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Global Smart Machines in Banking Market to reach USD XX billion by the end of 2029.

Global Smart Machines in Banking Market Size study & Forecast, by Component (Hardware, Software, Service) by Machine Type (Robots, Autonomous Cars, Drones Wearable Devices, Others) By Technology (Cloud Computing Technology, Big Data, Internet of Everything, Robotics Cognitive Technology, Affective Technology) and Regional Analysis, 2022-2029

Product Code: BFBFSI-75889623
Publish Date: 17-10-2022
Page: 200

Global Smart Machines in Banking Market is valued approximately USD XX billion in 2021 and is anticipated to grow with a healthy growth rate of more than XX% over the forecast period 2022-2029. Smart Machines refers to a device integrated with machine-to-machine (M2M) technology and other computing technologies such as artificial intelligence (AI), machine learning & deep learning utilized by banking and financial services institutions. These machines are used in banking sector to access frauds and to manage risk associated with different aspects of banking such as credit risk assessment etc. The growing number of Machine-to-machine connections worldwide and rising digitization in banking sector worldwide as well as strategic initiatives from leading market players are key factors accelerating the market growth.

According to Statista – during 2020, globally around 8.9 billion machines to machine (M2M) connections were estimated, and as per projection the number of M2M connections would grow to 14.7 billion connections by 2023. Also, rising integration of speech recognition systems with smart machines and growing penetration of digital banking services would create lucrative growth prospectus for the market over the forecast period. However, the high cost of Smart Machines and rising security concerns impede the market growth throughout the forecast period of 2022-2029.

The key regions considered for the Global Smart Machines in Banking Market study includes Asia Pacific, North America, Europe, Latin America, and Rest of the World. North America is the leading region in terms of market share owing to presence of leading market players as well as availability of required technological infrastructure in the region. Whereas Asia Pacific is expected to grow significantly during the forecast period, owing to factors such as rising banking sector and growing emergence of digital banking services in the region.

Major market player included in this report are:
Apple Inc.
BAE Systems
Digital Reasoning Systems Inc
General Electric
Google LLC,
International Business Machines (IBM) Corporation
Mobile Industrial Robots A/S
Salesforce.com Inc.

Recent Developments in the Market:
 In June 2021, Dover Federal Credit Union (DFCU), collaborated with interface.ai’ for deployment of Intelligent Virtual Assistant (IVA). This new IVA would be initially available through DFCU’s call center, followed by their website and mobile banking services.

 In September 2021, Arvest Bank announced collaboration with Thought Machine and Accenture for deployment of next generation core banking platform. Through this partnership both the players would facilitate Arvest Bank to build personalized, real-time banking services for its customers.

Global Smart Machines in Banking Market Report Scope:
Historical Data 2019-2020-2021
Base Year for Estimation 2021
Forecast period 2022-2029
Report Coverage Revenue forecast, Company Ranking, Competitive Landscape, Growth factors, and Trends
Segments Covered Component, Machine Type, Technology, Region
Regional Scope North America; Europe; Asia Pacific; Latin America; Rest of the World
Customization Scope Free report customization (equivalent up to 8 analyst’s working hours) with purchase. Addition or alteration to country, regional & segment scope*

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 years. The report is designed to incorporate both qualitative and quantitative aspects of the industry within countries involved in the study.

The report also caters detailed information about the crucial aspects such as driving factors & challenges which will define the future growth of the market. Additionally, it also incorporates potential 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 Machine Type
Autonomous Cars
Wearable Devices
By Technology
Cloud Computing Technology
Big Data
Internet of Everything
Cognitive Technology
Affective Technology

By Region:
North America
Asia Pacific
South Korea
Latin America
Rest of the World

Chapter 1. Executive Summary
1.1. Market Snapshot
1.2. Global & Segmental Market Estimates & Forecasts, 2019-2029 (USD Billion)
1.2.1. Smart Machines in Banking Market, by Region, 2019-2029 (USD Billion)
1.2.2. Smart Machines in Banking Market, by Component, 2019-2029 (USD Billion)
1.2.3. Smart Machines in Banking Market, by Machine Type, 2019-2029 (USD Billion)
1.2.4. Smart Machines in Banking Market, by Technology, 2019-2029 (USD Billion)
1.3. Key Trends
1.4. Estimation Methodology
1.5. Research Assumption
Chapter 2. Global Smart Machines 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 Smart Machines in Banking Market Dynamics
3.1. Smart Machines in Banking Market Impact Analysis (2019-2029)
3.1.1. Market Drivers Growing number of Machine-to-machine connections worldwide. Rising digitization in banking sector worldwide. Strategic initiatives from leading market players.
3.1.2. Market Challenges High deployment cost. Rising concern over data security.
3.1.3. Market Opportunities Growing integration of speech recognition systems with smart machines. Rising penetration of digital banking services.
Chapter 4. Global Smart Machines 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
4.2. Futuristic Approach to Porter’s 5 Force Model (2019-2029)
4.3. PEST Analysis
4.3.1. Political
4.3.2. Economical
4.3.3. Social
4.3.4. Technological
4.4. Investment Adoption Model
4.5. Analyst Recommendation & Conclusion
4.6. Top investment opportunity
4.7. Top winning strategies
Chapter 5. Risk Assessment: COVID-19 Impact
5.1. Assessment of the overall impact of COVID-19 on the industry
5.2. Pre COVID-19 and post COVID-19 Market scenario
Chapter 6. Global Smart Machines in Banking Market, by Component
6.1. Market Snapshot
6.2. Global Smart Machines in Banking Market by Component, Performance – Potential Analysis
6.3. Global Smart Machines in Banking Market Estimates & Forecasts by Component 2019-2029 (USD Billion)
6.4. Smart Machines in Banking Market, Sub Segment Analysis
6.4.1. Hardware
6.4.2. Software
6.4.3. Service
Chapter 7. Global Smart Machines in Banking Market, by Machine Type
7.1. Market Snapshot
7.2. Global Smart Machines in Banking Market by Machine Type, Performance – Potential Analysis
7.3. Global Smart Machines in Banking Market Estimates & Forecasts by Machine Type 2019-2029 (USD Billion)
7.4. Smart Machines in Banking Market, Sub Segment Analysis
7.4.1. Robots
7.4.2. Autonomous Cars
7.4.3. Drones
7.4.4. Wearable Devices
7.4.5. Others
Chapter 8. Global Smart Machines in Banking Market, by Technology
8.1. Market Snapshot
8.2. Global Smart Machines in Banking Market by Technology, Performance – Potential Analysis
8.3. Global Smart Machines in Banking Market Estimates & Forecasts by Technology 2019-2029 (USD Billion)
8.4. Smart Machines in Banking Market, Sub Segment Analysis
8.4.1. Cloud Computing Technology
8.4.2. Big Data
8.4.3. Internet of Everything
8.4.4. Robotics
8.4.5. Cognitive Technology
8.4.6. Affective Technology
Chapter 9. Global Smart Machines in Banking Market, Regional Analysis
9.1. Smart Machines in Banking Market, Regional Market Snapshot
9.2. North America Smart Machines in Banking Market
9.2.1. U.S. Smart Machines in Banking Market Component breakdown estimates & forecasts, 2019-2029 Machine Type breakdown estimates & forecasts, 2019-2029 Technology breakdown estimates & forecasts, 2019-2029
9.2.2. Canada Smart Machines in Banking Market
9.3. Europe Smart Machines in Banking Market Snapshot
9.3.1. U.K. Smart Machines in Banking Market
9.3.2. Germany Smart Machines in Banking Market
9.3.3. France Smart Machines in Banking Market
9.3.4. Spain Smart Machines in Banking Market
9.3.5. Italy Smart Machines in Banking Market
9.3.6. Rest of Europe Smart Machines in Banking Market
9.4. Asia-Pacific Smart Machines in Banking Market Snapshot
9.4.1. China Smart Machines in Banking Market
9.4.2. India Smart Machines in Banking Market
9.4.3. Japan Smart Machines in Banking Market
9.4.4. Australia Smart Machines in Banking Market
9.4.5. South Korea Smart Machines in Banking Market
9.4.6. Rest of Asia Pacific Smart Machines in Banking Market
9.5. Latin America Smart Machines in Banking Market Snapshot
9.5.1. Brazil Smart Machines in Banking Market
9.5.2. Mexico Smart Machines in Banking Market
9.6. Rest of The World Smart Machines in Banking Market

Chapter 10. Competitive Intelligence
10.1. Top Market Strategies
10.2. Company Profiles
10.2.1. Aethon Key Information Overview Financial (Subject to Data Availability) Product Summary Recent Developments
10.2.2. Apple Inc.
10.2.3. BAE Systems
10.2.4. Digital Reasoning Systems Inc
10.2.5. General Electric
10.2.6. Google LLC,
10.2.7. International Business Machines (IBM) Corporation
10.2.8. KUKA AG
10.2.9. Mobile Industrial Robots A/S
10.2.10. Salesforce.com Inc.
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.
We are dedicated to clearly communicating the purpose and objectives of each research project in the final deliverables. Our process begins by identifying the specific problem or challenge our client wishes to address, and from there, we establish precise research questions that need to be answered. To gain a comprehensive understanding of the subject matter and identify the most relevant trends and best practices, we conduct an extensive review of existing literature, industry reports, case studies, and pertinent academic research.
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:
After conducting the research, our experts analyze the findings in relation to the research objectives and the specific needs of the client. They generate valuable insights and recommendations that directly address the client’s business challenges. These insights are carefully connected to the research findings to provide a comprehensive understanding.
Next, we create a well-structured research report that effectively communicates the research findings, insights, and recommendations to the client. To enhance clarity and comprehension, we utilize visual aids such as charts, graphs, and tables. These visual elements are employed to present the data in an engaging and easily understandable format, ensuring that the information is accessible and visually appealing to the client. Our aim is to deliver a clear and concise report that conveys the research findings effectively and provides actionable recommendations to meet the client’s specific needs.

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