Big Data in the Financial Services Industry: 2018 – 2030 – Opportunities, Challenges, Strategies & Forecasts
“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.
SNS Telecom & IT 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.
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.
SNS Telecom & IT 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.
1 CHAPTER 1: INTRODUCTION
1.1 Executive Summary
1.2 Topics Covered
1.3 Forecast Segmentation
1.4 Key Questions Answered
1.5 Key Findings
1.6 Methodology
1.7 Target Audience
1.8 Companies & Organizations Mentioned
2 CHAPTER 2: AN OVERVIEW OF BIG DATA
2.1 What is Big Data?
2.2 Key Approaches to Big Data Processing
2.2.1 Hadoop
2.2.2 NoSQL
2.2.3 MPAD (Massively Parallel Analytic Databases)
2.2.4 In-Memory Processing
2.2.5 Stream Processing Technologies
2.2.6 Spark
2.2.7 Other Databases & Analytic Technologies
2.3 Key Characteristics of Big Data
2.3.1 Volume
2.3.2 Velocity
2.3.3 Variety
2.3.4 Value
2.4 Market Growth Drivers
2.4.1 Awareness of Benefits
2.4.2 Maturation of Big Data Platforms
2.4.3 Continued Investments by Web Giants, Governments & Enterprises
2.4.4 Growth of Data Volume, Velocity & Variety
2.4.5 Vendor Commitments & Partnerships
2.4.6 Technology Trends Lowering Entry Barriers
2.5 Market Barriers
2.5.1 Lack of Analytic Specialists
2.5.2 Uncertain Big Data Strategies
2.5.3 Organizational Resistance to Big Data Adoption
2.5.4 Technical Challenges: Scalability & Maintenance
2.5.5 Security & Privacy Concerns
3 CHAPTER 3: BIG DATA ANALYTICS
3.1 What are Big Data Analytics?
3.2 The Importance of Analytics
3.3 Reactive vs. Proactive Analytics
3.4 Customer vs. Operational Analytics
3.5 Technology & Implementation Approaches
3.5.1 Grid Computing
3.5.2 In-Database Processing
3.5.3 In-Memory Analytics
3.5.4 Machine Learning & Data Mining
3.5.5 Predictive Analytics
3.5.6 NLP (Natural Language Processing)
3.5.7 Text Analytics
3.5.8 Visual Analytics
3.5.9 Graph Analytics
3.5.10 Social Media, IT & Telco Network Analytics
4 CHAPTER 4: BUSINESS CASE & APPLICATIONS IN THE FINANCIAL SERVICES INDUSTRY
4.1 Overview & Investment Potential
4.2 Industry Specific Market Growth Drivers
4.3 Industry Specific Market Barriers
4.4 Key Application Areas
4.4.1 Personal & Business Banking
4.4.2 Investment Banking & Capital Markets
4.4.3 Insurance Services
4.4.4 Credit Cards & Payments Processing
4.4.5 Lending & Financing
4.4.6 Asset & Wealth Management
4.5 Use Cases
4.5.1 Personalized & Targeted Marketing
4.5.2 Customer Service & Experience
4.5.3 Product Innovation & Development
4.5.4 Risk Modeling, Management & Reporting
4.5.5 Fraud Detection & Prevention
4.5.6 Robotic & Intelligent Process Automation
4.5.7 Usage & Analytics-Based Insurance
4.5.8 Credit Scoring & Control
4.5.9 Data-Driven Trading & Investment
4.5.10 Third Party Data Monetization
4.5.11 Other Use Cases
5 CHAPTER 5: FINANCIAL SERVICES INDUSTRY CASE STUDIES
5.1 Banks
5.1.1 CBA/CommBank (Commonwealth Bank of Australia): Driving Customer Engagement with Big Data
5.1.2 Credit Suisse: Enhancing Regulatory Compliance with Big Data
5.1.3 Deutsche Bank: Quantifying the Importance of Intangible Assets with Big Data
5.1.4 HSBC Group: Combating Money Laundering & Financial Crime with Big Data
5.1.5 JPMorgan Chase & Co.: Enabling Responsible Prospecting with Big Data
5.1.6 OTP Bank: Reducing Loan Defaults with Big Data
5.2 Insurers
5.2.1 AXA: Simplifying Customer Interaction with Big Data
5.2.2 Cigna: Streamlining Health Insurance Claims with Big Data
5.2.3 Progressive Corporation: Rewarding Safe Drivers & Improving Traffic Safety with Big Data
5.2.4 Samsung Fire & Marine Insurance: Transforming Insurance Underwriting with Big Data
5.2.5 UnitedHealth Group: Enhancing Patient Care & Value with Big Data
5.2.6 Zurich Insurance Group: Improving Risk Management with Big Data
5.3 Credit Card & Payment Processing Specialists
5.3.1 American Express: Enabling Real-Time Targeting Marketing with Big Data
5.3.2 Capital One: Enriching Cybersecurity with Big Data
5.3.3 Mastercard: Predictively Combating Account Related Fraud with Big Data
5.3.4 TransferWise: Simplifying International Money Transfers With Big Data
5.3.5 Visa: Saving Billions of Dollars with Big Data
5.3.6 Western Union: Personalizing Customer Experience with Big Data
5.4 Asset & Wealth Management Firms
5.4.1 Acadian Asset Management: Exploiting Market Inefficiencies with Big Data
5.4.2 AQR Capital Management: Finding Profitable Trading Patterns with Big Data
5.4.3 BlackRock: Gleaning Economic Clues with Big Data
5.4.4 Man Group: Accelerating Trades & Investment Modeling with Big Data
5.4.5 qplum: Optimizing Client Portfolios with Big Data
5.4.6 Two Sigma Investments: Making Systematic Trades with Big Data
5.5 Lenders & Other Stakeholders
5.5.1 Avant: Streamlining Borrowing with Big Data
5.5.2 Equifax: Helping Make Informed Credit Decisions with Big Data
5.5.3 FICO (Fair Isaac Corporation): Expanding Access to Credit with Big Data
5.5.4 Kabbage: Empowering Small Business Lending with Big Data
5.5.5 LenddoEFL: Increasing Access to Financial Services in Emerging Economies with Big Data
5.5.6 Upstart: Facilitating Smarter Loans with Big Data
6 CHAPTER 6: FUTURE ROADMAP & VALUE CHAIN
6.1 Future Roadmap
6.1.1 Pre-2020: Investments in Advanced Analytics & AI (Artificial Intelligence)
6.1.2 2020 – 2025: Large-Scale Adoption of Cloud-Based Big Data Platforms
6.1.3 2025 – 2030: Towards the Digitization of Financial Services
6.2 The Big Data Value Chain
6.2.1 Hardware Providers
6.2.1.1 Storage & Compute Infrastructure Providers
6.2.1.2 Networking Infrastructure Providers
6.2.2 Software Providers
6.2.2.1 Hadoop & Infrastructure Software Providers
6.2.2.2 SQL & NoSQL Providers
6.2.2.3 Analytic Platform & Application Software Providers
6.2.2.4 Cloud Platform Providers
6.2.3 Professional Services Providers
6.2.4 End-to-End Solution Providers
6.2.5 Financial Services Industry
7 CHAPTER 7: STANDARDIZATION & REGULATORY INITIATIVES
7.1 ASF (Apache Software Foundation)
7.1.1 Management of Hadoop
7.1.2 Big Data Projects Beyond Hadoop
7.2 CSA (Cloud Security Alliance)
7.2.1 BDWG (Big Data Working Group)
7.3 CSCC (Cloud Standards Customer Council)
7.3.1 Big Data Working Group
7.4 DMG (Data Mining Group)
7.4.1 PMML (Predictive Model Markup Language) Working Group
7.4.2 PFA (Portable Format for Analytics) Working Group
7.5 IEEE (Institute of Electrical and Electronics Engineers)
7.5.1 Big Data Initiative
7.6 INCITS (InterNational Committee for Information Technology Standards)
7.6.1 Big Data Technical Committee
7.7 ISO (International Organization for Standardization)
7.7.1 ISO/IEC JTC 1/SC 32: Data Management and Interchange
7.7.2 ISO/IEC JTC 1/SC 38: Cloud Computing and Distributed Platforms
7.7.3 ISO/IEC JTC 1/SC 27: IT Security Techniques
7.7.4 ISO/IEC JTC 1/WG 9: Big Data
7.7.5 Collaborations with Other ISO Work Groups
7.8 ITU (International Telecommunication Union)
7.8.1 ITU-T Y.3600: Big Data – Cloud Computing Based Requirements and Capabilities
7.8.2 Other Deliverables Through SG (Study Group) 13 on Future Networks
7.8.3 Other Relevant Work
7.9 Linux Foundation
7.9.1 ODPi (Open Ecosystem of Big Data)
7.10 NIST (National Institute of Standards and Technology)
7.10.1 NBD-PWG (NIST Big Data Public Working Group)
7.11 OASIS (Organization for the Advancement of Structured Information Standards)
7.11.1 Technical Committees
7.12 ODaF (Open Data Foundation)
7.12.1 Big Data Accessibility
7.13 ODCA (Open Data Center Alliance)
7.13.1 Work on Big Data
7.14 OGC (Open Geospatial Consortium)
7.14.1 Big Data DWG (Domain Working Group)
7.15 TM Forum
7.15.1 Big Data Analytics Strategic Program
7.16 TPC (Transaction Processing Performance Council)
7.16.1 TPC-BDWG (TPC Big Data Working Group)
7.17 W3C (World Wide Web Consortium)
7.17.1 Big Data Community Group
7.17.2 Open Government Community Group
8 CHAPTER 8: MARKET SIZING & FORECASTS
8.1 Global Outlook for the Big Data in the Financial Services Industry
8.2 Hardware, Software & Professional Services Segmentation
8.3 Horizontal Submarket Segmentation
8.4 Hardware Submarkets
8.4.1 Storage and Compute Infrastructure
8.4.2 Networking Infrastructure
8.5 Software Submarkets
8.5.1 Hadoop & Infrastructure Software
8.5.2 SQL
8.5.3 NoSQL
8.5.4 Analytic Platforms & Applications
8.5.5 Cloud Platforms
8.6 Professional Services Submarket
8.6.1 Professional Services
8.7 Application Area Segmentation
8.7.1 Personal & Business Banking
8.7.2 Investment Banking & Capital Markets
8.7.3 Insurance Services
8.7.4 Credit Cards & Payment Processing
8.7.5 Lending & Financing
8.7.6 Asset & Wealth Management
8.8 Use Case Segmentation
8.8.1 Personalized & Targeted Marketing
8.8.2 Customer Service & Experience
8.8.3 Product Innovation & Development
8.8.4 Risk Modeling, Management & Reporting
8.8.5 Fraud Detection & Prevention
8.8.6 Robotic & Intelligent Process Automation
8.8.7 Usage & Analytics-Based Insurance
8.8.8 Credit Scoring & Control
8.8.9 Data-Driven Trading & Investment
8.8.10 Third Party Data Monetization
8.8.11 Other Use Cases
8.9 Regional Outlook
8.10 Asia Pacific
8.10.1 Country Level Segmentation
8.10.2 Australia
8.10.3 China
8.10.4 India
8.10.5 Indonesia
8.10.6 Japan
8.10.7 Malaysia
8.10.8 Pakistan
8.10.9 Philippines
8.10.10 Singapore
8.10.11 South Korea
8.10.12 Taiwan
8.10.13 Thailand
8.10.14 Rest of Asia Pacific
8.11 Eastern Europe
8.11.1 Country Level Segmentation
8.11.2 Czech Republic
8.11.3 Poland
8.11.4 Russia
8.11.5 Rest of Eastern Europe
8.12 Latin & Central America
8.12.1 Country Level Segmentation
8.12.2 Argentina
8.12.3 Brazil
8.12.4 Mexico
8.12.5 Rest of Latin & Central America
8.13 Middle East & Africa
8.13.1 Country Level Segmentation
8.13.2 Israel
8.13.3 Qatar
8.13.4 Saudi Arabia
8.13.5 South Africa
8.13.6 UAE
8.13.7 Rest of the Middle East & Africa
8.14 North America
8.14.1 Country Level Segmentation
8.14.2 Canada
8.14.3 USA
8.15 Western Europe
8.15.1 Country Level Segmentation
8.15.2 Denmark
8.15.3 Finland
8.15.4 France
8.15.5 Germany
8.15.6 Italy
8.15.7 Netherlands
8.15.8 Norway
8.15.9 Spain
8.15.10 Sweden
8.15.11 UK
8.15.12 Rest of Western Europe
9 CHAPTER 9: VENDOR LANDSCAPE
9.1 1010data
9.2 Absolutdata
9.3 Accenture
9.4 Actian Corporation/HCL Technologies
9.5 Adaptive Insights
9.6 Adobe Systems
9.7 Advizor Solutions
9.8 AeroSpike
9.9 AFS Technologies
9.10 Alation
9.11 Algorithmia
9.12 Alluxio
9.13 ALTEN
9.14 Alteryx
9.15 AMD (Advanced Micro Devices)
9.16 Anaconda
9.17 Apixio
9.18 Arcadia Data
9.19 ARM
9.20 AtScale
9.21 Attivio
9.22 Attunity
9.23 Automated Insights
9.24 AVORA
9.25 AWS (Amazon Web Services)
9.26 Axiomatics
9.27 Ayasdi
9.28 BackOffice Associates
9.29 Basho Technologies
9.30 BCG (Boston Consulting Group)
9.31 Bedrock Data
9.32 BetterWorks
9.33 Big Panda
9.34 BigML
9.35 Bitam
9.36 Blue Medora
9.37 BlueData Software
9.38 BlueTalon
9.39 BMC Software
9.40 BOARD International
9.41 Booz Allen Hamilton
9.42 Boxever
9.43 CACI International
9.44 Cambridge Semantics
9.45 Capgemini
9.46 Cazena
9.47 Centrifuge Systems
9.48 CenturyLink
9.49 Chartio
9.50 Cisco Systems
9.51 Civis Analytics
9.52 ClearStory Data
9.53 Cloudability
9.54 Cloudera
9.55 Cloudian
9.56 Clustrix
9.57 CognitiveScale
9.58 Collibra
9.59 Concurrent Technology/Vecima Networks
9.60 Confluent
9.61 Contexti
9.62 Couchbase
9.63 Crate.io
9.64 Cray
9.65 Databricks
9.66 Dataiku
9.67 Datalytyx
9.68 Datameer
9.69 DataRobot
9.70 DataStax
9.71 Datawatch Corporation
9.72 DDN (DataDirect Networks)
9.73 Decisyon
9.74 Dell Technologies
9.75 Deloitte
9.76 Demandbase
9.77 Denodo Technologies
9.78 Dianomic Systems
9.79 Digital Reasoning Systems
9.80 Dimensional Insight
9.81 Dolphin Enterprise Solutions Corporation/Hanse Orga Group
9.82 Domino Data Lab
9.83 Domo
9.84 Dremio
9.85 DriveScale
9.86 Druva
9.87 Dundas Data Visualization
9.88 DXC Technology
9.89 Elastic
9.90 Engineering Group (Engineering Ingegneria Informatica)
9.91 EnterpriseDB Corporation
9.92 eQ Technologic
9.93 Ericsson
9.94 Erwin
9.95 EVO (Big Cloud Analytics)
9.96 EXASOL
9.97 EXL (ExlService Holdings)
9.98 Facebook
9.99 FICO (Fair Isaac Corporation)
9.100 Figure Eight
9.101 FogHorn Systems
9.102 Fractal Analytics
9.103 Franz
9.104 Fujitsu
9.105 Fuzzy Logix
9.106 Gainsight
9.107 GE (General Electric)
9.108 Glassbeam
9.109 GoodData Corporation
9.110 Google/Alphabet
9.111 Grakn Labs
9.112 Greenwave Systems
9.113 GridGain Systems
9.114 H2O.ai
9.115 HarperDB
9.116 Hedvig
9.117 Hitachi Vantara
9.118 Hortonworks
9.119 HPE (Hewlett Packard Enterprise)
9.120 Huawei
9.121 HVR
9.122 HyperScience
9.123 HyTrust
9.124 IBM Corporation
9.125 iDashboards
9.126 IDERA
9.127 Ignite Technologies
9.128 Imanis Data
9.129 Impetus Technologies
9.130 Incorta
9.131 InetSoft Technology Corporation
9.132 InfluxData
9.133 Infogix
9.134 Infor/Birst
9.135 Informatica
9.136 Information Builders
9.137 Infosys
9.138 Infoworks
9.139 Insightsoftware.com
9.140 InsightSquared
9.141 Intel Corporation
9.142 Interana
9.143 InterSystems Corporation
9.144 Jedox
9.145 Jethro
9.146 Jinfonet Software
9.147 Juniper Networks
9.148 KALEAO
9.149 Keen IO
9.150 Keyrus
9.151 Kinetica
9.152 KNIME
9.153 Kognitio
9.154 Kyvos Insights
9.155 LeanXcale
9.156 Lexalytics
9.157 Lexmark International
9.158 Lightbend
9.159 Logi Analytics
9.160 Logical Clocks
9.161 Longview Solutions/Tidemark
9.162 Looker Data Sciences
9.163 LucidWorks
9.164 Luminoso Technologies
9.165 Maana
9.166 Manthan Software Services
9.167 MapD Technologies
9.168 MapR Technologies
9.169 MariaDB Corporation
9.170 MarkLogic Corporation
9.171 Mathworks
9.172 Melissa
9.173 MemSQL
9.174 Metric Insights
9.175 Microsoft Corporation
9.176 MicroStrategy
9.177 Minitab
9.178 MongoDB
9.179 Mu Sigma
9.180 NEC Corporation
9.181 Neo4j
9.182 NetApp
9.183 Nimbix
9.184 Nokia
9.185 NTT Data Corporation
9.186 Numerify
9.187 NuoDB
9.188 NVIDIA Corporation
9.189 Objectivity
9.190 Oblong Industries
9.191 OpenText Corporation
9.192 Opera Solutions
9.193 Optimal Plus
9.194 Oracle Corporation
9.195 Palantir Technologies
9.196 Panasonic Corporation/Arimo
9.197 Panorama Software
9.198 Paxata
9.199 Pepperdata
9.200 Phocas Software
9.201 Pivotal Software
9.202 Prognoz
9.203 Progress Software Corporation
9.204 Provalis Research
9.205 Pure Storage
9.206 PwC (PricewaterhouseCoopers International)
9.207 Pyramid Analytics
9.208 Qlik
9.209 Qrama/Tengu
9.210 Quantum Corporation
9.211 Qubole
9.212 Rackspace
9.213 Radius Intelligence
9.214 RapidMiner
9.215 Recorded Future
9.216 Red Hat
9.217 Redis Labs
9.218 RedPoint Global
9.219 Reltio
9.220 RStudio
9.221 Rubrik/Datos IO
9.222 Ryft
9.223 Sailthru
9.224 Salesforce.com
9.225 Salient Management Company
9.226 Samsung Group
9.227 SAP
9.228 SAS Institute
9.229 ScaleOut Software
9.230 Seagate Technology
9.231 Sinequa
9.232 SiSense
9.233 Sizmek
9.234 SnapLogic
9.235 Snowflake Computing
9.236 Software AG
9.237 Splice Machine
9.238 Splunk
9.239 Strategy Companion Corporation
9.240 Stratio
9.241 Streamlio
9.242 StreamSets
9.243 Striim
9.244 Sumo Logic
9.245 Supermicro (Super Micro Computer)
9.246 Syncsort
9.247 SynerScope
9.248 SYNTASA
9.249 Tableau Software
9.250 Talend
9.251 Tamr
9.252 TARGIT
9.253 TCS (Tata Consultancy Services)
9.254 Teradata Corporation
9.255 Thales/Guavus
9.256 ThoughtSpot
9.257 TIBCO Software
9.258 Toshiba Corporation
9.259 Transwarp
9.260 Trifacta
9.261 Unifi Software
9.262 Unravel Data
9.263 VANTIQ
9.264 VMware
9.265 VoltDB
9.266 WANdisco
9.267 Waterline Data
9.268 Western Digital Corporation
9.269 WhereScape
9.270 WiPro
9.271 Wolfram Research
9.272 Workday
9.273 Xplenty
9.274 Yellowfin BI
9.275 Yseop
9.276 Zendesk
9.277 Zoomdata
9.278 Zucchetti
10 CHAPTER 10: CONCLUSION & STRATEGIC RECOMMENDATIONS
10.1 Why is the Market Poised to Grow?
10.2 Geographic Outlook: Which Countries Offer the Highest Growth Potential?
10.3 Big Data is for Everyone
10.4 Addressing Customer Expectations with Data-Driven Financial Services
10.5 The Importance of AI (Artificial Intelligence) & Machine Learning
10.6 Impact of Blockchain on Big Data Processing
10.7 Growing Use of Alternative Data Sources
10.8 Adoption of Cloud Platforms to Address On-Premise System Limitations
10.9 Data Security & Privacy Concerns
10.10 Emergence of Data-Driven Cybersecurity for Financial Services
10.11 Recommendations
10.11.1 Big Data Hardware, Software & Professional Services Providers
10.11.2 Financial Services Industry Stakeholders
1.1 Executive Summary
1.2 Topics Covered
1.3 Forecast Segmentation
1.4 Key Questions Answered
1.5 Key Findings
1.6 Methodology
1.7 Target Audience
1.8 Companies & Organizations Mentioned
2 CHAPTER 2: AN OVERVIEW OF BIG DATA
2.1 What is Big Data?
2.2 Key Approaches to Big Data Processing
2.2.1 Hadoop
2.2.2 NoSQL
2.2.3 MPAD (Massively Parallel Analytic Databases)
2.2.4 In-Memory Processing
2.2.5 Stream Processing Technologies
2.2.6 Spark
2.2.7 Other Databases & Analytic Technologies
2.3 Key Characteristics of Big Data
2.3.1 Volume
2.3.2 Velocity
2.3.3 Variety
2.3.4 Value
2.4 Market Growth Drivers
2.4.1 Awareness of Benefits
2.4.2 Maturation of Big Data Platforms
2.4.3 Continued Investments by Web Giants, Governments & Enterprises
2.4.4 Growth of Data Volume, Velocity & Variety
2.4.5 Vendor Commitments & Partnerships
2.4.6 Technology Trends Lowering Entry Barriers
2.5 Market Barriers
2.5.1 Lack of Analytic Specialists
2.5.2 Uncertain Big Data Strategies
2.5.3 Organizational Resistance to Big Data Adoption
2.5.4 Technical Challenges: Scalability & Maintenance
2.5.5 Security & Privacy Concerns
3 CHAPTER 3: BIG DATA ANALYTICS
3.1 What are Big Data Analytics?
3.2 The Importance of Analytics
3.3 Reactive vs. Proactive Analytics
3.4 Customer vs. Operational Analytics
3.5 Technology & Implementation Approaches
3.5.1 Grid Computing
3.5.2 In-Database Processing
3.5.3 In-Memory Analytics
3.5.4 Machine Learning & Data Mining
3.5.5 Predictive Analytics
3.5.6 NLP (Natural Language Processing)
3.5.7 Text Analytics
3.5.8 Visual Analytics
3.5.9 Graph Analytics
3.5.10 Social Media, IT & Telco Network Analytics
4 CHAPTER 4: BUSINESS CASE & APPLICATIONS IN THE FINANCIAL SERVICES INDUSTRY
4.1 Overview & Investment Potential
4.2 Industry Specific Market Growth Drivers
4.3 Industry Specific Market Barriers
4.4 Key Application Areas
4.4.1 Personal & Business Banking
4.4.2 Investment Banking & Capital Markets
4.4.3 Insurance Services
4.4.4 Credit Cards & Payments Processing
4.4.5 Lending & Financing
4.4.6 Asset & Wealth Management
4.5 Use Cases
4.5.1 Personalized & Targeted Marketing
4.5.2 Customer Service & Experience
4.5.3 Product Innovation & Development
4.5.4 Risk Modeling, Management & Reporting
4.5.5 Fraud Detection & Prevention
4.5.6 Robotic & Intelligent Process Automation
4.5.7 Usage & Analytics-Based Insurance
4.5.8 Credit Scoring & Control
4.5.9 Data-Driven Trading & Investment
4.5.10 Third Party Data Monetization
4.5.11 Other Use Cases
5 CHAPTER 5: FINANCIAL SERVICES INDUSTRY CASE STUDIES
5.1 Banks
5.1.1 CBA/CommBank (Commonwealth Bank of Australia): Driving Customer Engagement with Big Data
5.1.2 Credit Suisse: Enhancing Regulatory Compliance with Big Data
5.1.3 Deutsche Bank: Quantifying the Importance of Intangible Assets with Big Data
5.1.4 HSBC Group: Combating Money Laundering & Financial Crime with Big Data
5.1.5 JPMorgan Chase & Co.: Enabling Responsible Prospecting with Big Data
5.1.6 OTP Bank: Reducing Loan Defaults with Big Data
5.2 Insurers
5.2.1 AXA: Simplifying Customer Interaction with Big Data
5.2.2 Cigna: Streamlining Health Insurance Claims with Big Data
5.2.3 Progressive Corporation: Rewarding Safe Drivers & Improving Traffic Safety with Big Data
5.2.4 Samsung Fire & Marine Insurance: Transforming Insurance Underwriting with Big Data
5.2.5 UnitedHealth Group: Enhancing Patient Care & Value with Big Data
5.2.6 Zurich Insurance Group: Improving Risk Management with Big Data
5.3 Credit Card & Payment Processing Specialists
5.3.1 American Express: Enabling Real-Time Targeting Marketing with Big Data
5.3.2 Capital One: Enriching Cybersecurity with Big Data
5.3.3 Mastercard: Predictively Combating Account Related Fraud with Big Data
5.3.4 TransferWise: Simplifying International Money Transfers With Big Data
5.3.5 Visa: Saving Billions of Dollars with Big Data
5.3.6 Western Union: Personalizing Customer Experience with Big Data
5.4 Asset & Wealth Management Firms
5.4.1 Acadian Asset Management: Exploiting Market Inefficiencies with Big Data
5.4.2 AQR Capital Management: Finding Profitable Trading Patterns with Big Data
5.4.3 BlackRock: Gleaning Economic Clues with Big Data
5.4.4 Man Group: Accelerating Trades & Investment Modeling with Big Data
5.4.5 qplum: Optimizing Client Portfolios with Big Data
5.4.6 Two Sigma Investments: Making Systematic Trades with Big Data
5.5 Lenders & Other Stakeholders
5.5.1 Avant: Streamlining Borrowing with Big Data
5.5.2 Equifax: Helping Make Informed Credit Decisions with Big Data
5.5.3 FICO (Fair Isaac Corporation): Expanding Access to Credit with Big Data
5.5.4 Kabbage: Empowering Small Business Lending with Big Data
5.5.5 LenddoEFL: Increasing Access to Financial Services in Emerging Economies with Big Data
5.5.6 Upstart: Facilitating Smarter Loans with Big Data
6 CHAPTER 6: FUTURE ROADMAP & VALUE CHAIN
6.1 Future Roadmap
6.1.1 Pre-2020: Investments in Advanced Analytics & AI (Artificial Intelligence)
6.1.2 2020 – 2025: Large-Scale Adoption of Cloud-Based Big Data Platforms
6.1.3 2025 – 2030: Towards the Digitization of Financial Services
6.2 The Big Data Value Chain
6.2.1 Hardware Providers
6.2.1.1 Storage & Compute Infrastructure Providers
6.2.1.2 Networking Infrastructure Providers
6.2.2 Software Providers
6.2.2.1 Hadoop & Infrastructure Software Providers
6.2.2.2 SQL & NoSQL Providers
6.2.2.3 Analytic Platform & Application Software Providers
6.2.2.4 Cloud Platform Providers
6.2.3 Professional Services Providers
6.2.4 End-to-End Solution Providers
6.2.5 Financial Services Industry
7 CHAPTER 7: STANDARDIZATION & REGULATORY INITIATIVES
7.1 ASF (Apache Software Foundation)
7.1.1 Management of Hadoop
7.1.2 Big Data Projects Beyond Hadoop
7.2 CSA (Cloud Security Alliance)
7.2.1 BDWG (Big Data Working Group)
7.3 CSCC (Cloud Standards Customer Council)
7.3.1 Big Data Working Group
7.4 DMG (Data Mining Group)
7.4.1 PMML (Predictive Model Markup Language) Working Group
7.4.2 PFA (Portable Format for Analytics) Working Group
7.5 IEEE (Institute of Electrical and Electronics Engineers)
7.5.1 Big Data Initiative
7.6 INCITS (InterNational Committee for Information Technology Standards)
7.6.1 Big Data Technical Committee
7.7 ISO (International Organization for Standardization)
7.7.1 ISO/IEC JTC 1/SC 32: Data Management and Interchange
7.7.2 ISO/IEC JTC 1/SC 38: Cloud Computing and Distributed Platforms
7.7.3 ISO/IEC JTC 1/SC 27: IT Security Techniques
7.7.4 ISO/IEC JTC 1/WG 9: Big Data
7.7.5 Collaborations with Other ISO Work Groups
7.8 ITU (International Telecommunication Union)
7.8.1 ITU-T Y.3600: Big Data – Cloud Computing Based Requirements and Capabilities
7.8.2 Other Deliverables Through SG (Study Group) 13 on Future Networks
7.8.3 Other Relevant Work
7.9 Linux Foundation
7.9.1 ODPi (Open Ecosystem of Big Data)
7.10 NIST (National Institute of Standards and Technology)
7.10.1 NBD-PWG (NIST Big Data Public Working Group)
7.11 OASIS (Organization for the Advancement of Structured Information Standards)
7.11.1 Technical Committees
7.12 ODaF (Open Data Foundation)
7.12.1 Big Data Accessibility
7.13 ODCA (Open Data Center Alliance)
7.13.1 Work on Big Data
7.14 OGC (Open Geospatial Consortium)
7.14.1 Big Data DWG (Domain Working Group)
7.15 TM Forum
7.15.1 Big Data Analytics Strategic Program
7.16 TPC (Transaction Processing Performance Council)
7.16.1 TPC-BDWG (TPC Big Data Working Group)
7.17 W3C (World Wide Web Consortium)
7.17.1 Big Data Community Group
7.17.2 Open Government Community Group
8 CHAPTER 8: MARKET SIZING & FORECASTS
8.1 Global Outlook for the Big Data in the Financial Services Industry
8.2 Hardware, Software & Professional Services Segmentation
8.3 Horizontal Submarket Segmentation
8.4 Hardware Submarkets
8.4.1 Storage and Compute Infrastructure
8.4.2 Networking Infrastructure
8.5 Software Submarkets
8.5.1 Hadoop & Infrastructure Software
8.5.2 SQL
8.5.3 NoSQL
8.5.4 Analytic Platforms & Applications
8.5.5 Cloud Platforms
8.6 Professional Services Submarket
8.6.1 Professional Services
8.7 Application Area Segmentation
8.7.1 Personal & Business Banking
8.7.2 Investment Banking & Capital Markets
8.7.3 Insurance Services
8.7.4 Credit Cards & Payment Processing
8.7.5 Lending & Financing
8.7.6 Asset & Wealth Management
8.8 Use Case Segmentation
8.8.1 Personalized & Targeted Marketing
8.8.2 Customer Service & Experience
8.8.3 Product Innovation & Development
8.8.4 Risk Modeling, Management & Reporting
8.8.5 Fraud Detection & Prevention
8.8.6 Robotic & Intelligent Process Automation
8.8.7 Usage & Analytics-Based Insurance
8.8.8 Credit Scoring & Control
8.8.9 Data-Driven Trading & Investment
8.8.10 Third Party Data Monetization
8.8.11 Other Use Cases
8.9 Regional Outlook
8.10 Asia Pacific
8.10.1 Country Level Segmentation
8.10.2 Australia
8.10.3 China
8.10.4 India
8.10.5 Indonesia
8.10.6 Japan
8.10.7 Malaysia
8.10.8 Pakistan
8.10.9 Philippines
8.10.10 Singapore
8.10.11 South Korea
8.10.12 Taiwan
8.10.13 Thailand
8.10.14 Rest of Asia Pacific
8.11 Eastern Europe
8.11.1 Country Level Segmentation
8.11.2 Czech Republic
8.11.3 Poland
8.11.4 Russia
8.11.5 Rest of Eastern Europe
8.12 Latin & Central America
8.12.1 Country Level Segmentation
8.12.2 Argentina
8.12.3 Brazil
8.12.4 Mexico
8.12.5 Rest of Latin & Central America
8.13 Middle East & Africa
8.13.1 Country Level Segmentation
8.13.2 Israel
8.13.3 Qatar
8.13.4 Saudi Arabia
8.13.5 South Africa
8.13.6 UAE
8.13.7 Rest of the Middle East & Africa
8.14 North America
8.14.1 Country Level Segmentation
8.14.2 Canada
8.14.3 USA
8.15 Western Europe
8.15.1 Country Level Segmentation
8.15.2 Denmark
8.15.3 Finland
8.15.4 France
8.15.5 Germany
8.15.6 Italy
8.15.7 Netherlands
8.15.8 Norway
8.15.9 Spain
8.15.10 Sweden
8.15.11 UK
8.15.12 Rest of Western Europe
9 CHAPTER 9: VENDOR LANDSCAPE
9.1 1010data
9.2 Absolutdata
9.3 Accenture
9.4 Actian Corporation/HCL Technologies
9.5 Adaptive Insights
9.6 Adobe Systems
9.7 Advizor Solutions
9.8 AeroSpike
9.9 AFS Technologies
9.10 Alation
9.11 Algorithmia
9.12 Alluxio
9.13 ALTEN
9.14 Alteryx
9.15 AMD (Advanced Micro Devices)
9.16 Anaconda
9.17 Apixio
9.18 Arcadia Data
9.19 ARM
9.20 AtScale
9.21 Attivio
9.22 Attunity
9.23 Automated Insights
9.24 AVORA
9.25 AWS (Amazon Web Services)
9.26 Axiomatics
9.27 Ayasdi
9.28 BackOffice Associates
9.29 Basho Technologies
9.30 BCG (Boston Consulting Group)
9.31 Bedrock Data
9.32 BetterWorks
9.33 Big Panda
9.34 BigML
9.35 Bitam
9.36 Blue Medora
9.37 BlueData Software
9.38 BlueTalon
9.39 BMC Software
9.40 BOARD International
9.41 Booz Allen Hamilton
9.42 Boxever
9.43 CACI International
9.44 Cambridge Semantics
9.45 Capgemini
9.46 Cazena
9.47 Centrifuge Systems
9.48 CenturyLink
9.49 Chartio
9.50 Cisco Systems
9.51 Civis Analytics
9.52 ClearStory Data
9.53 Cloudability
9.54 Cloudera
9.55 Cloudian
9.56 Clustrix
9.57 CognitiveScale
9.58 Collibra
9.59 Concurrent Technology/Vecima Networks
9.60 Confluent
9.61 Contexti
9.62 Couchbase
9.63 Crate.io
9.64 Cray
9.65 Databricks
9.66 Dataiku
9.67 Datalytyx
9.68 Datameer
9.69 DataRobot
9.70 DataStax
9.71 Datawatch Corporation
9.72 DDN (DataDirect Networks)
9.73 Decisyon
9.74 Dell Technologies
9.75 Deloitte
9.76 Demandbase
9.77 Denodo Technologies
9.78 Dianomic Systems
9.79 Digital Reasoning Systems
9.80 Dimensional Insight
9.81 Dolphin Enterprise Solutions Corporation/Hanse Orga Group
9.82 Domino Data Lab
9.83 Domo
9.84 Dremio
9.85 DriveScale
9.86 Druva
9.87 Dundas Data Visualization
9.88 DXC Technology
9.89 Elastic
9.90 Engineering Group (Engineering Ingegneria Informatica)
9.91 EnterpriseDB Corporation
9.92 eQ Technologic
9.93 Ericsson
9.94 Erwin
9.95 EVO (Big Cloud Analytics)
9.96 EXASOL
9.97 EXL (ExlService Holdings)
9.98 Facebook
9.99 FICO (Fair Isaac Corporation)
9.100 Figure Eight
9.101 FogHorn Systems
9.102 Fractal Analytics
9.103 Franz
9.104 Fujitsu
9.105 Fuzzy Logix
9.106 Gainsight
9.107 GE (General Electric)
9.108 Glassbeam
9.109 GoodData Corporation
9.110 Google/Alphabet
9.111 Grakn Labs
9.112 Greenwave Systems
9.113 GridGain Systems
9.114 H2O.ai
9.115 HarperDB
9.116 Hedvig
9.117 Hitachi Vantara
9.118 Hortonworks
9.119 HPE (Hewlett Packard Enterprise)
9.120 Huawei
9.121 HVR
9.122 HyperScience
9.123 HyTrust
9.124 IBM Corporation
9.125 iDashboards
9.126 IDERA
9.127 Ignite Technologies
9.128 Imanis Data
9.129 Impetus Technologies
9.130 Incorta
9.131 InetSoft Technology Corporation
9.132 InfluxData
9.133 Infogix
9.134 Infor/Birst
9.135 Informatica
9.136 Information Builders
9.137 Infosys
9.138 Infoworks
9.139 Insightsoftware.com
9.140 InsightSquared
9.141 Intel Corporation
9.142 Interana
9.143 InterSystems Corporation
9.144 Jedox
9.145 Jethro
9.146 Jinfonet Software
9.147 Juniper Networks
9.148 KALEAO
9.149 Keen IO
9.150 Keyrus
9.151 Kinetica
9.152 KNIME
9.153 Kognitio
9.154 Kyvos Insights
9.155 LeanXcale
9.156 Lexalytics
9.157 Lexmark International
9.158 Lightbend
9.159 Logi Analytics
9.160 Logical Clocks
9.161 Longview Solutions/Tidemark
9.162 Looker Data Sciences
9.163 LucidWorks
9.164 Luminoso Technologies
9.165 Maana
9.166 Manthan Software Services
9.167 MapD Technologies
9.168 MapR Technologies
9.169 MariaDB Corporation
9.170 MarkLogic Corporation
9.171 Mathworks
9.172 Melissa
9.173 MemSQL
9.174 Metric Insights
9.175 Microsoft Corporation
9.176 MicroStrategy
9.177 Minitab
9.178 MongoDB
9.179 Mu Sigma
9.180 NEC Corporation
9.181 Neo4j
9.182 NetApp
9.183 Nimbix
9.184 Nokia
9.185 NTT Data Corporation
9.186 Numerify
9.187 NuoDB
9.188 NVIDIA Corporation
9.189 Objectivity
9.190 Oblong Industries
9.191 OpenText Corporation
9.192 Opera Solutions
9.193 Optimal Plus
9.194 Oracle Corporation
9.195 Palantir Technologies
9.196 Panasonic Corporation/Arimo
9.197 Panorama Software
9.198 Paxata
9.199 Pepperdata
9.200 Phocas Software
9.201 Pivotal Software
9.202 Prognoz
9.203 Progress Software Corporation
9.204 Provalis Research
9.205 Pure Storage
9.206 PwC (PricewaterhouseCoopers International)
9.207 Pyramid Analytics
9.208 Qlik
9.209 Qrama/Tengu
9.210 Quantum Corporation
9.211 Qubole
9.212 Rackspace
9.213 Radius Intelligence
9.214 RapidMiner
9.215 Recorded Future
9.216 Red Hat
9.217 Redis Labs
9.218 RedPoint Global
9.219 Reltio
9.220 RStudio
9.221 Rubrik/Datos IO
9.222 Ryft
9.223 Sailthru
9.224 Salesforce.com
9.225 Salient Management Company
9.226 Samsung Group
9.227 SAP
9.228 SAS Institute
9.229 ScaleOut Software
9.230 Seagate Technology
9.231 Sinequa
9.232 SiSense
9.233 Sizmek
9.234 SnapLogic
9.235 Snowflake Computing
9.236 Software AG
9.237 Splice Machine
9.238 Splunk
9.239 Strategy Companion Corporation
9.240 Stratio
9.241 Streamlio
9.242 StreamSets
9.243 Striim
9.244 Sumo Logic
9.245 Supermicro (Super Micro Computer)
9.246 Syncsort
9.247 SynerScope
9.248 SYNTASA
9.249 Tableau Software
9.250 Talend
9.251 Tamr
9.252 TARGIT
9.253 TCS (Tata Consultancy Services)
9.254 Teradata Corporation
9.255 Thales/Guavus
9.256 ThoughtSpot
9.257 TIBCO Software
9.258 Toshiba Corporation
9.259 Transwarp
9.260 Trifacta
9.261 Unifi Software
9.262 Unravel Data
9.263 VANTIQ
9.264 VMware
9.265 VoltDB
9.266 WANdisco
9.267 Waterline Data
9.268 Western Digital Corporation
9.269 WhereScape
9.270 WiPro
9.271 Wolfram Research
9.272 Workday
9.273 Xplenty
9.274 Yellowfin BI
9.275 Yseop
9.276 Zendesk
9.277 Zoomdata
9.278 Zucchetti
10 CHAPTER 10: CONCLUSION & STRATEGIC RECOMMENDATIONS
10.1 Why is the Market Poised to Grow?
10.2 Geographic Outlook: Which Countries Offer the Highest Growth Potential?
10.3 Big Data is for Everyone
10.4 Addressing Customer Expectations with Data-Driven Financial Services
10.5 The Importance of AI (Artificial Intelligence) & Machine Learning
10.6 Impact of Blockchain on Big Data Processing
10.7 Growing Use of Alternative Data Sources
10.8 Adoption of Cloud Platforms to Address On-Premise System Limitations
10.9 Data Security & Privacy Concerns
10.10 Emergence of Data-Driven Cybersecurity for Financial Services
10.11 Recommendations
10.11.1 Big Data Hardware, Software & Professional Services Providers
10.11.2 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
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)
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
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)
