Market Overview:
The global big data analytics in banking market is expected to grow at a CAGR of 16.8% during the forecast period from 2018 to 2030. The growth of the market can be attributed to the increasing demand for big data analytics solutions by banks for various applications such as feedback management, customer analytics, social media analytics, fraud detection and management, and others. In addition, the growing trend of cloud-based deployment is also contributing to the growth of this market. However, lack of awareness about big data analytics solutions among banking sector organizations is restraining the growth of this market.
Product Definition:
Big data analytics is the process of examining large data sets to uncover hidden patterns, correlations and other insights. Big data analytics can be used in a number of different industries, including banking.
Banks can use big data analytics to improve their customer service, fraud prevention and risk management programs. By analyzing large amounts of customer data, banks can identify trends and patterns that may not be visible when looking at small sample sizes. This information can help banks make more informed decisions about how to best serve their customers and protect them from financial crime.
On-Premise:
On-premise software is a type of software that is installed and used directly without going through a server. The programs are installed on the user's own computer, which uses the internet to communicate with other devices. On-premise solutions include database applications as well as application software such as customer relationship management (CRM) or order processing system (OPS).
Cloud:
Cloud and it's usage in big data analytics in banking market is a technology-driven trend that has gained momentum over the past few years. The need to reduce IT costs, increase flexibility, and focus on core business processes are some of the key factors responsible for this shift towards cloud computing. Cloud computing offers several benefits such as scalability, easy availability of resources coupled with lower operational cost which is expected to drive demand over the forecast period.
Application Insights:
The feedback management application segment dominated the global big data analytics in banking market, accounting for over 25.0% share of the overall revenue in 2017. The growing need for monitoring and analyzing customer feedback is driving adoption of this technology across various banks to improve customer experience and bank performance. In addition, social media analytics has been found to be beneficial in identifying emerging trends from social media platforms to assist marketing teams with their plans for future campaigns.
The fraud detection and management segment is expected register a significant CAGR during the forecast period owing to increasing incidence of cyberattacks that are targeting financial institutions worldwide including banks, payment processors, and e-commerce companies. Furthermore, advanced machine learning techniques have enabled fraud detection by analyzing patterns such as transaction behavior across different channels-online/offline together with historical data-to make accurate predictions about future fraudulent transactions which would otherwise go undetected using conventional methods only capable of detecting broad trends or suspicious activity (such as an unusual increase in login attempts).
Regional Analysis:
North America dominated the market in 2017 owing to the presence of a large number of global players such as IBM Corporation; H2O.ai, Inc.; and Cloudera, Inc. Moreover, these companies are also focusing on expanding their operations in this region by opening new offices or collaborating with other firms to expand their services. For instance, in September 2018, H2O announced its expansion into Canada by opening a new office there along with an investment of USD X million for developing artificial intelligence-based solutions for retail businesses.
Asia Pacific is expected to grow at the highest CAGR over the forecast period due to increasing adoption of big data analytics technology among financial institutions and growing awareness about advanced banking technologies among customers and end users such as small & medium enterprises (SMEs).
Growth Factors:
- Increasing demand for big data analytics in banking sector to reduce the risk of fraudulent activities and improve customer experience.
- The increasing popularity of cloud-based big data analytics solutions that offer faster deployment, scalability, and lower total cost of ownership (TCO).
- The growing trend of mergers and acquisitions (M&A) in the banking sector is expected to drive the demand for big data analytics solutions as banks seek to gain a competitive edge by analyzing large volumes of customer data quickly and accurately.
- The increasing use of mobile devices for banking transactions is generating huge volumes of transaction data that can be analyzed using big data analytics tools to identify patterns and trends.
- Banks are increasingly looking to harness the power of artificial intelligence (AI) and machine learning algorithms to enable them not only analyze large amounts at once but also make predictions about future customer behavior
Scope Of The Report
Report Attributes
Report Details
Report Title
Big Data Analytics in Banking Market Research Report
By Type
On-Premise, Cloud
By Application
Feedback Management, Customer Analytics, Social Media Analytics, Fraud Detection and Management, Others
By Companies
IBM, Oracle, SAP SE, Microsoft, HP, Amazon AWS, Google, Hitachi Data Systems, Tableau, New Relic, Alation, Teradata, VMware, Splice Machine, Splunk Enterprise, Alteryx
Regions Covered
North America, Europe, APAC, Latin America, MEA
Base Year
2021
Historical Year
2019 to 2020 (Data from 2010 can be provided as per availability)
Forecast Year
2030
Number of Pages
216
Number of Tables & Figures
152
Customization Available
Yes, the report can be customized as per your need.
Global Big Data Analytics in Banking Market Report Segments:
The global Big Data Analytics in Banking market is segmented on the basis of:
Types
On-Premise, Cloud
The product segment provides information about the market share of each product and the respective CAGR during the forecast period. It lays out information about the product pricing parameters, trends, and profits that provides in-depth insights of the market. Furthermore, it discusses latest product developments & innovation in the market.
Applications
Feedback Management, Customer Analytics, Social Media Analytics, Fraud Detection and Management, Others
The application segment fragments various applications of the product and provides information on the market share and growth rate of each application segment. It discusses the potential future applications of the products and driving and restraining factors of each application segment.
Some of the companies that are profiled in this report are:
- IBM
- Oracle
- SAP SE
- Microsoft
- HP
- Amazon AWS
- Google
- Hitachi Data Systems
- Tableau
- New Relic
- Alation
- Teradata
- VMware
- Splice Machine
- Splunk Enterprise
- Alteryx
Highlights of The Big Data Analytics in Banking Market Report:
- The market structure and projections for the coming years.
- Drivers, restraints, opportunities, and current trends of market.
- Historical data and forecast.
- Estimations for the forecast period 2030.
- Developments and trends in the market.
- By Type:
- On-Premise
- Cloud
- By Application:
- Feedback Management
- Customer Analytics
- Social Media Analytics
- Fraud Detection and Management
- Others
- Market scenario by region, sub-region, and country.
- Market share of the market players, company profiles, product specifications, SWOT analysis, and competitive landscape.
- Analysis regarding upstream raw materials, downstream demand, and current market dynamics.
- Government Policies, Macro & Micro economic factors are also included in the report.
We have studied the Big Data Analytics in Banking Market in 360 degrees via. both primary & secondary research methodologies. This helped us in building an understanding of the current market dynamics, supply-demand gap, pricing trends, product preferences, consumer patterns & so on. The findings were further validated through primary research with industry experts & opinion leaders across countries. The data is further compiled & validated through various market estimation & data validation methodologies. Further, we also have our in-house data forecasting model to predict market growth up to 2030.
Regional Analysis
- North America
- Europe
- Asia Pacific
- Middle East & Africa
- Latin America
Note: A country of choice can be added in the report at no extra cost. If more than one country needs to be added, the research quote will vary accordingly.
The geographical analysis part of the report provides information about the product sales in terms of volume and revenue in regions. It lays out potential opportunities for the new entrants, emerging players, and major players in the region. The regional analysis is done after considering the socio-economic factors and government regulations of the countries in the regions.
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8 Reasons to Buy This Report
- Includes a Chapter on the Impact of COVID-19 Pandemic On the Market
- Report Prepared After Conducting Interviews with Industry Experts & Top Designates of the Companies in the Market
- Implemented Robust Methodology to Prepare the Report
- Includes Graphs, Statistics, Flowcharts, and Infographics to Save Time
- Industry Growth Insights Provides 24/5 Assistance Regarding the Doubts in the Report
- Provides Information About the Top-winning Strategies Implemented by Industry Players.
- In-depth Insights On the Market Drivers, Restraints, Opportunities, and Threats
- Customization of the Report Available
Frequently Asked Questions?
Big data analytics in banking is the process of using big data technologies to improve the understanding and management of financial data. This can include things like improving risk assessment, forecasting trends, and making better decisions about where to allocate resources.
Some of the key players operating in the big data analytics in banking market are IBM, Oracle, SAP SE, Microsoft, HP, Amazon AWS, Google, Hitachi Data Systems, Tableau, New Relic, Alation, Teradata, VMware, Splice Machine, Splunk Enterprise, Alteryx.
The big data analytics in banking market is expected to register a CAGR of 16.8%.
Chapter 1 Executive Summary
Chapter 2 Assumptions and Acronyms Used
Chapter 3 Research Methodology
Chapter 4 Big Data Analytics in Banking Market Overview
4.1 Introduction
4.1.1 Market Taxonomy
4.1.2 Market Definition
4.1.3 Macro-Economic Factors Impacting the Market Growth
4.2 Big Data Analytics in Banking Market Dynamics
4.2.1 Market Drivers
4.2.2 Market Restraints
4.2.3 Market Opportunity
4.3 Big Data Analytics in Banking Market - Supply Chain Analysis
4.3.1 List of Key Suppliers
4.3.2 List of Key Distributors
4.3.3 List of Key Consumers
4.4 Key Forces Shaping the Big Data Analytics in Banking Market
4.4.1 Bargaining Power of Suppliers
4.4.2 Bargaining Power of Buyers
4.4.3 Threat of Substitution
4.4.4 Threat of New Entrants
4.4.5 Competitive Rivalry
4.5 Global Big Data Analytics in Banking Market Size & Forecast, 2020-2028
4.5.1 Big Data Analytics in Banking Market Size and Y-o-Y Growth
4.5.2 Big Data Analytics in Banking Market Absolute $ Opportunity
Chapter 5 Global Market Analysis and Forecast by Type
5.1 Introduction
5.1.1 Key Market Trends & Growth Opportunities by Type
5.1.2 Basis Point Share (BPS) Analysis by Type
5.1.3 Absolute $ Opportunity Assessment by Type
5.2 Market Size Forecast by Type
5.2.1 On-Premise
5.2.2 Cloud
5.3 Market Attractiveness Analysis by Type
Chapter 6 Global Market Analysis and Forecast by Applications
6.1 Introduction
6.1.1 Key Market Trends & Growth Opportunities by Applications
6.1.2 Basis Point Share (BPS) Analysis by Applications
6.1.3 Absolute $ Opportunity Assessment by Applications
6.2 Market Size Forecast by Applications
6.2.1 Feedback Management
6.2.2 Customer Analytics
6.2.3 Social Media Analytics
6.2.4 Fraud Detection and Management
6.2.5 Others
6.3 Market Attractiveness Analysis by Applications
Chapter 7 Global Big Data Analytics in Banking Market Analysis and Forecast by Region
7.1 Introduction
7.1.1 Key Market Trends & Growth Opportunities by Region
7.1.2 Basis Point Share (BPS) Analysis by Region
7.1.3 Absolute $ Opportunity Assessment by Region
7.2 Big Data Analytics in Banking Market Size Forecast by Region
7.2.1 North America
7.2.2 Europe
7.2.3 Asia Pacific
7.2.4 Latin America
7.2.5 Middle East & Africa (MEA)
7.3 Market Attractiveness Analysis by Region
Chapter 8 Coronavirus Disease (COVID-19) Impact
8.1 Introduction
8.2 Current & Future Impact Analysis
8.3 Economic Impact Analysis
8.4 Government Policies
8.5 Investment Scenario
Chapter 9 North America Analysis and Forecast
9.1 Introduction
9.2 North America Market Size Forecast by Country
9.2.1 U.S.
9.2.2 Canada
9.3 Basis Point Share (BPS) Analysis by Country
9.4 Absolute $ Opportunity Assessment by Country
9.5 Market Attractiveness Analysis by Country
9.6 North America Market Size Forecast by Type
9.6.1 On-Premise
9.6.2 Cloud
9.7 Basis Point Share (BPS) Analysis by Type
9.8 Absolute $ Opportunity Assessment by Type
9.9 Market Attractiveness Analysis by Type
9.10 North America Market Size Forecast by Applications
9.10.1 Feedback Management
9.10.2 Customer Analytics
9.10.3 Social Media Analytics
9.10.4 Fraud Detection and Management
9.10.5 Others
9.11 Basis Point Share (BPS) Analysis by Applications
9.12 Absolute $ Opportunity Assessment by Applications
9.13 Market Attractiveness Analysis by Applications
Chapter 10 Europe Analysis and Forecast
10.1 Introduction
10.2 Europe Market Size Forecast by Country
10.2.1 Germany
10.2.2 France
10.2.3 Italy
10.2.4 U.K.
10.2.5 Spain
10.2.6 Russia
10.2.7 Rest of Europe
10.3 Basis Point Share (BPS) Analysis by Country
10.4 Absolute $ Opportunity Assessment by Country
10.5 Market Attractiveness Analysis by Country
10.6 Europe Market Size Forecast by Type
10.6.1 On-Premise
10.6.2 Cloud
10.7 Basis Point Share (BPS) Analysis by Type
10.8 Absolute $ Opportunity Assessment by Type
10.9 Market Attractiveness Analysis by Type
10.10 Europe Market Size Forecast by Applications
10.10.1 Feedback Management
10.10.2 Customer Analytics
10.10.3 Social Media Analytics
10.10.4 Fraud Detection and Management
10.10.5 Others
10.11 Basis Point Share (BPS) Analysis by Applications
10.12 Absolute $ Opportunity Assessment by Applications
10.13 Market Attractiveness Analysis by Applications
Chapter 11 Asia Pacific Analysis and Forecast
11.1 Introduction
11.2 Asia Pacific Market Size Forecast by Country
11.2.1 China
11.2.2 Japan
11.2.3 South Korea
11.2.4 India
11.2.5 Australia
11.2.6 South East Asia (SEA)
11.2.7 Rest of Asia Pacific (APAC)
11.3 Basis Point Share (BPS) Analysis by Country
11.4 Absolute $ Opportunity Assessment by Country
11.5 Market Attractiveness Analysis by Country
11.6 Asia Pacific Market Size Forecast by Type
11.6.1 On-Premise
11.6.2 Cloud
11.7 Basis Point Share (BPS) Analysis by Type
11.8 Absolute $ Opportunity Assessment by Type
11.9 Market Attractiveness Analysis by Type
11.10 Asia Pacific Market Size Forecast by Applications
11.10.1 Feedback Management
11.10.2 Customer Analytics
11.10.3 Social Media Analytics
11.10.4 Fraud Detection and Management
11.10.5 Others
11.11 Basis Point Share (BPS) Analysis by Applications
11.12 Absolute $ Opportunity Assessment by Applications
11.13 Market Attractiveness Analysis by Applications
Chapter 12 Latin America Analysis and Forecast
12.1 Introduction
12.2 Latin America Market Size Forecast by Country
12.2.1 Brazil
12.2.2 Mexico
12.2.3 Rest of Latin America (LATAM)
12.3 Basis Point Share (BPS) Analysis by Country
12.4 Absolute $ Opportunity Assessment by Country
12.5 Market Attractiveness Analysis by Country
12.6 Latin America Market Size Forecast by Type
12.6.1 On-Premise
12.6.2 Cloud
12.7 Basis Point Share (BPS) Analysis by Type
12.8 Absolute $ Opportunity Assessment by Type
12.9 Market Attractiveness Analysis by Type
12.10 Latin America Market Size Forecast by Applications
12.10.1 Feedback Management
12.10.2 Customer Analytics
12.10.3 Social Media Analytics
12.10.4 Fraud Detection and Management
12.10.5 Others
12.11 Basis Point Share (BPS) Analysis by Applications
12.12 Absolute $ Opportunity Assessment by Applications
12.13 Market Attractiveness Analysis by Applications
Chapter 13 Middle East & Africa (MEA) Analysis and Forecast
13.1 Introduction
13.2 Middle East & Africa (MEA) Market Size Forecast by Country
13.2.1 Saudi Arabia
13.2.2 South Africa
13.2.3 UAE
13.2.4 Rest of Middle East & Africa (MEA)
13.3 Basis Point Share (BPS) Analysis by Country
13.4 Absolute $ Opportunity Assessment by Country
13.5 Market Attractiveness Analysis by Country
13.6 Middle East & Africa (MEA) Market Size Forecast by Type
13.6.1 On-Premise
13.6.2 Cloud
13.7 Basis Point Share (BPS) Analysis by Type
13.8 Absolute $ Opportunity Assessment by Type
13.9 Market Attractiveness Analysis by Type
13.10 Middle East & Africa (MEA) Market Size Forecast by Applications
13.10.1 Feedback Management
13.10.2 Customer Analytics
13.10.3 Social Media Analytics
13.10.4 Fraud Detection and Management
13.10.5 Others
13.11 Basis Point Share (BPS) Analysis by Applications
13.12 Absolute $ Opportunity Assessment by Applications
13.13 Market Attractiveness Analysis by Applications
Chapter 14 Competition Landscape
14.1 Big Data Analytics in Banking Market: Competitive Dashboard
14.2 Global Big Data Analytics in Banking Market: Market Share Analysis, 2019
14.3 Company Profiles (Details – Overview, Financials, Developments, Strategy)
14.3.1 IBM
14.3.2 Oracle
14.3.3 SAP SE
14.3.4 Microsoft
14.3.5 HP
14.3.6 Amazon AWS
14.3.7 Google
14.3.8 Hitachi Data Systems
14.3.9 Tableau
14.3.10 New Relic
14.3.11 Alation
14.3.12 Teradata
14.3.13 VMware
14.3.14 Splice Machine
14.3.15 Splunk Enterprise
14.3.16 Alteryx
