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Global Machine Learning in Banking Market to reach USD XX million by 2028.

Global Machine Learning in Banking Market Size study, By Component (Solution, Service), by Enterprise Size (Large Enterprises, Small and Medium-sized Enterprises (SMEs)), by Application (Credit Scoring, Risk Management Compliance and Security, Payments and Transactions, Customer Service), and Regional Forecasts 2022-2028

Product Code: BFBFSI-80574504
Publish Date: 30-08-2022
Page: 200

Global Machine Learning in Banking Market is valued approximately USD XX million in 2021 and is anticipated to grow with a healthy growth rate of more than XX % over the forecast period 2022-2028.

In Banking sector Machine Learning solutions are utilized for automating different applications such as fraud prevention, anomaly detection, credit scoring, anti-money laundering and kyc process, payment processing, onboarding & document processing, and process automation among others. The rising expansion of banking sector worldwide and increasing adoption of AI and ML based services as well as recent strategic partnership from leading market players are factors that are accelerating the global market demand. For instance, according to India Brand Equity Forum (IBEF) – during FY 2020, total deposits in banking were estimated at USD 1936.29 billion, and the deposits are projected to grow to USD 2101.93 billion by end of 2022. Furthermore, leading market players are working towards strategic initiatives to leverage the growing adoption of Machine learning solutions in banking sector. For instance, in February 2022, Dubai, United Arab Emirates based Mashreq Bank announced a new partnership with Israel based ThetaRay, a fintech software and big data analytics solution provider. Through this collaboration the bank would utilize ThetaRay’s AI-driven solution. Moreover, in July 2022, Bentonville, Arkansas, United States based Arvest Bank entered in a five-year partnership with Google Cloud. Under this partnership the bank would leverage Google Cloud’s artificial intelligence (AI) and machine learning (ML) tools to enhance customer experience and streamline its banking services. Also, growing digitization in BFSI sector coupled with increasing emergence of neo banks are anticipated to act as a catalyzing factor for the market demand during the forecast period. However, a high deployment cost associated with AI and ML solutions impedes the growth of the market over the forecast period of 2022-2028.

The key regions considered for the global Machine Learning in Banking Market study include Asia Pacific, North America, Europe, Latin America, and the Rest of the World. North America is the leading region across the world in terms of market share owing to the growing number of governance and regulatory compliances and presence of leading financial institutions in the region. Whereas, Asia Pacific is anticipated to exhibit a significant growth rate over the forecast period 2022-2028. Factors such as the thriving growth of BFSI industry and increasing penetration of leading market players in the region, would create lucrative growth prospects for the global Machine Learning in Banking Market across the Asia Pacific region.

Major market players included in this report are:
Affirm Inc.
Amazon Web Services, Inc.
Big ML, Inc.
Cisco Systems, Inc.
Google LLC

The objective of the study is to define market sizes of different segments & countries in recent years and to forecast the values to the coming eight years. The report is designed to incorporate both qualitative and quantitative aspects of the industry within each of the regions and countries involved in the study. Furthermore, the report also caters the detailed information about the crucial aspects such as driving factors & challenges which will define the future growth of the market. Additionally, the report shall also incorporate available opportunities in micro markets for stakeholders to invest along with the detailed analysis of competitive landscape and product offerings of key players. The detailed segments and sub-segment of the market are explained below:
By Component
By Enterprise Size
Large Enterprises
Small and Medium-sized Enterprises (SMEs)
By Application
Credit Scoring
Risk Management Compliance and Security
Payments and Transactions
Customer Service
By Region:
North America

Asia Pacific
South Korea
Latin America
Rest of the World

Furthermore, years considered for the study are as follows:

Historical year – 2018, 2019, 2020
Base year – 2021
Forecast period – 2022 to 2028

Target Audience of the Global Machine Learning in Banking Market in Market Study:

Key Consulting Companies & Advisors
Large, medium-sized, and small enterprises
Venture capitalists
Value-Added Resellers (VARs)
Third-party knowledge providers
Investment bankers

Chapter 1. Executive Summary
1.1. Market Snapshot
1.2. Global & Segmental Market Estimates & Forecasts, 2020-2028 (USD Million)
1.2.1. Global Machine Learning in Banking Market, by Region, 2020-2028 (USD Million)
1.2.2. Global Machine Learning in Banking Market, by Component, 2020-2028 (USD Million)
1.2.3. Global Machine Learning in Banking Market, by Enterprise Size, 2020-2028 (USD Million)
1.2.4. Global Machine Learning in Banking Market, by Application, 2020-2028 (USD Million)
1.3. Key Trends
1.4. Estimation Methodology
1.5. Research Assumption
Chapter 2. Global Machine Learning in Banking Market Definition and Scope
2.1. Objective of the Study
2.2. Market Definition & Scope
2.2.1. Scope of the Study
2.2.2. Industry Evolution
2.3. Years Considered for the Study
2.4. Currency Conversion Rates
Chapter 3. Global Machine Learning in Banking Market Dynamics
3.1. Machine Learning in Banking Market Impact Analysis (2020-2028)
3.1.1. Market Drivers Rising expansion of banking sector worldwide. Increasing adoption of AI and ML based services. Recent strategic partnership from leading market players.
3.1.2. Market Challenges High deployment cost associated with AI and ML solutions.
3.1.3. Market Opportunities Growing digitization in BFSI sector. Increasing emergence of neo banks
Chapter 4. Global Machine Learning in Banking Market Industry Analysis
4.1. Porter’s 5 Force Model
4.1.1. Bargaining Power of Suppliers
4.1.2. Bargaining Power of Buyers
4.1.3. Threat of New Entrants
4.1.4. Threat of Substitutes
4.1.5. Competitive Rivalry
4.1.6. Futuristic Approach to Porter’s 5 Force Model (2018-2028)
4.2. PEST Analysis
4.2.1. Political
4.2.2. Economical
4.2.3. Social
4.2.4. Technological
4.3. Investment Adoption Model
4.4. Analyst Recommendation & Conclusion
4.5. Top investment opportunity
4.6. Top winning strategies
Chapter 5. Risk Assessment: COVID-19 Impact
5.1.1. Assessment of the overall impact of COVID-19 on the industry
5.1.2. Pre COVID-19 and post COVID-19 Market scenario
Chapter 6. Global Machine Learning in Banking Market, by Component
6.1. Market Snapshot
6.2. Global Machine Learning in Banking Market by Component, Performance – Potential Analysis
6.3. Global Machine Learning in Banking Market Estimates & Forecasts by Component 2018-2028 (USD Million)
6.4. Machine Learning in Banking Market, Sub Segment Analysis
6.4.1. Solution
6.4.2. Service
Chapter 7. Global Machine Learning in Banking Market, by Enterprise Size
7.1. Market Snapshot
7.2. Global Machine Learning in Banking Market by Enterprise Size, Performance – Potential Analysis
7.3. Global Machine Learning in Banking Market Estimates & Forecasts by Enterprise Size 2018-2028 (USD Million)
7.4. Machine Learning in Banking Market, Sub Segment Analysis
7.4.1. Large Enterprises
7.4.2. Small and Medium-sized Enterprises (SMEs)
Chapter 8. Global Machine Learning in Banking Market, by Application
8.1. Market Snapshot
8.2. Global Machine Learning in Banking Market by Application, Performance – Potential Analysis
8.3. Global Machine Learning in Banking Market Estimates & Forecasts by Application 2018-2028 (USD Million)
8.4. Machine Learning in Banking Market, Sub Segment Analysis
8.4.1. Credit Scoring
8.4.2. Risk Management Compliance and Security
8.4.3. Payments and Transactions
8.4.4. Customer Service
Chapter 9. Global Machine Learning in Banking Market, Regional Analysis
9.1. Machine Learning in Banking Market, Regional Market Snapshot
9.2. North America Machine Learning in Banking Market
9.2.1. U.S. Machine Learning in Banking Market Component estimates & forecasts, 2018-2028 Enterprise Size estimates & forecasts, 2018-2028 Application estimates & forecasts, 2018-2028
9.2.2. Canada Machine Learning in Banking Market
9.3. Europe Machine Learning in Banking Market Snapshot
9.3.1. U.K. Machine Learning in Banking Market
9.3.2. Germany Machine Learning in Banking Market
9.3.3. France Machine Learning in Banking Market
9.3.4. Spain Machine Learning in Banking Market
9.3.5. Italy Machine Learning in Banking Market
9.3.6. Rest of Europe Machine Learning in Banking Market
9.4. Asia-Pacific Machine Learning in Banking Market Snapshot
9.4.1. China Machine Learning in Banking Market
9.4.2. India Machine Learning in Banking Market
9.4.3. Japan Machine Learning in Banking Market
9.4.4. Australia Machine Learning in Banking Market
9.4.5. South Korea Machine Learning in Banking Market
9.4.6. Rest of Asia Pacific Machine Learning in Banking Market
9.5. Latin America Machine Learning in Banking Market Snapshot
9.5.1. Brazil Machine Learning in Banking Market
9.5.2. Mexico Machine Learning in Banking Market
9.6. Rest of The World Machine Learning in Banking Market

Chapter 10. Competitive Intelligence
10.1. Top Market Strategies
10.2. Company Profiles
10.2.1. Affirm, Inc. Key Information Overview Financial (Subject to Data Availability) Product Summary Recent Developments
10.2.2. Amazon Web Services, Inc.
10.2.3. Big ML, Inc.
10.2.4. Cisco Systems, Inc.
10.2.5. FICO
10.2.6. Google LLC
10.2.7. Mindtree
10.2.8. Microsoft
10.2.9. SAP SE
10.2.10. SPD-Group
Chapter 11. Research Process
11.1. Research Process
11.1.1. Data Mining
11.1.2. Analysis
11.1.3. Market Estimation
11.1.4. Validation
11.1.5. Publishing
11.2. Research Attributes
11.3. Research Assumption

At Bizwit Research and Consultancy, we employ a thorough and iterative research methodology with the goal of minimizing discrepancies, ensuring the provision of highly accurate estimates and predictions over the forecast period. Our approach involves a combination of bottom-up and top-down strategies to effectively segment and estimate quantitative aspects of the market, utilizing our proprietary data & AI tools. Our Proprietary Tools allow us for the creation of customized models specific to the research objectives. This enables us to develop tailored statistical models and forecasting algorithms to estimate market trends, future growth, or consumer behavior. The customization enhances the accuracy and relevance of the research findings.
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Critical elements of methodology employed for all our studies include:
Data Collection:
To determine the appropriate methods of data collection based on the research objectives, we consider both primary and secondary sources. Primary data collection involves gathering information directly from various industry experts in core and related fields, original equipment manufacturers (OEMs), vendors, suppliers, technology developers, alliances, and organizations. These sources encompass all segments of the value chain within the specific industry. Through in-depth interviews, we engage with key industry participants, subject-matter experts, C-level executives of major market players, industry consultants, and other relevant experts. This allows us to obtain and validate critical qualitative and quantitative information while evaluating market prospects. AI and Big Data are instrumental in our primary research, providing us with powerful tools to collect, analyze, and derive insights from data efficiently. These technologies contribute to the advancement of research methodologies, enabling us to make data-driven decisions and uncover valuable findings.
In addition to primary sources, we extensively utilize secondary sources to enhance our research. These include directories, databases, journals focusing on related industries, company newsletters, and information portals such as Bloomberg, D&B Hoovers, and Factiva. These secondary sources enable us to identify and gather valuable information for our comprehensive, technical, market-oriented, and commercial study of the market. Additionally, we utilize AI algorithms to automate the collection of vast amounts of data from various sources such as surveys, social media platforms, online transactions, and web scraping. And employ Big Data technologies for storage and processing of large datasets, ensuring that no valuable information is missed during the data collection process.
Data Analysis:
Our team of experts carefully examine the gathered data using suitable statistical techniques and qualitative analysis methods. For quantitative analysis, we employ descriptive statistics, regression analysis, and other advanced statistical methods, depending on the characteristics of the data. This analysis may also incorporate the utilization of AI tools and big data analysis techniques to extract meaningful insights.
To ensure the accuracy and reliability of our findings, we extensively leverage data science techniques, which help us minimize discrepancies and uncertainties in our analysis. We employ Data Science to clean and preprocess the data, ensuring its quality and reliability. This involves handling missing data, removing outliers, standardizing variables, and transforming data into suitable formats for analysis. The application of data science techniques enhances our accuracy, efficiency, and depth of analysis, enabling us to stay competitive in dynamic market environments.
Market Size Estimation:
Our proprietary data tools play a crucial role in deriving our market estimates and forecasts. Each study involves the creation of a unique and customized model. The model incorporates the gathered information on market dynamics, technology landscape, application development, and pricing trends. AI techniques, such as machine learning and deep learning, aid us to analyze patterns within the data to identify correlations, trends, and relationships. By recognizing patterns in consumer behavior, purchasing habits, or market dynamics, our AI algorithms aid us in more precise estimations of market size. These factors are simultaneously analyzed within the model, allowing for a comprehensive assessment. To quantify their impact over the forecast period, correlation, regression, and time series analysis are employed.
To estimate and validate the market size, we employ both top-down and bottom-up approaches. The preference is given to a bottom-up approach, where key regional markets are analyzed as separate entities. This data is then integrated to obtain global estimates. This approach is crucial as it provides a deep understanding of the industry and helps minimize errors.
In our forecasting process, we consider various parameters such as economic tools, technological analysis, industry experience, and domain expertise. By taking all these factors into account, we strive to produce accurate and reliable market forecasts. When forecasting, we take into consideration several parameters, which include:
Market driving trends and favorable economic conditions
Restraints and challenges that are expected to be encountered during the forecast period.
Anticipated opportunities for growth and development
Technological advancements and projected developments in the market
Consumer spending trends and dynamics
Shifts in consumer preferences and behaviors.
The current state of raw materials and trends in supply versus pricing
Regulatory landscape and expected changes or developments.
The existing capacity in the market and any expected additions or expansions up to the end of the forecast period.
To assess the market impact of these parameters, we assign weights to each one and utilize weighted average analysis. This process allows us to quantify their influence on the market and derive an expected growth rate for the forecasted period. By considering these various factors and applying a weighted analysis approach, we strive to provide accurate and reliable market forecasts.
Insight Generation & Report Presentation:
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