includes market research, advertising, and marketing campaign)
11. Regional Analysis
11.1 Introduction
11.2 North America
11.2.1 the USA
11.2.2 Canada
11.2.3 Mexico
11.3 Europe
11.3.1 Germany
11.3.2 the UK
11.3.3 France
11.3.4 Italy
11.3.5 Spain
11.3.6 The Netherlands
11.3.7 Rest of Europe
11.4 Asia-Pacific
11.4.1 Japan
11.4.2 South Korea
11.4.3 China
11.4.4 India
11.4.5 Australia
11.4.6 Rest of Asia-Pacific
11.5 The Middle East & Africa
11.5.1 Israel
11.5.2 UAE
11.5.3 South Africa
11.5.4 Rest
11.6 Latin America
11.6.1 Brazil
11.6.2 Argentina
11.6.3 Rest of Latin America
12. Company Profiles
12.1 Microsoft (Washington, US)
12.1.1 Financial
12.1.2 Products/ Services Offered
12.1.3 SWOT Analysis
12.1.4 The SNS view
12.2 Google (California, US)
12.3 Salesforce.com (California, US)
12.4 IBM (New York, US)
12.5 Intel (California, US)
12.6 Amazon Web Services (Washington, US)
12.7 Inbenta Technologies (California, US)
12.8 IPsoft (New York, US)
12.9 Nuance Communications (Massachusetts, US)
12.10 ComplyAdvantage.com (New York, US)
13. Competitive Landscape
13.1 Competitive Benchmarking
13.2 Market Share analysis
13.3 Recent Developments
14. Conclusion
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