Artificial Intelligence Ai In Fintech Market
PUBLISHED: 2022 ID: SMRC21385
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Artificial Intelligence Ai In Fintech Market

Artificial Intelligence (AI) in Fintech Market Forecasts to 2028 – Global Analysis By Place of Deployment (Front Office, Back Office), Application (Quantitative, Fraud Detection), End User (Retail Banking, Hedge Funds) and By Geography

4.9 (95 reviews)
4.9 (95 reviews)
Published: 2022 ID: SMRC21385

This report covers the impact of COVID-19 on this global market
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Years Covered

2020-2028

Estimated Year Value (2021)

US $8,218.01 MN

Projected Year Value (2028)

US $32,681.16 MN

CAGR (2021 - 2028)

21.8%

Regions Covered

North America, Europe, Asia Pacific, South America, and Middle East & Africa

Countries Covered

US, Canada, Mexico, Germany, UK, Italy, France, Spain, Japan, China, India, Australia, New Zealand, South Korea, Rest of Asia Pacific, South America, Argentina, Brazil, Chile, Middle East & Africa, Saudi Arabia, UAE, Qatar, and South Africa

Largest Market

North America

Highest Growing Market

Asia Pacific


According to Stratistics MRC, the Global Artificial Intelligence (AI) in Fintech Market is accounted for $8,218.01 million in 2021 and is expected to reach $32,681.16 million by 2028 growing at a CAGR of 21.8% during the forecast period. AI in Fintech gets better results by applying techniques derived from aspects of Human Intelligence at a beyond human scale. The computational arms race for past years has revolutionized the Fintech companies. AI and machine learning have gained banks and fintech as they can develop huge amounts of information about customers. This data and information are then compared to obtain results about suitable services/products that customers want, which has aided, effectively, in developing customer relations.



Market Dynamics:

Driver:

Focus of companies on cost reduction and improving efficiency


Artificial Intelligence in fintech is allowing companies to cut down their cost, automate their process and reduce the chances of error. AI Chatbots are used by companies as customer assistants for various purposes such as sales, customer care executive (over phone), and online chat executive. AI is empowering small finance companies as it is affordable as well as chances of error occurrence is very low. In addition, the insightful details about the cash flow and income and expense are gaining the traction from the end user as this helps companies to reduce their expenses.

Restraint:

Concerns regarding data privacy


The biggest challenge with AI is the sensitive issue of data privacy and security, which most fintech companies are facing. The fintech sector is governed by strict compliance to regulations and governance since any data breach or security failure could be disastrous.   Privacy concerns are cropping up as companies feed more and more consumer and vendor data into advanced, AI-fuelled algorithms to create new bits of sensitive information, unbeknownst to affected consumers and employees. This is especially prevalent in the retail banking sector, where consumer data collection has been at the forefront in terms of big data challenges. These data privacy concerns will hinder the adoption of AI especially in banking sector.

Opportunity:

New technological advancements


One of the biggest cybercrimes is credit card fraud. Thus, companies are designing a new generation of algorithms that are Convolutional Neural Networks and are based on the visual cortex, which is a small segment of cells that are sensitive to specific regions of the visual field in the human body. This means that they are able to extract elementary visual features like oriented edges, end-points and corners. This technology can study the spending data of an individual and be able to determine, based on this information, whether they performed the most recent transaction on their credit card or if someone else was using their credit card data.

Threat:

Increased number of regulations


The increasing number of multiple regulatory compliances will create hindrances for the growth of the market. Also, lack of skilled consultants to develop artificial intelligence in fintech will narrow down the scope of growth for the market. Further, suspension of business activity on account of the coronavirus pandemic will yet again create hindrances.

The quantitative and asset management segment is expected to have the highest CAGR during the forecast period

The quantitative and asset management segment is growing at the highest CAGR in the market. This is mainly because AI is changing the asset management sector by allowing fundamental analysts to conduct a large number of researches and extract more information quickly, allowing them to discover accurate investing ideas. Moreover, the financial institutions have invested a significant proportion of amount in AI solutions and services for quantitative and asset management, to optimize data management in a better way.

Region with highest share:

North America is projected to hold the largest share in the market. The region has also registered the maximum adoption of AI in Fintech solutions, due to the strong economy, robust presence of prominent AI software and system suppliers, and combined investment by government and private organizations for the development and growth of R&D activities.

Region with highest CAGR:

The Asia Pacific is projected to have the highest CAGR due to the growing economy, increasing investments in IT infrastructure, rising adoption of new technologies, and surging government initiatives toward the development and deployment of IoT and AI technologies in companies operating across several verticals in the region. Moreover, major players, as a part of their business strategy, are investing in the untapped markets of the region, which, in turn, is contributing to the regional market growth.



Key players in the market:

Some of the key players profiled in the Artificial Intelligence (AI) in Fintech Market include Ripple Labs Inc., Active.Ai, TIBCO Software (Alpine Data Labs), Trifacta Software Inc., Data Minr Inc., Zeitgold GmbH, Sift Science Inc., Pefin Holdings LLC, Betterment Holdings, WealthFront Inc., Intel Corporation, Sentifi AG, ComplyAdvantage.com, Narrative Science, Amazon Web Services Inc., IPsoft Inc., Next IT Corporation, Microsoft Corporation, Onfido, and IBM Corporation.

Key developments:

In May 2020: Sentifi AG announced the expanded alternative data-based analytics to surface investment opportunities and manage risks. Sentifi’s new analytics solution includes detection of the sector, industry outliers, ESG events with potential asset valuation impact, and investment themes trending real-time while offering investors the ability to detect outliers within their portfolio. Investors can assess portfolio sentiment performance to the custom benchmark and quickly identify significant market events and impacted sectors, industries, and assets.

In April 2020: Verient System INC, Next IT Corporation's parent company, reached an agreement with one of the world's largest banking organizations to provide its new standard solution for fraud and corporate security investigations. The Variant Systems Al platform assists the bank in detecting fraud, as well as in meeting cybersecurity and deployment management requirements.

Place of Deployments Covered:
• Front Office
• Back Office          

Components Covered:
• Hardware
• Services          
• Solutions          

Deployments Covered:
• On-Premise          
• Cloud

Machine Learning Applications Covered:
• Deep Learning          
• Machine Vision
• Natural Language Processing          
• Reinforced Learning          
• Semi-Supervised Learning          
• Supervised Learning          
• Unsupervised Learning          

Applications Covered:
• Business Analytics and Reporting          
• Virtual Assistants (Chatbots)          
• Customer Behavioural Analytics           
• Risk Investigation          
• Quantitative and Asset Management          
• Fraud Detection          
• Know Your Customer (KYC)          
• Accounting          
• Customer Service          
• Peer-to-Peer (P2P) Lending      
• Insurance Support          
• Network Security          
• Capital Markets          
• Other Applications          

End Users Covered:
• Retail Banking          
• Hedge Funds          
• Stock Trading Firms          
• Investment Banking

Regions Covered:
• North America
o US
o Canada
o Mexico
• Europe
o Germany
o UK
o Italy
o France
o Spain
o Rest of Europe
• Asia Pacific
o Japan       
o China       
o India       
o Australia 
o New Zealand
o South Korea
o Rest of Asia Pacific   
• South America
o Argentina
o Brazil
o Chile
o Rest of South America
• Middle East & Africa
o Saudi Arabia
o UAE
o Qatar
o South Africa
o Rest of Middle East & Africa

What our report offers:
- Market share assessments for the regional and country-level segments
- Strategic recommendations for the new entrants
- Covers Market data for the years 2020, 2021, 2022, 2025, and 2028
- Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
- Strategic recommendations in key business segments based on the market estimations
- Competitive landscaping mapping the key common trends
- Company profiling with detailed strategies, financials, and recent developments
- Supply chain trends mapping the latest technological advancements

Free Customization Offerings:
All the customers of this report will be entitled to receive one of the following free customization options:
• Company Profiling
o Comprehensive profiling of additional market players (up to 3)
o SWOT Analysis of key players (up to 3)
• Regional Segmentation
o Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
• Competitive Benchmarking
o Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary   
    
2 Preface   

 2.1 Abstract  
 2.2 Stake Holders  
 2.3 Research Scope  
 2.4 Research Methodology  
  2.4.1 Data Mining 
  2.4.2 Data Analysis 
  2.4.3 Data Validation 
  2.4.4 Research Approach 
 2.5 Research Sources  
  2.5.1 Primary Research Sources 
  2.5.2 Secondary Research Sources 
  2.5.3 Assumptions 
    
3 Market Trend Analysis   
 3.1 Introduction  
 3.2 Drivers  
 3.3 Restraints  
 3.4 Opportunities  
 3.5 Threats  
 3.6 Application Analysis  
 3.7 End User Analysis  
 3.8 Emerging Markets  
 3.9 Impact of Covid-19  
    
4 Porters Five Force Analysis   
 4.1 Bargaining power of suppliers  
 4.2 Bargaining power of buyers  
 4.3 Threat of substitutes  
 4.4 Threat of new entrants  
 4.5 Competitive rivalry  
    
5 Global Artificial Intelligence (AI) in Fintech Market, By Place of Deployment   

 5.1 Introduction  
 5.2 Front Office   
 5.3 Back Office              
    
6 Global Artificial Intelligence (AI) in Fintech Market, By Component   
 6.1 Introduction  
 6.2 Hardware  
 6.3 Services             
  6.3.1 Managed Services           
  6.3.2 Professional Services           
  6.3.3 Consultancy Services           
 6.4 Solutions             
  6.4.1 Platforms           
  6.4.2 Software Tools           
   6.4.2.1 Data Discovery         
   6.4.2.2 Data Quality and Data Governance         
   6.4.2.3 Data Visualization         
  6.4.3 Intelligent Bots           
  6.4.4 Personalized Banking           
  6.4.5 Process Automation           
  6.4.6 Robot-Advisory           
  6.4.7 Trading           
    
7 Global Artificial Intelligence (AI) in Fintech Market, By Deployment   

 7.1 Introduction  
 7.2 On-Premise             
 7.3 Cloud  
    
8 Global Artificial Intelligence (AI) in Fintech Market, By Machine Learning Applications   
 8.1 Introduction  
 8.2 Deep Learning             
 8.3 Machine Vision  
 8.4 Natural Language Processing             
 8.5 Reinforced Learning             
 8.6 Semi-Supervised Learning             
 8.7 Supervised Learning             
 8.8 Unsupervised Learning             
    
9 Global Artificial Intelligence (AI) in Fintech Market, By Application   
 9.1 Introduction  
 9.2 Business Analytics and Reporting             
  9.2.1 Regulatory and Compliance Management           
  9.2.2 Predictive Analytics           
 9.3 Virtual Assistants (Chatbots)             
 9.4 Customer Behavioural Analytics             
  9.4.1 Asset and Portfolio Management           
  9.4.2 Credit Scoring           
  9.4.3 Debt Collection           
  9.4.4 Insurance Premium           
 9.5 Risk Investigation             
 9.6 Quantitative and Asset Management             
 9.7 Fraud Detection             
 9.8 Know Your Customer (KYC)             
 9.9 Accounting             
 9.10 Customer Service             
 9.11 Peer-to-Peer (P2P) Lending         
 9.12 Insurance Support             
 9.13 Network Security             
 9.14 Capital Markets             
 9.15 Other Applications             
  9.15.1 Market Research           
  9.15.2 Marketing Campaign           
  9.15.3 Financial Market Prediction 
  9.15.4 Advertising           
    
10 Global Artificial Intelligence (AI) in Fintech Market, By End User   
 10.1 Introduction  
 10.2 Retail Banking             
 10.3 Hedge Funds             
 10.4 Stock Trading Firms             
 10.5 Investment Banking  
    
11 Global Artificial Intelligence (AI) in Fintech Market, By Geography   
 11.1 Introduction  
 11.2 North America  
  11.2.1 US 
  11.2.2 Canada 
  11.2.3 Mexico 
 11.3 Europe  
  11.3.1 Germany 
  11.3.2 UK 
  11.3.3 Italy 
  11.3.4 France 
  11.3.5 Spain 
  11.3.6 Rest of Europe 
 11.4 Asia Pacific  
  11.4.1 Japan 
  11.4.2 China 
  11.4.3 India 
  11.4.4 Australia 
  11.4.5 New Zealand 
  11.4.6 South Korea 
  11.4.7 Rest of Asia Pacific 
 11.5 South America  
  11.5.1 Argentina 
  11.5.2 Brazil 
  11.5.3 Chile 
  11.5.4 Rest of South America 
 11.6 Middle East & Africa  
  11.6.1 Saudi Arabia 
  11.6.2 UAE 
  11.6.3 Qatar 
  11.6.4 South Africa 
  11.6.5 Rest of Middle East & Africa 
    
12 Key Developments   
 12.1 Agreements, Partnerships, Collaborations and Joint Ventures  
 12.2 Acquisitions & Mergers  
 12.3 New Product Launch  
 12.4 Expansions  
 12.5 Other Key Strategies  
    
13 Company Profiling   
 13.1 Ripple Labs Inc.             
 13.2 Active.Ai             
 13.3 TIBCO Software (Alpine Data Labs)             
 13.4 Trifacta Software Inc.             
 13.5 Data Minr Inc.             
 13.6 Zeitgold GmbH             
 13.7 Sift Science Inc.             
 13.8 Pefin Holdings LLC             
 13.9 Betterment Holdings             
 13.10 WealthFront Inc.             
 13.11 Intel Corporation             
 13.12 Sentifi AG             
 13.13 ComplyAdvantage.com             
 13.14 Narrative Science             
 13.15 Amazon Web Services Inc.             
 13.16 IPsoft Inc.             
 13.17 Next IT Corporation             
 13.18 Microsoft Corporation             
 13.19 Onfido             
 13.20 IBM Corporation  


List of Tables    
1 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Region (2020-2028) ($MN)   
2 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Place of Deployment (2020-2028) ($MN)   
3 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Front Office  (2020-2028) ($MN)   
4 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Back Office (2020-2028) ($MN)   
5 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Component (2020-2028) ($MN)   
6 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Hardware (2020-2028) ($MN)   
7 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Services (2020-2028) ($MN)   
8 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Managed Services (2020-2028) ($MN)   
9 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Professional Services (2020-2028) ($MN)   
10 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Consultancy Services (2020-2028) ($MN)   
11 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Solutions (2020-2028) ($MN)   
12 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Platforms (2020-2028) ($MN)   
13 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Software Tools (2020-2028) ($MN)   
14 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Intelligent Bots (2020-2028) ($MN)   
15 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Personalized Banking (2020-2028) ($MN)   
16 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Process Automation (2020-2028) ($MN)   
17 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Robot-Advisory (2020-2028) ($MN)   
18 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Trading (2020-2028) ($MN)   
19 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Deployment (2020-2028) ($MN)   
20 Global Artificial Intelligence (AI) in Fintech Market Outlook, By On-Premise (2020-2028) ($MN)   
21 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Cloud (2020-2028) ($MN)   
22 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Machine Learning Applications (2020-2028) ($MN)   
23 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Deep Learning (2020-2028) ($MN)   
24 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Machine Vision (2020-2028) ($MN)   
25 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Natural Language Processing (2020-2028) ($MN)   
26 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Reinforced Learning (2020-2028) ($MN)   
27 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Semi-Supervised Learning (2020-2028) ($MN)   
28 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Supervised Learning (2020-2028) ($MN)   
29 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Unsupervised Learning (2020-2028) ($MN)   
30 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Application (2020-2028) ($MN)   
31 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Business Analytics and Reporting (2020-2028) ($MN)   
32 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Regulatory and Compliance Management (2020-2028) ($MN)   
33 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Predictive Analytics (2020-2028) ($MN)   
34 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Virtual Assistants (Chatbots) (2020-2028) ($MN)   
35 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Customer Behavioural Analytics (2020-2028) ($MN)   
36 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Asset and Portfolio Management (2020-2028) ($MN)   
37 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Credit Scoring (2020-2028) ($MN)   
38 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Debt Collection (2020-2028) ($MN)   
39 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Insurance Premium (2020-2028) ($MN)   
40 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Risk Investigation (2020-2028) ($MN)   
41 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Quantitative and Asset Management (2020-2028) ($MN)   
42 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Fraud Detection (2020-2028) ($MN)   
43 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Know Your Customer (KYC) (2020-2028) ($MN)   
44 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Accounting (2020-2028) ($MN)   
45 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Customer Service (2020-2028) ($MN)   
46 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Peer-to-Peer (P2P) Lending (2020-2028) ($MN)   
47 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Insurance Support (2020-2028) ($MN)   
48 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Network Security (2020-2028) ($MN)   
49 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Capital Markets (2020-2028) ($MN)   
50 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Other Applications (2020-2028) ($MN)   
51 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Market Research (2020-2028) ($MN)   
52 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Marketing Campaign (2020-2028) ($MN)   
53 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Financial Market Prediction (2020-2028) ($MN)   
54 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Advertising (2020-2028) ($MN)   
55 Global Artificial Intelligence (AI) in Fintech Market Outlook, By End User (2020-2028) ($MN)   
56 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Retail Banking (2020-2028) ($MN)   
57 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Hedge Funds (2020-2028) ($MN)   
58 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Stock Trading Firms (2020-2028) ($MN)   
59 Global Artificial Intelligence (AI) in Fintech Market Outlook, By Investment Banking (2020-2028) ($MN)   
    
Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.    

List of Figures

RESEARCH METHODOLOGY


Research Methodology

We at Stratistics opt for an extensive research approach which involves data mining, data validation, and data analysis. The various research sources include in-house repository, secondary research, competitor’s sources, social media research, client internal data, and primary research.

Our team of analysts prefers the most reliable and authenticated data sources in order to perform the comprehensive literature search. With access to most of the authenticated data bases our team highly considers the best mix of information through various sources to obtain extensive and accurate analysis.

Each report takes an average time of a month and a team of 4 industry analysts. The time may vary depending on the scope and data availability of the desired market report. The various parameters used in the market assessment are standardized in order to enhance the data accuracy.

Data Mining

The data is collected from several authenticated, reliable, paid and unpaid sources and is filtered depending on the scope & objective of the research. Our reports repository acts as an added advantage in this procedure. Data gathering from the raw material suppliers, distributors and the manufacturers is performed on a regular basis, this helps in the comprehensive understanding of the products value chain. Apart from the above mentioned sources the data is also collected from the industry consultants to ensure the objective of the study is in the right direction.

Market trends such as technological advancements, regulatory affairs, market dynamics (Drivers, Restraints, Opportunities and Challenges) are obtained from scientific journals, market related national & international associations and organizations.

Data Analysis

From the data that is collected depending on the scope & objective of the research the data is subjected for the analysis. The critical steps that we follow for the data analysis include:

  • Product Lifecycle Analysis
  • Competitor analysis
  • Risk analysis
  • Porters Analysis
  • PESTEL Analysis
  • SWOT Analysis

The data engineering is performed by the core industry experts considering both the Marketing Mix Modeling and the Demand Forecasting. The marketing mix modeling makes use of multiple-regression techniques to predict the optimal mix of marketing variables. Regression factor is based on a number of variables and how they relate to an outcome such as sales or profits.


Data Validation

The data validation is performed by the exhaustive primary research from the expert interviews. This includes telephonic interviews, focus groups, face to face interviews, and questionnaires to validate our research from all aspects. The industry experts we approach come from the leading firms, involved in the supply chain ranging from the suppliers, distributors to the manufacturers and consumers so as to ensure an unbiased analysis.

We are in touch with more than 15,000 industry experts with the right mix of consultants, CEO's, presidents, vice presidents, managers, experts from both supply side and demand side, executives and so on.

The data validation involves the primary research from the industry experts belonging to:

  • Leading Companies
  • Suppliers & Distributors
  • Manufacturers
  • Consumers
  • Industry/Strategic Consultants

Apart from the data validation the primary research also helps in performing the fill gap research, i.e. providing solutions for the unmet needs of the research which helps in enhancing the reports quality.


For more details about research methodology, kindly write to us at info@strategymrc.com

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