Global Machine Learning in Finance 麻豆原创 Research Report 2024

Publication ID:

PB501245089

Publication Date:

February 2, 2024

Pages:

Publisher:

MRRPB5

Countries:

Global [1]

USD 2,900.00

IT Services [2]

Computing & Electronics [3]

Description:

The value of machine learning in finance is becoming more apparent by the day. As banks and other financial institutions strive to beef up security, streamline processes, and improve financial analysis, ML is becoming the technology of choice.
The global Machine Learning in Finance market was valued at US$ 546 million in 2023 and is anticipated to reach US$ 1194.9 million by 2030, witnessing a CAGR of 12.9% during the forecast period 2024-2030.
North American market for Machine Learning in Finance is estimated to increase from $ million in 2023 to reach $ million by 2030, at a CAGR of % during the forecast period of 2024 through 2030.
Asia-Pacific market for Machine Learning in Finance is estimated to increase from $ million in 2023 to reach $ million by 2030, at a CAGR of % during the forecast period of 2024 through 2030.
The global market for Machine Learning in Finance in Banks is estimated to increase from $ million in 2023 to $ million by 2030, at a CAGR of % during the forecast period of 2024 through 2030.
The major global companies of Machine Learning in Finance include Ignite Ltd, Yodlee, Trill A.I., MindTitan, Accenture and ZestFinance, etc. In 2023, the world's top three vendors accounted for approximately % of the revenue.
This report aims to provide a comprehensive presentation of the global market for Machine Learning in Finance, with both quantitative and qualitative analysis, to help readers develop business/growth strategies, assess the market competitive situation, analyze their position in the current marketplace, and make informed business decisions regarding Machine Learning in Finance.
Report Scope
The Machine Learning in Finance market size, estimations, and forecasts are provided in terms of revenue ($ millions), considering 2023 as the base year, with history and forecast data for the period from 2019 to 2030. This report segments the global Machine Learning in Finance market comprehensively. Regional market sizes, concerning products by Type, by Application, and by players, are also provided.
For a more in-depth understanding of the market, the report provides profiles of the competitive landscape, key competitors, and their respective market ranks. The report also discusses technological trends and new product developments.
The report will help the Machine Learning in Finance companies, new entrants, and industry chain related companies in this market with information on the revenues, sales volume, and average price for the overall market and the sub-segments across the different segments, by company, by Type, by Application, and by regions.
麻豆原创 Segmentation
By Company
Ignite Ltd
Yodlee
Trill A.I.
MindTitan
Accenture
ZestFinance
Segment by Type
Supervised Learning
Unsupervised Learning
Semi Supervised Learning
Reinforced Leaning
Segment by Application
Banks
Securities Company
Others
By Region
North America
United States
Canada
Europe
Germany
France
UK
Italy
Russia
Nordic Countries
Rest of Europe
Asia-Pacific
China
Japan
South Korea
Southeast Asia
India
Australia
Rest of Asia
Latin America
Mexico
Brazil
Rest of Latin America
Middle East & Africa
Turkey
Saudi Arabia
UAE
Rest of MEA
Chapter Outline
Chapter 1: Introduces the report scope of the report, executive summary of different market segments (by Type, by Application, etc), including the market size of each market segment, future development potential, and so on. It offers a high-level view of the current state of the market and its likely evolution in the short to mid-term, and long term.
Chapter 2: Introduces executive summary of global market size, regional market size, this section also introduces the market dynamics, latest developments of the market, the driving factors and restrictive factors of the market, the challenges and risks faced by companies in the industry, and the analysis of relevant policies in the industry.
Chapter 3: Detailed analysis of Machine Learning in Finance companies鈥� competitive landscape, revenue market share, latest development plan, merger, and acquisition information, etc.
Chapter 4: Provides the analysis of various market segments by Type, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different market segments.
Chapter 5: Provides the analysis of various market segments by Application, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different downstream markets.
Chapter 6, 7, 8, 9, 10: North America, Europe, Asia Pacific, Latin America, Middle East and Africa segment by country. It provides a quantitative analysis of the market size and development potential of each region and its main countries and introduces the market development, future development prospects, market space, and capacity of each country in the world.
Chapter 11: Provides profiles of key players, introducing the basic situation of the main companies in the market in detail, including product sales, revenue, price, gross margin, product introduction, recent development, etc.
Chapter 12: The main points and conclusions of the report.

Please Note - This is an on demand report and will be delivered in 2 business days (48 hours) post payment.

Table Of Contents:

1 Report Overview
1.1 Study Scope
1.2 麻豆原创 Analysis by Type
1.2.1 Global Machine Learning in Finance 麻豆原创 Size Growth Rate by Type: 2019 VS 2023 VS 2030
1.2.2 Supervised Learning
1.2.3 Unsupervised Learning
1.2.4 Semi Supervised Learning
1.2.5 Reinforced Leaning
1.3 麻豆原创 by Application
1.3.1 Global Machine Learning in Finance 麻豆原创 Growth by Application: 2019 VS 2023 VS 2030
1.3.2 Banks
1.3.3 Securities Company
1.3.4 Others
1.4 Study Objectives
1.5 Years Considered
1.6 Years Considered
2 Global Growth Trends
2.1 Global Machine Learning in Finance 麻豆原创 Perspective (2019-2030)
2.2 Machine Learning in Finance Growth Trends by Region
2.2.1 Global Machine Learning in Finance 麻豆原创 Size by Region: 2019 VS 2023 VS 2030
2.2.2 Machine Learning in Finance Historic 麻豆原创 Size by Region (2019-2024)
2.2.3 Machine Learning in Finance Forecasted 麻豆原创 Size by Region (2025-2030)
2.3 Machine Learning in Finance 麻豆原创 Dynamics
2.3.1 Machine Learning in Finance Industry Trends
2.3.2 Machine Learning in Finance 麻豆原创 Drivers
2.3.3 Machine Learning in Finance 麻豆原创 Challenges
2.3.4 Machine Learning in Finance 麻豆原创 Restraints
3 Competition Landscape by Key Players
3.1 Global Top Machine Learning in Finance Players by Revenue
3.1.1 Global Top Machine Learning in Finance Players by Revenue (2019-2024)
3.1.2 Global Machine Learning in Finance Revenue 麻豆原创 Share by Players (2019-2024)
3.2 Global Machine Learning in Finance 麻豆原创 Share by Company Type (Tier 1, Tier 2, and Tier 3)
3.3 Players Covered: Ranking by Machine Learning in Finance Revenue
3.4 Global Machine Learning in Finance 麻豆原创 Concentration Ratio
3.4.1 Global Machine Learning in Finance 麻豆原创 Concentration Ratio (CR5 and HHI)
3.4.2 Global Top 10 and Top 5 Companies by Machine Learning in Finance Revenue in 2023
3.5 Machine Learning in Finance Key Players Head office and Area Served
3.6 Key Players Machine Learning in Finance Product Solution and Service
3.7 Date of Enter into Machine Learning in Finance 麻豆原创
3.8 Mergers & Acquisitions, Expansion Plans
4 Machine Learning in Finance Breakdown Data by Type
4.1 Global Machine Learning in Finance Historic 麻豆原创 Size by Type (2019-2024)
4.2 Global Machine Learning in Finance Forecasted 麻豆原创 Size by Type (2025-2030)
5 Machine Learning in Finance Breakdown Data by Application
5.1 Global Machine Learning in Finance Historic 麻豆原创 Size by Application (2019-2024)
5.2 Global Machine Learning in Finance Forecasted 麻豆原创 Size by Application (2025-2030)
6 North America
6.1 North America Machine Learning in Finance 麻豆原创 Size (2019-2030)
6.2 North America Machine Learning in Finance 麻豆原创 Growth Rate by Country: 2019 VS 2023 VS 2030
6.3 North America Machine Learning in Finance 麻豆原创 Size by Country (2019-2024)
6.4 North America Machine Learning in Finance 麻豆原创 Size by Country (2025-2030)
6.5 United States
6.6 Canada
7 Europe
7.1 Europe Machine Learning in Finance 麻豆原创 Size (2019-2030)
7.2 Europe Machine Learning in Finance 麻豆原创 Growth Rate by Country: 2019 VS 2023 VS 2030
7.3 Europe Machine Learning in Finance 麻豆原创 Size by Country (2019-2024)
7.4 Europe Machine Learning in Finance 麻豆原创 Size by Country (2025-2030)
7.5 Germany
7.6 France
7.7 U.K.
7.8 Italy
7.9 Russia
7.10 Nordic Countries
8 Asia-Pacific
8.1 Asia-Pacific Machine Learning in Finance 麻豆原创 Size (2019-2030)
8.2 Asia-Pacific Machine Learning in Finance 麻豆原创 Growth Rate by Region: 2019 VS 2023 VS 2030
8.3 Asia-Pacific Machine Learning in Finance 麻豆原创 Size by Region (2019-2024)
8.4 Asia-Pacific Machine Learning in Finance 麻豆原创 Size by Region (2025-2030)
8.5 China
8.6 Japan
8.7 South Korea
8.8 Southeast Asia
8.9 India
8.10 Australia
9 Latin America
9.1 Latin America Machine Learning in Finance 麻豆原创 Size (2019-2030)
9.2 Latin America Machine Learning in Finance 麻豆原创 Growth Rate by Country: 2019 VS 2023 VS 2030
9.3 Latin America Machine Learning in Finance 麻豆原创 Size by Country (2019-2024)
9.4 Latin America Machine Learning in Finance 麻豆原创 Size by Country (2025-2030)
9.5 Mexico
9.6 Brazil
10 Middle East & Africa
10.1 Middle East & Africa Machine Learning in Finance 麻豆原创 Size (2019-2030)
10.2 Middle East & Africa Machine Learning in Finance 麻豆原创 Growth Rate by Country: 2019 VS 2023 VS 2030
10.3 Middle East & Africa Machine Learning in Finance 麻豆原创 Size by Country (2019-2024)
10.4 Middle East & Africa Machine Learning in Finance 麻豆原创 Size by Country (2025-2030)
10.5 Turkey
10.6 Saudi Arabia
10.7 UAE
11 Key Players Profiles
11.1 Ignite Ltd
11.1.1 Ignite Ltd Company Detail
11.1.2 Ignite Ltd Business Overview
11.1.3 Ignite Ltd Machine Learning in Finance Introduction
11.1.4 Ignite Ltd Revenue in Machine Learning in Finance Business (2019-2024)
11.1.5 Ignite Ltd Recent Development
11.2 Yodlee
11.2.1 Yodlee Company Detail
11.2.2 Yodlee Business Overview
11.2.3 Yodlee Machine Learning in Finance Introduction
11.2.4 Yodlee Revenue in Machine Learning in Finance Business (2019-2024)
11.2.5 Yodlee Recent Development
11.3 Trill A.I.
11.3.1 Trill A.I. Company Detail
11.3.2 Trill A.I. Business Overview
11.3.3 Trill A.I. Machine Learning in Finance Introduction
11.3.4 Trill A.I. Revenue in Machine Learning in Finance Business (2019-2024)
11.3.5 Trill A.I. Recent Development
11.4 MindTitan
11.4.1 MindTitan Company Detail
11.4.2 MindTitan Business Overview
11.4.3 MindTitan Machine Learning in Finance Introduction
11.4.4 MindTitan Revenue in Machine Learning in Finance Business (2019-2024)
11.4.5 MindTitan Recent Development
11.5 Accenture
11.5.1 Accenture Company Detail
11.5.2 Accenture Business Overview
11.5.3 Accenture Machine Learning in Finance Introduction
11.5.4 Accenture Revenue in Machine Learning in Finance Business (2019-2024)
11.5.5 Accenture Recent Development
11.6 ZestFinance
11.6.1 ZestFinance Company Detail
11.6.2 ZestFinance Business Overview
11.6.3 ZestFinance Machine Learning in Finance Introduction
11.6.4 ZestFinance Revenue in Machine Learning in Finance Business (2019-2024)
11.6.5 ZestFinance Recent Development
12 Analyst's Viewpoints/Conclusions
13 Appendix
13.1 Research Methodology
13.1.1 Methodology/Research Approach
13.1.2 Data Source
13.2 Disclaimer
13.3 Author Details

Companies Mentioned:

Ignite Ltd
Yodlee
Trill A.I.
MindTitan
Accenture
ZestFinance

License Types:

Single User License (PDF)

This license allows for use of a publication by one person.
This person may print out a single copy of the publication.
This person can include information given in the publication in presentations and internal reports by providing full copyright credit to the publisher.
This person cannot share the publication (or any information contained therein) with any other person or persons.
Unless a Enterprise License is purchased, a Single User License must be purchased for every person that wishes to use the publication within the same organization.
Customers who infringe these license terms are liable for a Global license fee.

听

Site License (PDF)*

This license allows for use of a publication by all users within one corporate location, e.g. a regional office.
These users may print out a single copy of the publication.
These users can include information given in the publication in presentations and internal reports by providing full copyright credit to the publisher.
These users cannot share the publication (or any information contained therein) with any other person or persons outside the corporate location for which the publication is purchased.
Unless a Enterprise License is purchased, a Site User License must be purchased for every corporate location by an organization that wishes to use the publication within the same organization.
Customers who infringe these license terms are liable for a Global license fee.

听

Global License (PDF)*

This license allows for use of a publication by unlimited users within the purchasing organization e.g. all employees of a single company.
Each of these people may use the publication on any computer, and may print out the report, but may not share the publication (or any information contained therein) with any other person or persons outside of the organization.
These employees of purchasing organization can include information given in the publication in presentations and internal reports by providing full copyright credit to the publisher.

*If Applicable.