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(MoneyNewsWire.Net, February 17, 2019 ) “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.
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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.
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Table Of Content
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
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