Market Reports Center

Big Data in the Financial Services Industry: 2018 – 2030 – Opportunities, Challenges, Strategies & Forecasts


Report Details

Big Data in the Financial Services Industry: 2018 – 2030 – Opportunities, Challenges, Strategies & Forecasts

SKU SNSJUL021801
Category ICT
Publisher SNS Telecom
Pages 521
Published Jul-18
Request Discount Pay by Wire/Invoice

Description

Big Data investments in the financial services industry will account for nearly $9 Billion in 2018,further expected to grow at a CAGR of approximately 17% over the next three years.
“Big Data” originally emerged as a term to describe datasets whose size is beyond the ability of traditional databases to capture, store, manage and analyze. However, the scope of the term has significantly expanded over the years. Big Data not only refers to the data itself but also a set of technologies that capture, store, manage and analyze large and variable collections of data, to solve complex problems.

Amid the proliferation of real-time and historical data from sources such as connected devices, web, social media, sensors, log files and transactional applications, Big Data is rapidly gaining traction from a diverse range of vertical sectors. The financial services industry is no exception to this trend, where Big Data has found a host of applications ranging from targeted marketing and credit scoring to usage-based insurance, data-driven trading, fraud detection and beyond.

Report estimates that Big Data investments in the financial services industry will account for nearly $9 Billion in 2018 alone. Led by a plethora of business opportunities for banks, insurers, credit card and payment processing specialists, asset and wealth management firms, lenders and other stakeholders, these investments are further expected to grow at a CAGR of approximately 17% over the next three years.

The “Big Data in the Financial Services Industry: 2018 – 2030 – Opportunities, Challenges, Strategies & Forecasts” report presents an in-depth assessment of Big Data in the financial services industry including key market drivers, challenges, investment potential, application areas, use cases, future roadmap, value chain, case studies, vendor profiles and strategies. The report also presents market size forecasts for Big Data hardware, software and professional services investments from 2018 through to 2030. The forecasts are segmented for 8 horizontal submarkets, 6 application areas, 11 use cases, 6 regions and 35 countries.

The report comes with an associated Excel datasheet suite covering quantitative data from all numeric forecasts presented in the report.

Topics Covered
The report covers the following topics:
- Big Data ecosystem
- Market drivers and barriers
- Enabling technologies, standardization and regulatory initiatives
- Big Data analytics and implementation models
- Business case, application areas and use cases in the financial services industry
- 30 case studies of Big Data investments by banks, insurers, credit card and payment processing specialists, asset and wealth management firms, lenders, and other stakeholders in the financial services industry
- Future roadmap and value chain
- Profiles and strategies of over 270 leading and emerging Big Data ecosystem players
- Strategic recommendations for Big Data vendors and financial services industry stakeholders
- Market analysis and forecasts from 2018 till 2030

Forecast Segmentation
Market forecasts are provided for each of the following submarkets and their subcategories:

Hardware, Software & Professional Services
- Hardware
- Software
- Professional Services

Horizontal Submarkets
- Storage & Compute Infrastructure
- Networking Infrastructure
- Hadoop & Infrastructure Software
- SQL
- NoSQL
- Analytic Platforms & Applications
- Cloud Platforms
- Professional Services

Application Areas
- Personal & Business Banking
- Investment Banking & Capital Markets
- Insurance Services
- Credit Cards & Payment Processing
- Lending & Financing
- Asset & Wealth Management

Use Cases
- Personalized & Targeted Marketing
- Customer Service & Experience
- Product Innovation & Development
- Risk Modeling, Management & Reporting
- Fraud Detection & Prevention
- Robotic & Intelligent Process Automation
- Usage & Analytics-Based Insurance
- Credit Scoring & Control
- Data-Driven Trading & Investment
- Third Party Data Monetization
- Other Use Cases

Regional Markets
- Asia Pacific
- Eastern Europe
- Latin & Central America
- Middle East & Africa
- North America
- Western Europe

Country Markets
- Argentina, Australia, Brazil, Canada, China, Czech Republic, Denmark, Finland, France, Germany, India, Indonesia, Israel, Italy, Japan, Malaysia, Mexico, Netherlands, Norway, Pakistan, Philippines, Poland, Qatar, Russia, Saudi Arabia, Singapore, South Africa, South Korea, Spain, Sweden, Taiwan, Thailand, UAE, UK, USA

Key Questions Answered
The report provides answers to the following key questions:
- How big is the Big Data opportunity in the financial services industry?
- How is the market evolving by segment and region?
- What will the market size be in 2021, and at what rate will it grow?
- What trends, challenges and barriers are influencing its growth?
- Who are the key Big Data software, hardware and services vendors, and what are their strategies?
- How much are banks, insurers, credit card and payment processing specialists, asset and wealth management firms, lenders and other stakeholders investing in Big Data?
- What opportunities exist for Big Data analytics in the financial services industry?
- Which countries, application areas and use cases will see the highest percentage of Big Data investments in the financial services industry?

Key Findings
The report has the following key findings:
- In 2018, Big Data vendors will pocket nearly $9 Billion from hardware, software and professional services revenues in the financial services industry. These investments are further expected to grow at a CAGR of approximately 17% over the next three years, eventually accounting for over $14 Billion by the end of 2021.
- Banks and other traditional financial services institutes are warming to the idea of embracing cloud-based platforms, particularly hybrid-cloud implementations, in a bid to alleviate the technical and scalability challenges associated with on-premise Big Data environments.
- Big Data technologies are playing a pivotal role in facilitating the creation and success of innovative FinTech (Financial Technology) startups, most notably in the online lending, alterative insurance and money transfer sectors.
- In addition to utilizing traditional information sources, financial services institutes are increasingly becoming reliant on alternative sources of data – ranging from social media to satellite imagery – that can provide previously hidden insights for multiple application areas including data-driven trading and investments, and credit scoring.


News/Press Release

Table of Content

Table of Contents
Chapter 1: Introduction
Executive Summary
Topics Covered
Forecast Segmentation
Key Questions Answered
Key Findings
Methodology
Target Audience
Companies & Organizations Mentioned

Chapter 2: An Overview of Big Data
What is Big Data?
Key Approaches to Big Data Processing
Hadoop
NoSQL
MPAD (Massively Parallel Analytic Databases)
In-Memory Processing
Stream Processing Technologies
Spark
Other Databases & Analytic Technologies
Key Characteristics of Big Data
Volume
Velocity
Variety
Value
Market Growth Drivers
Awareness of Benefits
Maturation of Big Data Platforms
Continued Investments by Web Giants, Governments & Enterprises
Growth of Data Volume, Velocity & Variety
Vendor Commitments & Partnerships
Technology Trends Lowering Entry Barriers
Market Barriers
Lack of Analytic Specialists
Uncertain Big Data Strategies
Organizational Resistance to Big Data Adoption
Technical Challenges: Scalability & Maintenance
Security & Privacy Concerns

Chapter 3: Big Data Analytics
What are Big Data Analytics?
The Importance of Analytics
Reactive vs. Proactive Analytics
Customer vs. Operational Analytics
Technology & Implementation Approaches
Grid Computing
In-Database Processing
In-Memory Analytics
Machine Learning & Data Mining
Predictive Analytics
NLP (Natural Language Processing)
Text Analytics
Visual Analytics
Graph Analytics
Social Media, IT & Telco Network Analytics

Chapter 4: Business Case & Applications in the Financial Services Industry
Overview & Investment Potential
Industry Specific Market Growth Drivers
Industry Specific Market Barriers
Key Application Areas
Personal & Business Banking
Investment Banking & Capital Markets
Insurance Services
Credit Cards & Payments Processing
Lending & Financing
Asset & Wealth Management
Use Cases
Personalized & Targeted Marketing
Customer Service & Experience
Product Innovation & Development
Risk Modeling, Management & Reporting
Fraud Detection & Prevention
Robotic & Intelligent Process Automation
Usage & Analytics-Based Insurance
Credit Scoring & Control
Data-Driven Trading & Investment
Third Party Data Monetization
Other Use Cases

Chapter 5: Financial Services Industry Case Studies
Banks
CBA/CommBank (Commonwealth Bank of Australia): Driving Customer Engagement with Big Data
Credit Suisse: Enhancing Regulatory Compliance with Big Data
Deutsche Bank: Quantifying the Importance of Intangible Assets with Big Data
HSBC Group: Combating Money Laundering & Financial Crime with Big Data
JPMorgan Chase & Co.: Enabling Responsible Prospecting with Big Data
OTP Bank: Reducing Loan Defaults with Big Data
Insurers
AXA: Simplifying Customer Interaction with Big Data
Cigna: Streamlining Health Insurance Claims with Big Data
Progressive Corporation: Rewarding Safe Drivers & Improving Traffic Safety with Big Data
Samsung Fire & Marine Insurance: Transforming Insurance Underwriting with Big Data
UnitedHealth Group: Enhancing Patient Care & Value with Big Data
Zurich Insurance Group: Improving Risk Management with Big Data
Credit Card & Payment Processing Specialists
American Express: Enabling Real-Time Targeting Marketing with Big Data
Capital One: Enriching Cybersecurity with Big Data
Mastercard: Predictively Combating Account Related Fraud with Big Data
TransferWise: Simplifying International Money Transfers With Big Data
Visa: Saving Billions of Dollars with Big Data
Western Union: Personalizing Customer Experience with Big Data
Asset & Wealth Management Firms
Acadian Asset Management: Exploiting Market Inefficiencies with Big Data
AQR Capital Management: Finding Profitable Trading Patterns with Big Data
BlackRock: Gleaning Economic Clues with Big Data
Man Group: Accelerating Trades & Investment Modeling with Big Data
qplum: Optimizing Client Portfolios with Big Data
Two Sigma Investments: Making Systematic Trades with Big Data
Lenders & Other Stakeholders
Avant: Streamlining Borrowing with Big Data
Equifax: Helping Make Informed Credit Decisions with Big Data
FICO (Fair Isaac Corporation): Expanding Access to Credit with Big Data
Kabbage: Empowering Small Business Lending with Big Data
LenddoEFL: Increasing Access to Financial Services in Emerging Economies with Big Data
Upstart: Facilitating Smarter Loans with Big Data

Chapter 6: Future Roadmap & Value Chain
Future Roadmap
Pre-2020: Investments in Advanced Analytics & AI (Artificial Intelligence)
2020 – 2025: Large-Scale Adoption of Cloud-Based Big Data Platforms
2025 – 2030: Towards the Digitization of Financial Services
The Big Data Value Chain
Hardware Providers
Storage & Compute Infrastructure Providers
Networking Infrastructure Providers
Software Providers
Hadoop & Infrastructure Software Providers
SQL & NoSQL Providers
Analytic Platform & Application Software Providers
Cloud Platform Providers
Professional Services Providers
End-to-End Solution Providers
Financial Services Industry

Chapter 7: Standardization & Regulatory Initiatives
ASF (Apache Software Foundation)
Management of Hadoop
Big Data Projects Beyond Hadoop
CSA (Cloud Security Alliance)
BDWG (Big Data Working Group)
CSCC (Cloud Standards Customer Council)
Big Data Working Group
DMG (Data Mining Group)
PMML (Predictive Model Markup Language) Working Group
PFA (Portable Format for Analytics) Working Group
IEEE (Institute of Electrical and Electronics Engineers)
Big Data Initiative
INCITS (InterNational Committee for Information Technology Standards)
Big Data Technical Committee
ISO (International Organization for Standardization)
ISO/IEC JTC 1/SC 32: Data Management and Interchange
ISO/IEC JTC 1/SC 38: Cloud Computing and Distributed Platforms
ISO/IEC JTC 1/SC 27: IT Security Techniques
ISO/IEC JTC 1/WG 9: Big Data
Collaborations with Other ISO Work Groups
ITU (International Telecommunication Union)
ITU-T Y.3600: Big Data – Cloud Computing Based Requirements and Capabilities
Other Deliverables Through SG (Study Group) 13 on Future Networks
Other Relevant Work
Linux Foundation
ODPi (Open Ecosystem of Big Data)
NIST (National Institute of Standards and Technology)
NBD-PWG (NIST Big Data Public Working Group)
OASIS (Organization for the Advancement of Structured Information Standards)
Technical Committees
ODaF (Open Data Foundation)
Big Data Accessibility
ODCA (Open Data Center Alliance)
Work on Big Data
OGC (Open Geospatial Consortium)
Big Data DWG (Domain Working Group)
TM Forum
Big Data Analytics Strategic Program
TPC (Transaction Processing Performance Council)
TPC-BDWG (TPC Big Data Working Group)
W3C (World Wide Web Consortium)
Big Data Community Group
Open Government Community Group

Chapter 8: Market Sizing & Forecasts
Global Outlook for the Big Data in the Financial Services Industry
Hardware, Software & Professional Services Segmentation
Horizontal Submarket Segmentation
Hardware Submarkets
Storage and Compute Infrastructure
Networking Infrastructure
Software Submarkets
Hadoop & Infrastructure Software
SQL
NoSQL
Analytic Platforms & Applications
Cloud Platforms
Professional Services Submarket
Professional Services
Application Area Segmentation
Personal & Business Banking
Investment Banking & Capital Markets
Insurance Services
Credit Cards & Payment Processing
Lending & Financing
Asset & Wealth Management
Use Case Segmentation
Personalized & Targeted Marketing
Customer Service & Experience
Product Innovation & Development
Risk Modeling, Management & Reporting
Fraud Detection & Prevention
Robotic & Intelligent Process Automation
Usage & Analytics-Based Insurance
Credit Scoring & Control
Data-Driven Trading & Investment
Third Party Data Monetization
Other Use Cases
Regional Outlook
Asia Pacific
Country Level Segmentation
Australia
China
India
Indonesia
Japan
Malaysia
Pakistan
Philippines
Singapore
South Korea
Taiwan
Thailand
Rest of Asia Pacific
Eastern Europe
Country Level Segmentation
Czech Republic
Poland
Russia
Rest of Eastern Europe
Latin & Central America
Country Level Segmentation
Argentina
Brazil
Mexico
Rest of Latin & Central America
Middle East & Africa
Country Level Segmentation
Israel
Qatar
Saudi Arabia
South Africa
UAE
Rest of the Middle East & Africa
North America
Country Level Segmentation
Canada
USA
Western Europe
Country Level Segmentation
Denmark
Finland
France
Germany
Italy
Netherlands
Norway
Spain
Sweden
UK
Rest of Western Europe

Chapter 9: Vendor Landscape
1010data
Absolutdata
Accenture
Actian Corporation/HCL Technologies
Adaptive Insights
Adobe Systems
Advizor Solutions
AeroSpike
AFS Technologies
Alation
Algorithmia
Alluxio
ALTEN
Alteryx
AMD (Advanced Micro Devices)
Anaconda
Apixio
Arcadia Data
ARM
AtScale
Attivio
Attunity
Automated Insights
AVORA
AWS (Amazon Web Services)
Axiomatics
Ayasdi
BackOffice Associates
Basho Technologies
BCG (Boston Consulting Group)
Bedrock Data
BetterWorks
Big Panda
BigML
Bitam
Blue Medora
BlueData Software
BlueTalon
BMC Software
BOARD International
Booz Allen Hamilton
Boxever
CACI International
Cambridge Semantics
Capgemini
Cazena
Centrifuge Systems
CenturyLink
Chartio
Cisco Systems
Civis Analytics
ClearStory Data
Cloudability
Cloudera
Cloudian
Clustrix
CognitiveScale
Collibra
Concurrent Technology/Vecima Networks
Confluent
Contexti
Couchbase
Crate.io
Cray
Databricks
Dataiku
Datalytyx
Datameer
DataRobot
DataStax
Datawatch Corporation
DDN (DataDirect Networks)
Decisyon
Dell Technologies
Deloitte
Demandbase
Denodo Technologies
Dianomic Systems
Digital Reasoning Systems
Dimensional Insight
Dolphin Enterprise Solutions Corporation/Hanse Orga Group
Domino Data Lab
Domo
Dremio
DriveScale
Druva
Dundas Data Visualization
DXC Technology
Elastic
Engineering Group (Engineering Ingegneria Informatica)
EnterpriseDB Corporation
eQ Technologic
Ericsson
Erwin
EV? (Big Cloud Analytics)
EXASOL
EXL (ExlService Holdings)
Facebook
FICO (Fair Isaac Corporation)
Figure Eight
FogHorn Systems
Fractal Analytics
Franz
Fujitsu
Fuzzy Logix
Gainsight
GE (General Electric)
Glassbeam
GoodData Corporation
Google/Alphabet
Grakn Labs
Greenwave Systems
GridGain Systems
H2O.ai
HarperDB
Hedvig
Hitachi Vantara
Hortonworks
HPE (Hewlett Packard Enterprise)
Huawei
HVR
HyperScience
HyTrust
IBM Corporation
iDashboards
IDERA
Ignite Technologies
Imanis Data
Impetus Technologies
Incorta
InetSoft Technology Corporation
InfluxData
Infogix
Infor/Birst
Informatica
Information Builders
Infosys
Infoworks
Insightsoftware.com
InsightSquared
Intel Corporation
Interana
InterSystems Corporation
Jedox
Jethro
Jinfonet Software
Juniper Networks
KALEAO
Keen IO
Keyrus
Kinetica
KNIME
Kognitio
Kyvos Insights
LeanXcale
Lexalytics
Lexmark International
Lightbend
Logi Analytics
Logical Clocks
Longview Solutions/Tidemark
Looker Data Sciences
LucidWorks
Luminoso Technologies
Maana
Manthan Software Services
MapD Technologies
MapR Technologies
MariaDB Corporation
MarkLogic Corporation
Mathworks
Melissa
MemSQL
Metric Insights
Microsoft Corporation
MicroStrategy
Minitab
MongoDB
Mu Sigma
NEC Corporation
Neo4j
NetApp
Nimbix
Nokia
NTT Data Corporation
Numerify
NuoDB
NVIDIA Corporation
Objectivity
Oblong Industries
OpenText Corporation
Opera Solutions
Optimal Plus
Oracle Corporation
Palantir Technologies
Panasonic Corporation/Arimo
Panorama Software
Paxata
Pepperdata
Phocas Software
Pivotal Software
Prognoz
Progress Software Corporation
Provalis Research
Pure Storage
PwC (PricewaterhouseCoopers International)
Pyramid Analytics
Qlik
Qrama/Tengu
Quantum Corporation
Qubole
Rackspace
Radius Intelligence
RapidMiner
Recorded Future
Red Hat
Redis Labs
RedPoint Global
Reltio
RStudio
Rubrik/Datos IO
Ryft
Sailthru
Salesforce.com
Salient Management Company
Samsung Group
SAP
SAS Institute
ScaleOut Software
Seagate Technology
Sinequa
SiSense
Sizmek
SnapLogic
Snowflake Computing
Software AG
Splice Machine
Splunk
Strategy Companion Corporation
Stratio
Streamlio
StreamSets
Striim
Sumo Logic
Supermicro (Super Micro Computer)
Syncsort
SynerScope
SYNTASA
Tableau Software
Talend
Tamr
TARGIT
TCS (Tata Consultancy Services)
Teradata Corporation
Thales/Guavus
ThoughtSpot
TIBCO Software
Toshiba Corporation
Transwarp
Trifacta
Unifi Software
Unravel Data
VANTIQ
VMware
VoltDB
WANdisco
Waterline Data
Western Digital Corporation
WhereScape
WiPro
Wolfram Research
Workday
Xplenty
Yellowfin BI
Yseop
Zendesk
Zoomdata
Zucchetti

Chapter 10: Conclusion & Strategic Recommendations
Why is the Market Poised to Grow?
Geographic Outlook: Which Countries Offer the Highest Growth Potential?
Big Data is for Everyone
Addressing Customer Expectations with Data-Driven Financial Services
The Importance of AI (Artificial Intelligence) & Machine Learning
Impact of Blockchain on Big Data Processing
Growing Use of Alternative Data Sources
Adoption of Cloud Platforms to Address On-Premise System Limitations
Data Security & Privacy Concerns
Emergence of Data-Driven Cybersecurity for Financial Services
Recommendations
Big Data Hardware, Software & Professional Services Providers
Financial Services Industry Stakeholders

List of Figures

Figure 1: Hadoop Architecture
Figure 2: Reactive vs. Proactive Analytics
Figure 3: Distribution of Big Data Investments in the Financial Services Industry, by Application Area: 2018 (%)
Figure 4: Progressive Corporation's Use of Big Data for Auto Insurance
Figure 5: Capital One's Purple Rain Framework
Figure 6: TransferWise's Money Transfer Platform
Figure 7: qplum's HFT (High Frequency Trading) Architecture
Figure 8: Use of Alternative Data Sources in FICO Score XD 2
Figure 9: Kabbage's Data-Driven Decision Engine
Figure 10: Digital & Alternative Data Sources for LenddoEFL's Credit Scoring Platform
Figure 11: Comparison of Data Sources Between Upstart & Traditional Lenders
Figure 12: Big Data Roadmap in the Financial Services Industry: 2018 – 2030
Figure 13: Big Data Value Chain in the Financial Services Industry
Figure 14: Key Aspects of Big Data Standardization
Figure 15: Global Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million)
Figure 16: Global Big Data Revenue in the Financial Services Industry, by Hardware, Software & Professional Services: 2018 – 2030 ($ Million)
Figure 17: Global Big Data Revenue in the Financial Services Industry, by Submarket: 2018 – 2030 ($ Million)
Figure 18: Global Big Data Storage and Compute Infrastructure Submarket Revenue in the Financial Services Industry: 2018 – 2030 ($ Million)
Figure 19: Global Big Data Networking Infrastructure Submarket Revenue in the Financial Services Industry: 2018 – 2030 ($ Million)
Figure 20: Global Big Data Hadoop & Infrastructure Software Submarket Revenue in the Financial Services Industry: 2018 – 2030 ($ Million)
Figure 21: Global Big Data SQL Submarket Revenue in the Financial Services Industry: 2018 – 2030 ($ Million)
Figure 22: Global Big Data NoSQL Submarket Revenue in the Financial Services Industry: 2018 – 2030 ($ Million)
Figure 23: Global Big Data Analytic Platforms & Applications Submarket Revenue in the Financial Services Industry: 2018 – 2030 ($ Million)
Figure 24: Global Big Data Cloud Platforms Submarket Revenue in the Financial Services Industry: 2018 – 2030 ($ Million)
Figure 25: Global Big Data Professional Services Submarket Revenue in the Financial Services Industry: 2018 – 2030 ($ Million)
Figure 26: Global Big Data Revenue in the Financial Services Industry, by Application Area: 2018 – 2030 ($ Million)
Figure 27: Global Big Data Revenue in Personal & Business Banking: 2018 – 2030 ($ Million)
Figure 28: Global Big Data Revenue in Investment Banking & Capital Markets: 2018 – 2030 ($ Million)
Figure 29: Global Big Data Revenue in Insurance Services: 2018 – 2030 ($ Million)
Figure 30: Global Big Data Revenue in Credit Cards & Payment Processing: 2018 – 2030 ($ Million)
Figure 31: Global Big Data Revenue in Lending & Financing: 2018 – 2030 ($ Million)
Figure 32: Global Big Data Revenue in Asset & Wealth Management: 2018 – 2030 ($ Million)
Figure 33: Global Big Data Revenue in the Financial Services Industry, by Use Case: 2018 – 2030 ($ Million)
Figure 34: Global Big Data Revenue in Personalized & Targeted Marketing for Financial Services: 2018 – 2030 ($ Million)
Figure 35: Global Big Data Revenue in Customer Service & Experience for Financial Services: 2018 – 2030 ($ Million)
Figure 36: Global Big Data Revenue in Product Innovation & Development for Financial Services: 2018 – 2030 ($ Million)
Figure 37: Global Big Data Revenue in Risk Modeling, Management & Reporting for Financial Services: 2018 – 2030 ($ Million)
Figure 38: Global Big Data Revenue in Fraud Detection & Prevention for Financial Services: 2018 – 2030 ($ Million)
Figure 39: Global Big Data Revenue in Robotic & Intelligent Process Automation for Financial Services: 2018 – 2030 ($ Million)
Figure 40: Global Big Data Revenue in Usage & Analytics-Based Insurance: 2018 – 2030 ($ Million)
Figure 41: Global Big Data Revenue in Credit Scoring & Control: 2018 – 2030 ($ Million)
Figure 42: Global Big Data Revenue in Data-Driven Trading & Investment: 2018 – 2030 ($ Million)
Figure 43: Global Big Data Revenue in Third Party Data Monetization for Financial Services: 2018 – 2030 ($ Million)
Figure 44: Global Big Data Revenue in Other Use Cases for Financial Services: 2018 – 2030 ($ Million)
Figure 45: Big Data Revenue in the Financial Services Industry, by Region: 2018 – 2030 ($ Million)
Figure 46: Asia Pacific Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million)
Figure 47: Asia Pacific Big Data Revenue in the Financial Services Industry, by Country: 2018 – 2030 ($ Million)
Figure 48: Australia Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million)
Figure 49: China Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million)
Figure 50: India Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million)
Figure 51: Indonesia Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million)
Figure 52: Japan Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million)
Figure 53: Malaysia Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million)
Figure 54: Pakistan Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million)
Figure 55: Philippines Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million)
Figure 56: Singapore Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million)
Figure 57: South Korea Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million)
Figure 58: Taiwan Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million)
Figure 59: Thailand Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million)
Figure 60: Rest of Asia Pacific Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million)
Figure 61: Eastern Europe Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million)
Figure 62: Eastern Europe Big Data Revenue in the Financial Services Industry, by Country: 2018 – 2030 ($ Million)
Figure 63: Czech Republic Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million)
Figure 64: Poland Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million)
Figure 65: Russia Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million)
Figure 66: Rest of Eastern Europe Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million)
Figure 67: Latin & Central America Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million)
Figure 68: Latin & Central America Big Data Revenue in the Financial Services Industry, by Country: 2018 – 2030 ($ Million)
Figure 69: Argentina Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million)
Figure 70: Brazil Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million)
Figure 71: Mexico Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million)
Figure 72: Rest of Latin & Central America Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million)
Figure 73: Middle East & Africa Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million)
Figure 74: Middle East & Africa Big Data Revenue in the Financial Services Industry, by Country: 2018 – 2030 ($ Million)
Figure 75: Israel Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million)
Figure 76: Qatar Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million)
Figure 77: Saudi Arabia Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million)
Figure 78: South Africa Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million)
Figure 79: UAE Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million)
Figure 80: Rest of the Middle East & Africa Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million)
Figure 81: North America Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million)
Figure 82: North America Big Data Revenue in the Financial Services Industry, by Country: 2018 – 2030 ($ Million)
Figure 83: Canada Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million)
Figure 84: USA Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million)
Figure 85: Western Europe Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million)
Figure 86: Western Europe Big Data Revenue in the Financial Services Industry, by Country: 2018 – 2030 ($ Million)
Figure 87: Denmark Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million)
Figure 88: Finland Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million)
Figure 89: France Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million)
Figure 90: Germany Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million)
Figure 91: Italy Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million)
Figure 92: Netherlands Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million)
Figure 93: Norway Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million)
Figure 94: Spain Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million)
Figure 95: Sweden Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million)
Figure 96: UK Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million)
Figure 97: Rest of Western Europe Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million)

Companies Profiled

List of Companies Mentioned
1010data
Absolutdata
Acadian Asset Management
Accenture
Actian Corporation
Adaptive Insights
Adobe Systems
Advizor Solutions
AeroSpike
AFS Technologies
Alation
Algorithmia
Alluxio
Alphabet
ALTEN
Alteryx
AMD (Advanced Micro Devices)
American Express
Anaconda
Apixio
AQR Capital Management
Arcadia Data
Arimo
ARM
ASF (Apache Software Foundation)
AtScale
Attivio
Attunity
Automated Insights
Avant
AVORA
AWS (Amazon Web Services)
AXA
Axiomatics
Ayasdi
BackOffice Associates
Basho Technologies
BCG (Boston Consulting Group)
Bedrock Data
BetterWorks
Big Panda
BigML
Birst
Bitam
BlackRock
Bloomberg
Blue Medora
BlueData Software
BlueTalon
BMC Software
BOARD International
Booz Allen Hamilton
Boxever
CACI International
Cambridge Semantics
Capgemini
Capital One
Cazena
CBA/CommBank (Commonwealth Bank of Australia)
Centrifuge Systems
CenturyLink
Chartio
Cigna
Cisco Systems
Civis Analytics
ClearStory Data
Cloudability
Cloudera
Cloudian
Clustrix
CognitiveScale
Collibra
Concurrent Technology
Confluent
Contexti
Couchbase
Crate.io
Cray
Credit Suisse
CSA (Cloud Security Alliance)
CSCC (Cloud Standards Customer Council)
Databricks
Dataiku
Datalytyx
Datameer
DataRobot
DataStax
Datawatch Corporation
Datos IO
DDN (DataDirect Networks)
Decisyon
Dell Technologies
Deloitte
Demandbase
Denodo Technologies
Deutsche Bank
Dianomic Systems
Digital Reasoning Systems
Dimensional Insight
DMG  (Data Mining Group)
Dolphin Enterprise Solutions Corporation
Domino Data Lab
Domo
Dremio
DriveScale
Druva
Dun and Bradstreet
Dundas Data Visualization
DXC Technology
Eagle Alpha
Elastic
Engineering Group (Engineering Ingegneria Informatica)
EnterpriseDB Corporation
eQ Technologic
Equifax
Ericsson
Erwin
EV? (Big Cloud Analytics)
EXASOL
EXL (ExlService Holdings)
Facebook
Factset
FICO (Fair Isaac Corporation)
Figure Eight
FogHorn Systems
Fractal Analytics
Franz
Fujitsu
Fuzzy Logix
Gainsight
GE (General Electric)
Glassbeam
GoodData Corporation
Google
Grakn Labs
Greenwave Systems
GridGain Systems
Guavus
GuidePoint
H2O.ai
Hanse Orga Group
HarperDB
HCL Technologies
Hedvig
Hitachi Vantara
Hortonworks
HPE (Hewlett Packard Enterprise)
HSBC Group
Huawei
HVR
HyperScience
HyTrust
IBM Corporation
iDashboards
IDERA
IEC (International Electrotechnical Commission)
IEEE (Institute of Electrical and Electronics Engineers)
Ignite Technologies
Imanis Data
Impetus Technologies
INCITS (InterNational Committee for Information Technology Standards)
Incorta
InetSoft Technology Corporation
InfluxData
Infogix
Infor
Informatica
Information Builders
Infosys
Infoworks
Insightsoftware.com
InsightSquared
Intel Corporation
Interana
InterSystems Corporation
ISO (International Organization for Standardization)
ITU (International Telecommunication Union)
Jedox
Jethro
Jinfonet Software
JNB (Japan Net Bank)
JPMorgan Chase & Co.
Juniper Networks
Kabbage
KALEAO
Keen IO
Keyrus
Kinetica
KNIME
Kognitio
Kyvos Insights
LeanXcale
LenddoEFL
Lexalytics
Lexmark International
Lightbend
Linux Foundation
Logi Analytics
Logical Clocks
Longview Solutions
Looker Data Sciences
LucidWorks
Luminoso Technologies
Maana
Man Group
Manthan Software Services
MapD Technologies
MapR Technologies
MariaDB Corporation
MarkLogic Corporation
Mastercard
Mathworks
Melissa
MemSQL
Metric Insights
Microsoft Corporation
MicroStrategy
Minitab
MongoDB
Mu Sigma
NEC Corporation
Neo4j
NetApp
Nimbix
Nokia
NTT Data Corporation
Numerify
NuoDB
NVIDIA Corporation
OASIS (Organization for the Advancement of Structured Information Standards)
Objectivity
Oblong Industries
ODaF (Open Data Foundation)
ODCA (Open Data Center Alliance)
OGC (Open Geospatial Consortium)
OpenText Corporation
Opera Solutions
Optimal Plus
Oracle Corporation
OTP Bank
Palantir Technologies
Panasonic Corporation
Panorama Software
Paxata
Pepperdata
Phocas Software
Pivotal Software
Prognoz
Progress Software Corporation
Progressive Corporation
Provalis Research
Pure Storage
PwC (PricewaterhouseCoopers International)
Pyramid Analytics
Qlik
qplum
Qrama/Tengu
Quandl
Quantum Corporation
Qubole
Rackspace
Radius Intelligence
RapidMiner
RavenPack
Recorded Future
Red Hat
Redis Labs
RedPoint Global
Reltio
RStudio
Rubrik
Ryft
S&P's (Standard & Poor's)
Sailthru
Salesforce.com
Salient Management Company
Samsung Fire & Marine Insurance
Samsung Group
SAP
SAS Institute
ScaleOut Software
Seagate Technology
Shinhan Card
Sinequa
SiSense
Sizmek
SnapLogic
Snowflake Computing
Software AG
Splice Machine
Splunk
Strategy Companion Corporation
Stratio
Streamlio
StreamSets
Striim
Sumo Logic
Supermicro (Super Micro Computer)
Syncsort
SynerScope
SYNTASA
Tableau Software
Talend
Tamr
TARGIT
TCS (Tata Consultancy Services)
Teradata Corporation
Thales
Thomson Reuters
ThoughtSpot
TIBCO Software
Tidemark
TM Forum
Toshiba Corporation
TPC (Transaction Processing Performance Council)
TransferWise
Transwarp
Trifacta
Two Sigma Investments
U.S. NIST (National Institute of Standards and Technology)
Unifi Software
UnitedHealth Group
Unravel Data
Upstart
VANTIQ
Vecima Networks
Visa
VMware
VoltDB
W3C (World Wide Web Consortium)
WANdisco
Waterline Data
Western Digital Corporation
Western Union
WhereScape
WiPro
Wolfram Research
Workday
Xplenty
Yellowfin BI
Yseop
Zendesk
Zoomdata
Zucchetti
Zurich Insurance Group

Market Reports Center © Copyright 2018 All rights reserved.

wire transfer
HDFC
ssl