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AI in Fintech Market

AI in Fintech Market Size, Share & Segmentation by Component (Solution, Service), Application Area (Virtual Assistant, Business Analytics & Reporting, Customer Behavioral Analytics), Deployment Mode (Cloud, On-Premises), by Regions and Global Market Forecast 2022-2028

Report Id: SNS/ICT/1259 | May 2022 | Region: Global | 120 Pages

Report Scope & Overview:

The AI in Fintech Market was valued at USD 9.3 billion in 2021 and is predicted to increase at a CAGR of 42.3% from 2022 to 2028, to reach USD 109.87 billion by 2028.

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.

AI in Fintech Market Revenue Graph

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:

Market, By Deployment Mode:

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.

Market, By Application:

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)

AI in Fintech Market Segment Chart

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

AI in Fintech Market Report Scope:
Report Attributes Details
Market Size in 2021  US$ 9.3 Bn
Market Size by 2028  US$ 109.87 Bn
CAGR   CAGR of 42.3% From 2022 to 2028
Base Year  2021
Forecast Period  2022-2028
Historical Data  2017-2020
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 the Ukraine-Russia War

 

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

An accurate research report requires proper strategizing as well as implementation. There are multiple factors involved in the completion of good and accurate research report and selecting the best methodology to compete the research is the toughest part. Since the research reports we provide play a crucial role in any company’s decision-making process, therefore we at SNS Insider always believe that we should choose the best method which gives us results closer to reality. This allows us to reach at a stage wherein we can provide our clients best and accurate investment to output ratio.

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The 5 steps process:

Step 1: Secondary Research:

Secondary Research or Desk Research is as the name suggests is a research process wherein, we collect data through the readily available information. In this process we use various paid and unpaid databases which our team has access to and gather data through the same. This includes examining of listed companies’ annual reports, Journals, SEC filling etc. Apart from this our team has access to various associations across the globe across different industries. Lastly, we have exchange relationships with various university as well as individual libraries.

Secondary Research

Step 2: Primary Research

When we talk about primary research, it is a type of study in which the researchers collect relevant data samples directly, rather than relying on previously collected data.  This type of research is focused on gaining content specific facts that can be sued to solve specific problems. Since the collected data is fresh and first hand therefore it makes the study more accurate and genuine.

We at SNS Insider have divided Primary Research into 2 parts.

Part 1 wherein we interview the KOLs of major players as well as the upcoming ones across various geographic regions. This allows us to have their view over the market scenario and acts as an important tool to come closer to the accurate market numbers. As many as 45 paid and unpaid primary interviews are taken from both the demand and supply side of the industry to make sure we land at an accurate judgement and analysis of the market.

This step involves the triangulation of data wherein our team analyses the interview transcripts, online survey responses and observation of on filed participants. The below mentioned chart should give a better understanding of the part 1 of the primary interview.

Part 2: In this part of primary research the data collected via secondary research and the part 1 of the primary research is validated with the interviews from individual consultants and subject matter experts.

Consultants are those set of people who have at least 12 years of experience and expertise within the industry whereas Subject Matter Experts are those with at least 15 years of experience behind their back within the same space. The data with the help of two main processes i.e., FGDs (Focused Group Discussions) and IDs (Individual Discussions). This gives us a 3rd party nonbiased primary view of the market scenario making it a more dependable one while collation of the data pointers.

Step 3: Data Bank Validation

Once all the information is collected via primary and secondary sources, we run that information for data validation. At our intelligence centre our research heads track a lot of information related to the market which includes the quarterly reports, the daily stock prices, and other relevant information. Our data bank server gets updated every fortnight and that is how the information which we collected using our primary and secondary information is revalidated in real time.

Step 4: QA/QC Process

After all the data collection and validation our team does a final level of quality check and quality assurance to get rid of any unwanted or undesired mistakes. This might include but not limited to getting rid of the any typos, duplication of numbers or missing of any important information. The people involved in this process include technical content writers, research heads and graphics people. Once this process is completed the title gets uploader on our platform for our clients to read it.

Step 5: Final QC/QA Process:

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