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

Published By :

SNS Telecom & IT

Published Date : Jul 2018

Category :

Banking

No. of Pages : 521 Pages

“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 sub markets, 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 Sub markets

  • 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.

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
  • OmniSci
  • 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

Table of Contents

1 Chapter 1: Introduction 22
1.1 Executive Summary 22
1.2 Topics Covered 24
1.3 Forecast Segmentation 25
1.4 Key Questions Answered 28
1.5 Key Findings 29
1.6 Methodology 30
1.7 Target Audience 31
1.8 Companies & Organizations Mentioned 32

2 Chapter 2: An Overview of Big Data 35
2.1 What is Big Data? 35
2.2 Key Approaches to Big Data Processing 35
2.2.1 Hadoop 36
2.2.2 NoSQL 38
2.2.3 MPAD (Massively Parallel Analytic Databases) 38
2.2.4 In-Memory Processing 39
2.2.5 Stream Processing Technologies 39
2.2.6 Spark 40
2.2.7 Other Databases & Analytic Technologies 40
2.3 Key Characteristics of Big Data 41
2.3.1 Volume 41
2.3.2 Velocity 41
2.3.3 Variety 41
2.3.4 Value 42
2.4 Market Growth Drivers 42
2.4.1 Awareness of Benefits 42
2.4.2 Maturation of Big Data Platforms 42
2.4.3 Continued Investments by Web Giants, Governments & Enterprises 43
2.4.4 Growth of Data Volume, Velocity & Variety 43
2.4.5 Vendor Commitments & Partnerships 43
2.4.6 Technology Trends Lowering Entry Barriers 44
2.5 Market Barriers 44
2.5.1 Lack of Analytic Specialists 44
2.5.2 Uncertain Big Data Strategies 44
2.5.3 Organizational Resistance to Big Data Adoption 45
2.5.4 Technical Challenges: Scalability & Maintenance 45
2.5.5 Security & Privacy Concerns 45

3 Chapter 3: Big Data Analytics 46
3.1 What are Big Data Analytics? 46
3.2 The Importance of Analytics 46
3.3 Reactive vs. Proactive Analytics 47
3.4 Customer vs. Operational Analytics 47
3.5 Technology & Implementation Approaches 48
3.5.1 Grid Computing 48
3.5.2 In-Database Processing 48
3.5.3 In-Memory Analytics 49
3.5.4 Machine Learning & Data Mining 49
3.5.5 Predictive Analytics 50
3.5.6 NLP (Natural Language Processing) 50
3.5.7 Text Analytics 51
3.5.8 Visual Analytics 51
3.5.9 Graph Analytics 52
3.5.10 Social Media, IT & Telco Network Analytics 52

4 Chapter 4: Business Case & Applications in the Financial Services Industry 54
4.1 Overview & Investment Potential 54
4.2 Industry Specific Market Growth Drivers 55
4.3 Industry Specific Market Barriers 56
4.4 Key Application Areas 58
4.4.1 Personal & Business Banking 58
4.4.2 Investment Banking & Capital Markets 59
4.4.3 Insurance Services 59
4.4.4 Credit Cards & Payments Processing 60
4.4.5 Lending & Financing 60
4.4.6 Asset & Wealth Management 61
4.5 Use Cases 62
4.5.1 Personalized & Targeted Marketing 62
4.5.2 Customer Service & Experience 63
4.5.3 Product Innovation & Development 64
4.5.4 Risk Modeling, Management & Reporting 64
4.5.5 Fraud Detection & Prevention 65
4.5.6 Robotic & Intelligent Process Automation 66
4.5.7 Usage & Analytics-Based Insurance 67
4.5.8 Credit Scoring & Control 67
4.5.9 Data-Driven Trading & Investment 68
4.5.10 Third Party Data Monetization 68
4.5.11 Other Use Cases 69

5 Chapter 5: Financial Services Industry Case Studies 70
5.1 Banks 70
5.1.1 CBA/CommBank (Commonwealth Bank of Australia): Driving Customer Engagement with Big Data 70
5.1.2 Credit Suisse: Enhancing Regulatory Compliance with Big Data 72
5.1.3 Deutsche Bank: Quantifying the Importance of Intangible Assets with Big Data 74
5.1.4 HSBC Group: Combating Money Laundering & Financial Crime with Big Data 77
5.1.5 JPMorgan Chase & Co.: Enabling Responsible Prospecting with Big Data 79
5.1.6 OTP Bank: Reducing Loan Defaults with Big Data 81
5.2 Insurers 83
5.2.1 AXA: Simplifying Customer Interaction with Big Data 83
5.2.2 Cigna: Streamlining Health Insurance Claims with Big Data 87
5.2.3 Progressive Corporation: Rewarding Safe Drivers & Improving Traffic Safety with Big Data 89
5.2.4 Samsung Fire & Marine Insurance: Transforming Insurance Underwriting with Big Data 92
5.2.5 UnitedHealth Group: Enhancing Patient Care & Value with Big Data 94
5.2.6 Zurich Insurance Group: Improving Risk Management with Big Data 96
5.3 Credit Card & Payment Processing Specialists 98
5.3.1 American Express: Enabling Real-Time Targeting Marketing with Big Data 98
5.3.2 Capital One: Enriching Cybersecurity with Big Data 100
5.3.3 Mastercard: Predictively Combating Account Related Fraud with Big Data 103
5.3.4 TransferWise: Simplifying International Money Transfers With Big Data 105
5.3.5 Visa: Saving Billions of Dollars with Big Data 107
5.3.6 Western Union: Personalizing Customer Experience with Big Data 109
5.4 Asset & Wealth Management Firms 111
5.4.1 Acadian Asset Management: Exploiting Market Inefficiencies with Big Data 111
5.4.2 AQR Capital Management: Finding Profitable Trading Patterns with Big Data 113
5.4.3 BlackRock: Gleaning Economic Clues with Big Data 115
5.4.4 Man Group: Accelerating Trades & Investment Modeling with Big Data 118
5.4.5 qplum: Optimizing Client Portfolios with Big Data 120
5.4.6 Two Sigma Investments: Making Systematic Trades with Big Data 122
5.5 Lenders & Other Stakeholders 124
5.5.1 Avant: Streamlining Borrowing with Big Data 124
5.5.2 Equifax: Helping Make Informed Credit Decisions with Big Data 126
5.5.3 FICO (Fair Isaac Corporation): Expanding Access to Credit with Big Data 128
5.5.4 Kabbage: Empowering Small Business Lending with Big Data 131
5.5.5 LenddoEFL: Increasing Access to Financial Services in Emerging Economies with Big Data 133
5.5.6 Upstart: Facilitating Smarter Loans with Big Data 135

6 Chapter 6: Future Roadmap & Value Chain 137
6.1 Future Roadmap 137
6.1.1 Pre-2020: Investments in Advanced Analytics & AI (Artificial Intelligence) 137
6.1.2 2020 – 2025: Large-Scale Adoption of Cloud-Based Big Data Platforms 138
6.1.3 2025 – 2030: Towards the Digitization of Financial Services 139
6.2 The Big Data Value Chain 140
6.2.1 Hardware Providers 140
6.2.1.1 Storage & Compute Infrastructure Providers 140
6.2.1.2 Networking Infrastructure Providers 141
6.2.2 Software Providers 141
6.2.2.1 Hadoop & Infrastructure Software Providers 142
6.2.2.2 SQL & NoSQL Providers 142
6.2.2.3 Analytic Platform & Application Software Providers 142
6.2.2.4 Cloud Platform Providers 142
6.2.3 Professional Services Providers 143
6.2.4 End-to-End Solution Providers 143
6.2.5 Financial Services Industry 143

7 Chapter 7: Standardization & Regulatory Initiatives 144
7.1 ASF (Apache Software Foundation) 144
7.1.1 Management of Hadoop 144
7.1.2 Big Data Projects Beyond Hadoop 144
7.2 CSA (Cloud Security Alliance) 148
7.2.1 BDWG (Big Data Working Group) 148
7.3 CSCC (Cloud Standards Customer Council) 149
7.3.1 Big Data Working Group 149
7.4 DMG  (Data Mining Group) 150
7.4.1 PMML (Predictive Model Markup Language) Working Group 150
7.4.2 PFA (Portable Format for Analytics) Working Group 150
7.5 IEEE (Institute of Electrical and Electronics Engineers) 150
7.5.1 Big Data Initiative 151
7.6 INCITS (InterNational Committee for Information Technology Standards) 152
7.6.1 Big Data Technical Committee 152
7.7 ISO (International Organization for Standardization) 153
7.7.1 ISO/IEC JTC 1/SC 32: Data Management and Interchange 153
7.7.2 ISO/IEC JTC 1/SC 38: Cloud Computing and Distributed Platforms 154
7.7.3 ISO/IEC JTC 1/SC 27: IT Security Techniques 154
7.7.4 ISO/IEC JTC 1/WG 9: Big Data 154
7.7.5 Collaborations with Other ISO Work Groups 155
7.8 ITU (International Telecommunication Union) 156
7.8.1 ITU-T Y.3600: Big Data – Cloud Computing Based Requirements and Capabilities 156
7.8.2 Other Deliverables Through SG (Study Group) 13 on Future Networks 157
7.8.3 Other Relevant Work 157
7.9 Linux Foundation 158
7.9.1 ODPi (Open Ecosystem of Big Data) 158
7.10 NIST (National Institute of Standards and Technology) 158
7.10.1 NBD-PWG (NIST Big Data Public Working Group) 158
7.11 OASIS (Organization for the Advancement of Structured Information Standards) 159
7.11.1 Technical Committees 159
7.12 ODaF (Open Data Foundation) 160
7.12.1 Big Data Accessibility 160
7.13 ODCA (Open Data Center Alliance) 160
7.13.1 Work on Big Data 161
7.14 OGC (Open Geospatial Consortium) 161
7.14.1 Big Data DWG (Domain Working Group) 161
7.15 TM Forum 161
7.15.1 Big Data Analytics Strategic Program 162
7.16 TPC (Transaction Processing Performance Council) 162
7.16.1 TPC-BDWG (TPC Big Data Working Group) 162
7.17 W3C (World Wide Web Consortium) 162
7.17.1 Big Data Community Group 163
7.17.2 Open Government Community Group 163

8 Chapter 8: Market Sizing & Forecasts 164
8.1 Global Outlook for the Big Data in the Financial Services Industry 164
8.2 Hardware, Software & Professional Services Segmentation 165
8.3 Horizontal Submarket Segmentation 166
8.4 Hardware Submarkets 167
8.4.1 Storage and Compute Infrastructure 167
8.4.2 Networking Infrastructure 167
8.5 Software Submarkets 168
8.5.1 Hadoop & Infrastructure Software 168
8.5.2 SQL 168
8.5.3 NoSQL 169
8.5.4 Analytic Platforms & Applications 169
8.5.5 Cloud Platforms 170
8.6 Professional Services Submarket 170
8.6.1 Professional Services 170
8.7 Application Area Segmentation 171
8.7.1 Personal & Business Banking 172
8.7.2 Investment Banking & Capital Markets 172
8.7.3 Insurance Services 173
8.7.4 Credit Cards & Payment Processing 173
8.7.5 Lending & Financing 174
8.7.6 Asset & Wealth Management 174
8.8 Use Case Segmentation 175
8.8.1 Personalized & Targeted Marketing 176
8.8.2 Customer Service & Experience 176
8.8.3 Product Innovation & Development 177
8.8.4 Risk Modeling, Management & Reporting 177
8.8.5 Fraud Detection & Prevention 178
8.8.6 Robotic & Intelligent Process Automation 178
8.8.7 Usage & Analytics-Based Insurance 179
8.8.8 Credit Scoring & Control 179
8.8.9 Data-Driven Trading & Investment 180
8.8.10 Third Party Data Monetization 180
8.8.11 Other Use Cases 181
8.9 Regional Outlook 182
8.10 Asia Pacific 183
8.10.1 Country Level Segmentation 183
8.10.2 Australia 184
8.10.3 China 184
8.10.4 India 185
8.10.5 Indonesia 185
8.10.6 Japan 186
8.10.7 Malaysia 186
8.10.8 Pakistan 187
8.10.9 Philippines 187
8.10.10 Singapore 188
8.10.11 South Korea 188
8.10.12 Taiwan 189
8.10.13 Thailand 189
8.10.14 Rest of Asia Pacific 190
8.11 Eastern Europe 191
8.11.1 Country Level Segmentation 191
8.11.2 Czech Republic 192
8.11.3 Poland 192
8.11.4 Russia 193
8.11.5 Rest of Eastern Europe 193
8.12 Latin & Central America 194
8.12.1 Country Level Segmentation 194
8.12.2 Argentina 195
8.12.3 Brazil 195
8.12.4 Mexico 196
8.12.5 Rest of Latin & Central America 196
8.13 Middle East & Africa 197
8.13.1 Country Level Segmentation 197
8.13.2 Israel 198
8.13.3 Qatar 198
8.13.4 Saudi Arabia 199
8.13.5 South Africa 199
8.13.6 UAE 200
8.13.7 Rest of the Middle East & Africa 200
8.14 North America 201
8.14.1 Country Level Segmentation 201
8.14.2 Canada 202
8.14.3 USA 202
8.15 Western Europe 203
8.15.1 Country Level Segmentation 203
8.15.2 Denmark 204
8.15.3 Finland 204
8.15.4 France 205
8.15.5 Germany 205
8.15.6 Italy 206
8.15.7 Netherlands 206
8.15.8 Norway 207
8.15.9 Spain 207
8.15.10 Sweden 208
8.15.11 UK 208
8.15.12 Rest of Western Europe 209

9 Chapter 9: Vendor Landscape 210
9.1 1010data 210
9.2 Absolutdata 211
9.3 Accenture 212
9.4 Actian Corporation/HCL Technologies 213
9.5 Adaptive Insights 215
9.6 Adobe Systems 216
9.7 Advizor Solutions 218
9.8 AeroSpike 219
9.9 AFS Technologies 220
9.10 Alation 221
9.11 Algorithmia 222
9.12 Alluxio 223
9.13 ALTEN 224
9.14 Alteryx 225
9.15 AMD (Advanced Micro Devices) 226
9.16 Anaconda 227
9.17 Apixio 228
9.18 Arcadia Data 229
9.19 ARM 230
9.20 AtScale 231
9.21 Attivio 232
9.22 Attunity 233
9.23 Automated Insights 234
9.24 AVORA 235
9.25 AWS (Amazon Web Services) 236
9.26 Axiomatics 238
9.27 Ayasdi 239
9.28 BackOffice Associates 240
9.29 Basho Technologies 241
9.30 BCG (Boston Consulting Group) 242
9.31 Bedrock Data 243
9.32 BetterWorks 244
9.33 Big Panda 245
9.34 BigML 246
9.35 Bitam 247
9.36 Blue Medora 248
9.37 BlueData Software 249
9.38 BlueTalon 250
9.39 BMC Software 251
9.40 BOARD International 252
9.41 Booz Allen Hamilton 253
9.42 Boxever 254
9.43 CACI International 255
9.44 Cambridge Semantics 256
9.45 Capgemini 257
9.46 Cazena 258
9.47 Centrifuge Systems 259
9.48 CenturyLink 260
9.49 Chartio 261
9.50 Cisco Systems 262
9.51 Civis Analytics 263
9.52 ClearStory Data 264
9.53 Cloudability 265
9.54 Cloudera 266
9.55 Cloudian 267
9.56 Clustrix 268
9.57 CognitiveScale 269
9.58 Collibra 270
9.59 Concurrent Technology/Vecima Networks 271
9.60 Confluent 272
9.61 Contexti 273
9.62 Couchbase 274
9.63 Crate.io 275
9.64 Cray 276
9.65 Databricks 277
9.66 Dataiku 278
9.67 Datalytyx 279
9.68 Datameer 280
9.69 DataRobot 281
9.70 DataStax 282
9.71 Datawatch Corporation 283
9.72 DDN (DataDirect Networks) 284
9.73 Decisyon 285
9.74 Dell Technologies 286
9.75 Deloitte 287
9.76 Demandbase 288
9.77 Denodo Technologies 289
9.78 Dianomic Systems 290
9.79 Digital Reasoning Systems 291
9.80 Dimensional Insight 292
9.81 Dolphin Enterprise Solutions Corporation/Hanse Orga Group 293
9.82 Domino Data Lab 294
9.83 Domo 295
9.84 Dremio 296
9.85 DriveScale 297
9.86 Druva 298
9.87 Dundas Data Visualization 299
9.88 DXC Technology 300
9.89 Elastic 301
9.90 Engineering Group (Engineering Ingegneria Informatica) 302
9.91 EnterpriseDB Corporation 303
9.92 eQ Technologic 304
9.93 Ericsson 305
9.94 Erwin 306
9.95 EV? (Big Cloud Analytics) 307
9.96 EXASOL 308
9.97 EXL (ExlService Holdings) 309
9.98 Facebook 310
9.99 FICO (Fair Isaac Corporation) 311
9.100 Figure Eight 312
9.101 FogHorn Systems 313
9.102 Fractal Analytics 314
9.103 Franz 315
9.104 Fujitsu 316
9.105 Fuzzy Logix 318
9.106 Gainsight 319
9.107 GE (General Electric) 320
9.108 Glassbeam 321
9.109 GoodData Corporation 322
9.110 Google/Alphabet 323
9.111 Grakn Labs 325
9.112 Greenwave Systems 326
9.113 GridGain Systems 327
9.114 H2O.ai 328
9.115 HarperDB 329
9.116 Hedvig 330
9.117 Hitachi Vantara 331
9.118 Hortonworks 332
9.119 HPE (Hewlett Packard Enterprise) 333
9.120 Huawei 335
9.121 HVR 336
9.122 HyperScience 337
9.123 HyTrust 338
9.124 IBM Corporation 340
9.125 iDashboards 342
9.126 IDERA 343
9.127 Ignite Technologies 344
9.128 Imanis Data 346
9.129 Impetus Technologies 347
9.130 Incorta 348
9.131 InetSoft Technology Corporation 349
9.132 InfluxData 350
9.133 Infogix 351
9.134 Infor/Birst 352
9.135 Informatica 354
9.136 Information Builders 355
9.137 Infosys 356
9.138 Infoworks 357
9.139 Insightsoftware.com 358
9.140 InsightSquared 359
9.141 Intel Corporation 360
9.142 Interana 361
9.143 InterSystems Corporation 362
9.144 Jedox 363
9.145 Jethro 364
9.146 Jinfonet Software 365
9.147 Juniper Networks 366
9.148 KALEAO 367
9.149 Keen IO 368
9.150 Keyrus 369
9.151 Kinetica 370
9.152 KNIME 371
9.153 Kognitio 372
9.154 Kyvos Insights 373
9.155 LeanXcale 374
9.156 Lexalytics 375
9.157 Lexmark International 377
9.158 Lightbend 378
9.159 Logi Analytics 379
9.160 Logical Clocks 380
9.161 Longview Solutions/Tidemark 381
9.162 Looker Data Sciences 383
9.163 LucidWorks 384
9.164 Luminoso Technologies 385
9.165 Maana 386
9.166 Manthan Software Services 387
9.167 MapD Technologies 388
9.168 MapR Technologies 389
9.169 MariaDB Corporation 390
9.170 MarkLogic Corporation 391
9.171 Mathworks 392
9.172 Melissa 393
9.173 MemSQL 394
9.174 Metric Insights 395
9.175 Microsoft Corporation 396
9.176 MicroStrategy 398
9.177 Minitab 399
9.178 MongoDB 400
9.179 Mu Sigma 401
9.180 NEC Corporation 402
9.181 Neo4j 403
9.182 NetApp 404
9.183 Nimbix 405
9.184 Nokia 406
9.185 NTT Data Corporation 407
9.186 Numerify 408
9.187 NuoDB 409
9.188 NVIDIA Corporation 410
9.189 Objectivity 411
9.190 Oblong Industries 412
9.191 OpenText Corporation 413
9.192 Opera Solutions 415
9.193 Optimal Plus 416
9.194 Oracle Corporation 417
9.195 Palantir Technologies 420
9.196 Panasonic Corporation/Arimo 422
9.197 Panorama Software 423
9.198 Paxata 424
9.199 Pepperdata 425
9.200 Phocas Software 426
9.201 Pivotal Software 427
9.202 Prognoz 429
9.203 Progress Software Corporation 430
9.204 Provalis Research 431
9.205 Pure Storage 432
9.206 PwC (PricewaterhouseCoopers International) 433
9.207 Pyramid Analytics 434
9.208 Qlik 435
9.209 Qrama/Tengu 436
9.210 Quantum Corporation 437
9.211 Qubole 438
9.212 Rackspace 439
9.213 Radius Intelligence 440
9.214 RapidMiner 441
9.215 Recorded Future 442
9.216 Red Hat 443
9.217 Redis Labs 444
9.218 RedPoint Global 445
9.219 Reltio 446
9.220 RStudio 447
9.221 Rubrik/Datos IO 448
9.222 Ryft 449
9.223 Sailthru 450
9.224 Salesforce.com 451
9.225 Salient Management Company 452
9.226 Samsung Group 453
9.227 SAP 454
9.228 SAS Institute 455
9.229 ScaleOut Software 456
9.230 Seagate Technology 457
9.231 Sinequa 458
9.232 SiSense 459
9.233 Sizmek 460
9.234 SnapLogic 461
9.235 Snowflake Computing 462
9.236 Software AG 463
9.237 Splice Machine 464
9.238 Splunk 465
9.239 Strategy Companion Corporation 467
9.240 Stratio 468
9.241 Streamlio 469
9.242 StreamSets 470
9.243 Striim 471
9.244 Sumo Logic 472
9.245 Supermicro (Super Micro Computer) 473
9.246 Syncsort 474
9.247 SynerScope 476
9.248 SYNTASA 477
9.249 Tableau Software 478
9.250 Talend 479
9.251 Tamr 480
9.252 TARGIT 481
9.253 TCS (Tata Consultancy Services) 482
9.254 Teradata Corporation 483
9.255 Thales/Guavus 485
9.256 ThoughtSpot 486
9.257 TIBCO Software 487
9.258 Toshiba Corporation 489
9.259 Transwarp 490
9.260 Trifacta 491
9.261 Unifi Software 492
9.262 Unravel Data 493
9.263 VANTIQ 494
9.264 VMware 495
9.265 VoltDB 496
9.266 WANdisco 497
9.267 Waterline Data 498
9.268 Western Digital Corporation 499
9.269 WhereScape 500
9.270 WiPro 501
9.271 Wolfram Research 502
9.272 Workday 504
9.273 Xplenty 506
9.274 Yellowfin BI 507
9.275 Yseop 508
9.276 Zendesk 509
9.277 Zoomdata 510
9.278 Zucchetti 511

10 Chapter 10: Conclusion & Strategic Recommendations 512
10.1 Why is the Market Poised to Grow? 512
10.2 Geographic Outlook: Which Countries Offer the Highest Growth Potential? 513
10.3 Big Data is for Everyone 513
10.4 Addressing Customer Expectations with Data-Driven Financial Services 514
10.5 The Importance of AI (Artificial Intelligence) & Machine Learning 514
10.6 Impact of Blockchain on Big Data Processing 515
10.7 Growing Use of Alternative Data Sources 515
10.8 Adoption of Cloud Platforms to Address On-Premise System Limitations 516
10.9 Data Security & Privacy Concerns 517
10.10 Emergence of Data-Driven Cybersecurity for Financial Services 518
10.11 Recommendations 518
10.11.1 Big Data Hardware, Software & Professional Services Providers 519
10.11.2 Financial Services Industry Stakeholders 519

List of Chart

Figure 1: Hadoop Architecture 39
Figure 2: Reactive vs. Proactive Analytics 50
Figure 3: Distribution of Big Data Investments in the Financial Services Industry, by Application Area: 2018 (%) 57
Figure 4: Progressive Corporation's Use of Big Data for Auto Insurance 93
Figure 5: Capital One's Purple Rain Framework 104
Figure 6: TransferWise's Money Transfer Platform 108
Figure 7: qplum's HFT (High Frequency Trading) Architecture 124
Figure 8: Use of Alternative Data Sources in FICO Score XD 2 132
Figure 9: Kabbage's Data-Driven Decision Engine 134
Figure 10: Digital & Alternative Data Sources for LenddoEFL's Credit Scoring Platform 137
Figure 11: Comparison of Data Sources Between Upstart & Traditional Lenders 138
Figure 12: Big Data Roadmap in the Financial Services Industry: 2018 – 2030 140
Figure 13: Big Data Value Chain in the Financial Services Industry 143
Figure 14: Key Aspects of Big Data Standardization 154
Figure 15: Global Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million) 167
Figure 16: Global Big Data Revenue in the Financial Services Industry, by Hardware, Software & Professional Services: 2018 – 2030 ($ Million) 168
Figure 17: Global Big Data Revenue in the Financial Services Industry, by Submarket: 2018 – 2030 ($ Million) 169
Figure 18: Global Big Data Storage and Compute Infrastructure Submarket Revenue in the Financial Services Industry: 2018 – 2030 ($ Million) 170
Figure 19: Global Big Data Networking Infrastructure Submarket Revenue in the Financial Services Industry: 2018 – 2030 ($ Million) 170
Figure 20: Global Big Data Hadoop & Infrastructure Software Submarket Revenue in the Financial Services Industry: 2018 – 2030 ($ Million) 171
Figure 21: Global Big Data SQL Submarket Revenue in the Financial Services Industry: 2018 – 2030 ($ Million) 171
Figure 22: Global Big Data NoSQL Submarket Revenue in the Financial Services Industry: 2018 – 2030 ($ Million) 172
Figure 23: Global Big Data Analytic Platforms & Applications Submarket Revenue in the Financial Services Industry: 2018 – 2030 ($ Million) 172
Figure 24: Global Big Data Cloud Platforms Submarket Revenue in the Financial Services Industry: 2018 – 2030 ($ Million) 173
Figure 25: Global Big Data Professional Services Submarket Revenue in the Financial Services Industry: 2018 – 2030 ($ Million) 173
Figure 26: Global Big Data Revenue in the Financial Services Industry, by Application Area: 2018 – 2030 ($ Million) 174
Figure 27: Global Big Data Revenue in Personal & Business Banking: 2018 – 2030 ($ Million) 175
Figure 28: Global Big Data Revenue in Investment Banking & Capital Markets: 2018 – 2030 ($ Million) 175
Figure 29: Global Big Data Revenue in Insurance Services: 2018 – 2030 ($ Million) 176
Figure 30: Global Big Data Revenue in Credit Cards & Payment Processing: 2018 – 2030 ($ Million) 176
Figure 31: Global Big Data Revenue in Lending & Financing: 2018 – 2030 ($ Million) 177
Figure 32: Global Big Data Revenue in Asset & Wealth Management: 2018 – 2030 ($ Million) 177
Figure 33: Global Big Data Revenue in the Financial Services Industry, by Use Case: 2018 – 2030 ($ Million) 178
Figure 34: Global Big Data Revenue in Personalized & Targeted Marketing for Financial Services: 2018 – 2030 ($ Million) 179
Figure 35: Global Big Data Revenue in Customer Service & Experience for Financial Services: 2018 – 2030 ($ Million) 179
Figure 36: Global Big Data Revenue in Product Innovation & Development for Financial Services: 2018 – 2030 ($ Million) 180
Figure 37: Global Big Data Revenue in Risk Modeling, Management & Reporting for Financial Services: 2018 – 2030 ($ Million) 180
Figure 38: Global Big Data Revenue in Fraud Detection & Prevention for Financial Services: 2018 – 2030 ($ Million) 181
Figure 39: Global Big Data Revenue in Robotic & Intelligent Process Automation for Financial Services: 2018 – 2030 ($ Million) 181
Figure 40: Global Big Data Revenue in Usage & Analytics-Based Insurance: 2018 – 2030 ($ Million) 182
Figure 41: Global Big Data Revenue in Credit Scoring & Control: 2018 – 2030 ($ Million) 182
Figure 42: Global Big Data Revenue in Data-Driven Trading & Investment: 2018 – 2030 ($ Million) 183
Figure 43: Global Big Data Revenue in Third Party Data Monetization for Financial Services: 2018 – 2030 ($ Million) 183
Figure 44: Global Big Data Revenue in Other Use Cases for Financial Services: 2018 – 2030 ($ Million) 184
Figure 45: Big Data Revenue in the Financial Services Industry, by Region: 2018 – 2030 ($ Million) 185
Figure 46: Asia Pacific Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million) 186
Figure 47: Asia Pacific Big Data Revenue in the Financial Services Industry, by Country: 2018 – 2030 ($ Million) 186
Figure 48: Australia Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million) 187
Figure 49: China Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million) 187
Figure 50: India Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million) 188
Figure 51: Indonesia Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million) 188
Figure 52: Japan Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million) 189
Figure 53: Malaysia Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million) 189
Figure 54: Pakistan Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million) 190
Figure 55: Philippines Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million) 190
Figure 56: Singapore Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million) 191
Figure 57: South Korea Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million) 191
Figure 58: Taiwan Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million) 192
Figure 59: Thailand Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million) 192
Figure 60: Rest of Asia Pacific Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million) 193
Figure 61: Eastern Europe Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million) 194
Figure 62: Eastern Europe Big Data Revenue in the Financial Services Industry, by Country: 2018 – 2030 ($ Million) 194
Figure 63: Czech Republic Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million) 195
Figure 64: Poland Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million) 195
Figure 65: Russia Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million) 196
Figure 66: Rest of Eastern Europe Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million) 196
Figure 67: Latin & Central America Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million) 197
Figure 68: Latin & Central America Big Data Revenue in the Financial Services Industry, by Country: 2018 – 2030 ($ Million) 197
Figure 69: Argentina Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million) 198
Figure 70: Brazil Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million) 198
Figure 71: Mexico Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million) 199
Figure 72: Rest of Latin & Central America Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million) 199
Figure 73: Middle East & Africa Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million) 200
Figure 74: Middle East & Africa Big Data Revenue in the Financial Services Industry, by Country: 2018 – 2030 ($ Million) 200
Figure 75: Israel Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million) 201
Figure 76: Qatar Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million) 201
Figure 77: Saudi Arabia Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million) 202
Figure 78: South Africa Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million) 202
Figure 79: UAE Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million) 203
Figure 80: Rest of the Middle East & Africa Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million) 203
Figure 81: North America Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million) 204
Figure 82: North America Big Data Revenue in the Financial Services Industry, by Country: 2018 – 2030 ($ Million) 204
Figure 83: Canada Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million) 205
Figure 84: USA Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million) 205
Figure 85: Western Europe Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million) 206
Figure 86: Western Europe Big Data Revenue in the Financial Services Industry, by Country: 2018 – 2030 ($ Million) 206
Figure 87: Denmark Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million) 207
Figure 88: Finland Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million) 207
Figure 89: France Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million) 208
Figure 90: Germany Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million) 208
Figure 91: Italy Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million) 209
Figure 92: Netherlands Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million) 209
Figure 93: Norway Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million) 210
Figure 94: Spain Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million) 210
Figure 95: Sweden Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million) 211
Figure 96: UK Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million) 211
Figure 97: Rest of Western Europe Big Data Revenue in the Financial Services Industry: 2018 – 2030 ($ Million) 212

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