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Potential impact of coronavirus outbreak on 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.

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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

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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.

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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

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