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.
This research 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.
This 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.
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
Key Questions Answered
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?
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.
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: 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: 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: 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: 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 126.96.36.199 Storage & Compute Infrastructure Providers 188.8.131.52 Networking Infrastructure Providers 6.2.2 Software Providers 184.108.40.206 Hadoop & Infrastructure Software Providers 220.127.116.11 SQL & NoSQL Providers 18.104.22.168 Analytic Platform & Application Software Providers 22.214.171.124 Cloud Platform Providers 6.2.3 Professional Services Providers 6.2.4 End-to-End Solution Providers 6.2.5 Financial Services Industry
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: 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 8.10.10Singapore 8.10.11South Korea 8.10.12Taiwan 8.10.13Thailand 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 8.15.10Sweden 8.15.11 UK 8.15.12 Rest of Western Europe
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