Big Data in Insurance Industry

Big Data in Insurance Industry

Insurance companies routinely analyze huge volumes of data related to workplace claim and injury data, workers’ compensation, aggregated exposures with respect to catastrophic events, mortality and morbidity tables used in life and health insurance, loss, construction, fire protection and historical weather. Risk planning and evaluation as a category is fairly wide and covers the actuarial, product management, and underwriting aspects of business. This includes areas such as catastrophe modeling and loss control since they are also about assessing and managing risk.

With the growth and advances in technology and communication in conjunction with the explosive growth of data, customer is at the center of every organization’s focus. Insurance companies have specifically been made to create simpler and more transparent products in line with changing customer preferences. Companies are now looking at predicting customer behavior and obtaining insight into value with a view to developing and optimizing claims that will in turn result in improved customer retention and profitability.

This research evaluates the market for Big Data in the Insurance industry including high-impact areas and those with high ROI potential. All purchases of Mind Commerce reports includes time with an expert analyst who will help you link key findings in the report to the business issues you're addressing. This needs to be used within three months of purchasing the report.

Target Audience:

Insurance companies
Big Data Analytics companies
Risk assessment and consulting firms
Enterprise companies across all industry verticals
Any organization seeking to reduce insurance costs

General Methodology

Mind Commerce Publishing's research methodology encompasses input from a wide variety of sources.

We rely heavily upon our Subject Matter Experts (SME) in terms of their market knowledge, unique perspective, and vision. We utilize SME industry contacts as well as previous customers and participants in our market surveys and interactive interviews.

In addition, we rely upon our extensive internal database, which contains modeling, qualitative analysis, and quantitative data. We review secondary sources and compare to our primary sources to update previous findings (for prior version reports) and/or compile baseline information for technology and market modeling.

We share preliminary models with industry contacts (select previous clients, experts, and thought leaders) to verify the veracity of initial modeling. Prior to final report production (analysis, findings, and conclusions), we engage in an internal review with internal SMEs as well as cross-expertise, senior staff members to challenge results.

We believe that forecasts should be prepared as part of an integrated process which involves both quantitative as well as qualitative factors. We follow the following 3-step process for forecasting.

Forecasting Methodology

Step 1 - Forecasts Input: The inputs for the present and historical revenues are derived from industry players. Financial and other quantitative data for individual sub-market categories are derived from original research and tested with interviews with major industry constituents.

Step 2 - Forecasting of Future Years: Mind Commerce extends forecasts based on a variety of factors including demand drivers as well as supply side data. Key success factors and assumptions are considered.

Step 3 - Validation of Data: The final step is to validate projections, which is accomplished in consultation with both internal and external industry experts, including both topic and regional experts. Adjustments are made to the forecasts based on factors identified throughout this process.

1.0 Executive Summary
2.0 Introduction
2.1 What Is Big Data?
2.2 The Relevance And Importance Of Big Data
2.3 Analytics And Big Data
2.4 Big Data And Business Intelligence
3.0 Big Data And Analytics In Insurance
3.1 Big Data And Analytic Opportunities
3.1.1 Customer Related
3.1.2 Risk Related
3.1.3 Finance Related
3.2 Big Data Benefits Areas In Insurance Enterprises
3.2.1 Claims Fraud Detection And Mitigation 2
3.2.2 Customer Retention, Profiling And Insights
3.2.3 Customer Needs Analysis
3.2.4 Risk Evaluation, Management, And Planning
3.2.5 Product Personalization
3.2.6 Claims Management
3.2.7 Cross Selling And Up-selling
3.2.8 Catastrophe Planning
3.2.9 Customer Sentiment Analysis
4.0 Areas Of High Roi Potential
4.1 Group Health Insurance And Disability Insurance
4.2 Auto Insurers
4.3 Advertising And Campaign Management
4.4 Agents Analysis
4.5 Call Detail Records
4.6 Personalized Pricing
4.7 Underwriting And Loss Modeling
5.0 Big Data Impact Areas
5.1 Risk Evaluation And Management
5.2 Insurance Industry Structure
5.3 Customer Insights
5.4 Claims Management
5.5 Regulatory Compliance
6.0 Big Data Trends In Insurance
6.1 Organizational And Tech Aspects
6.2 Diversity In Business And Data Priorities
6.3 Risk Assessment With Granular Data
6.4 Use Of External Device Data And Telematics
6.5 New Big Data And Analytics Paradigms
7.0 Conclusions And Recommendations
Figure 1: Global Data 2009 -2020 (ZB)
Figure 2: Cost of Data Management per GB 2005 – 2015 (USD)
Figure 3: Global Spending on Big Data 2014 – 2019 (USD $B)
Figure 4: BI, Big Data, and Analytics
Figure 5: Risk, Customers, and Finance

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