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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