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Global Big Data Analytics in the Banking Market to reach USD 11.58 billion by the end of 2030.

Global Big Data Analytics in the Banking Market Size study & Forecast, by Type (On-premise, Cloud), by Application(Feedback Management, Customer Analytics, Social Media Analytics, Fraud Detection and Management, Other) and Regional Analysis, 2023-2030

Product Code: ICTBC-52157001
Publish Date: 19-06-2023
Page: 200

Global Big Data Analytics in the Banking Market is valued at approximately USD 5.1 billion in 2022 and is anticipated to grow with a healthy growth rate of more than 10.8% over the forecast period 2023-2030. The market of big data analytics in the banking industry refers to utilizing advanced analytics techniques and technology to extract valuable insights and make data-driven decisions making in the banking industry. Big data analytics includes the collection, storage, processing and the analysis on large amount of structured and unstructured data from various sources of banks ecosystem. The driving factors of the market are the increasing adoption of cloud services and the increasing need for fraud detection and prevention systems.

The study done by the Bank of England suggests that migrating in 2021 to the cloud has the potential to reduce technology infrastructure costs by 30 to 50 per cent, including the expenses associated with maintaining physical equipment. In addition to the growth of new technology such as AI and ML. By using technology banks can enhance their customer engagement and retention creating a lucrative opportunity for market growth. However, issues associated with installation and integration among banks and financial institutions is hinder the growth of the market.

The key regions considered for the Global Big Data Analytics in the Banking Market study includes Asia Pacific, North America, Europe, Latin America, and Middle East & Africa. North America dominated the market in 2022 owing to factors such as rising adoption of big data applications by major American banks and rising investments on advance analytics technology solutions. Whereas, Asia Pacific is emerging as the fastest growing region due to the increasing volume of data generated by banks and the large customer base in the region and this region also consists with the highest smartphone users.

Major market player included in this report are:
Oracle Corporation
International Business Machine Corporation
Alteryx Inc.
Aspire Systems Inc.
Adobe Systems Incorporated
Microstrategy Inc.
Mayato GmbH
Mastercard Inc.
ThetaRay Ltd

Recent Developments in the Market:
• January 2022 – RBL Bank and Google announced a collaboration to boost the lender’s customer experience strategy and enhance its value proposition to serve its rapidly growing customer base through its digital platform, Abacus 2.0. This partnership will help offer more better customer data management and analytics, enabling effective cross-selling within the Bank’s large customer base and significantly reducing customer acquisition costs.
• On February 2020 – Oracle Financial Crime and Compliance Management suite of products now include an integrated analytics workbench, 300-plus customer risk indicators, and embedded graph analytics visualizations. These capabilities build on Oracle’s strategy to help financial institutions fight money laundering and achieve compliance.

Global Big Data Analytics in the Banking Market Report Scope:
ü Historical Data – 2020 – 2021
ü Base Year for Estimation – 2022
ü Forecast period – 2023-2030
ü Report Coverage – Revenue forecast, Company Ranking, Competitive Landscape, Growth factors, and Trends
ü Segments Covered – Type, application, region
ü Regional Scope – North America; Europe; Asia Pacific; Latin America; Middle East & Africa
ü 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 Type:
By Application:
Feedback Management
Customer Analytics
Social Media Analytics
Fraud Detection and Management

By Region:

North America


Asia Pacific
South Korea

Latin America

Middle East & Africa
Saudi Arabia
South Africa
Rest of Middle East & Africa

Chapter 1. Executive Summary
1.1. Market Snapshot
1.2. Global & Segmental Market Estimates & Forecasts, 2020-2030 (USD Billion)
1.2.1. Big Data Analytics in the Banking Market, by Region, 2020-2030 (USD Billion)
1.2.2. Big Data Analytics in the Banking Market, by Type, 2020-2030 (USD Billion)
1.2.3. Big Data Analytics in the Banking Market, by Application, 2020-2030 (USD Billion)
1.3. Key Trends
1.4. Estimation Methodology
1.5. Research Assumption
Chapter 2. Global Big Data Analytics in the Banking Market Definition and Scope
2.1. Objective of the Study
2.2. Market Definition & Scope
2.2.1. Industry Evolution
2.2.2. Scope of the Study
2.3. Years Considered for the Study
2.4. Currency Conversion Rates
Chapter 3. Global Big Data Analytics in the Banking Market Dynamics
3.1. Big Data Analytics in the Banking Market Impact Analysis (2020-2030)
3.1.1. Market Drivers Increasing adoption of cloud service Increasing need of fraud detection and prevention
3.1.2. Market Challenges Difficult to implementation and integration
3.1.3. Market Opportunities Growth of new technology Banks can enhance their customer engagement and retention
Chapter 4. Global Big Data Analytics in the 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. Porter’s 5 Force Impact Analysis
4.3. PEST Analysis
4.3.1. Political
4.3.2. Economical
4.3.3. Social
4.3.4. Technological
4.3.5. Environmental
4.3.6. Legal
4.4. Top investment opportunity
4.5. Top winning strategies
4.6. COVID-19 Impact Analysis
4.7. Disruptive Trends
4.8. Industry Expert Perspective
4.9. Analyst Recommendation & Conclusion
Chapter 5. Global Big Data Analytics in the Banking Market, by Type
5.1. Market Snapshot
5.2. Global Big Data Analytics in the Banking Market by Type, Performance – Potential Analysis
5.3. Global Big Data Analytics in the Banking Market Estimates & Forecasts by Type2020-2030 (USD Billion)
5.4. Big Data Analytics in the Banking Market, Sub Segment Analysis
5.4.1. On-premise
5.4.2. Cloud
Chapter 6. Global Big Data Analytics in the Banking Market, by Application
6.1. Market Snapshot
6.2. Global Big Data Analytics in the Banking Market by Application, Performance – Potential Analysis
6.3. Global Big Data Analytics in the Banking Market Estimates & Forecasts by Application 2020-2030 (USD Billion)
6.4. Big Data Analytics in the Banking Market, Sub Segment Analysis
6.4.1. Feedback Management
6.4.2. Customer Analytics
6.4.3. Social Media Analytics
6.4.4. Fraud Detection and Management
6.4.5. Other
Chapter 7. Global Big Data Analytics in the Banking Market, Regional Analysis
7.1. Top Leading Countries
7.2. Top Emerging Countries
7.3. Big Data Analytics in the Banking Market, Regional Market Snapshot
7.4. North America Big Data Analytics in the Banking Market
7.4.1. U.S. Big Data Analytics in the Banking Market Typebreakdown estimates & forecasts, 2020-2030 Application breakdown estimates & forecasts, 2020-2030
7.4.2. Canada Big Data Analytics in the Banking Market
7.5. Europe Big Data Analytics in the Banking Market Snapshot
7.5.1. U.K. Big Data Analytics in the Banking Market
7.5.2. Germany Big Data Analytics in the Banking Market
7.5.3. France Big Data Analytics in the Banking Market
7.5.4. Spain Big Data Analytics in the Banking Market
7.5.5. Italy Big Data Analytics in the Banking Market
7.5.6. Rest of Europe Big Data Analytics in the Banking Market
7.6. Asia-Pacific Big Data Analytics in the Banking Market Snapshot
7.6.1. China Big Data Analytics in the Banking Market
7.6.2. India Big Data Analytics in the Banking Market
7.6.3. Japan Big Data Analytics in the Banking Market
7.6.4. Australia Big Data Analytics in the Banking Market
7.6.5. South Korea Big Data Analytics in the Banking Market
7.6.6. Rest of Asia Pacific Big Data Analytics in the Banking Market
7.7. Latin America Big Data Analytics in the Banking Market Snapshot
7.7.1. Brazil Big Data Analytics in the Banking Market
7.7.2. Mexico Big Data Analytics in the Banking Market
7.8. Middle East & Africa Big Data Analytics in the Banking Market
7.8.1. Saudi Arabia Big Data Analytics in the Banking Market
7.8.2. South Africa Big Data Analytics in the Banking Market
7.8.3. Rest of Middle East & Africa Big Data Analytics in the Banking Market

Chapter 8. Competitive Intelligence
8.1. Key Company SWOT Analysis
8.1.1. Company 1
8.1.2. Company 2
8.1.3. Company 3
8.2. Top Market Strategies
8.3. Company Profiles
8.3.1. Oracle Corporation Key Information Overview Financial (Subject to Data Availability) Product Summary Recent Developments
8.3.2. SAP SE
8.3.3. International Business Machine Corporation
8.3.4. Alteryx Inc.
8.3.5. Asphire Systems Inc.
8.3.6. Adobe Systems Incorporation
8.3.7. Microstrategy Inc.
8.3.8. Mayato Gmbh
8.3.9. Mastercard Inc.
8.3.10. Thetaray Ltd
Chapter 9. Research Process
9.1. Research Process
9.1.1. Data Mining
9.1.2. Analysis
9.1.3. Market Estimation
9.1.4. Validation
9.1.5. Publishing
9.2. Research Attributes
9.3. 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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