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市場調查報告書

保險產業上巨量資料

Big Data in Insurance Industry

出版商 Mind Commerce 商品編碼 344792
出版日期 內容資訊 英文 61 Pages
商品交期: 最快1-2個工作天內
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保險產業上巨量資料 Big Data in Insurance Industry
出版日期: 2015年11月09日 內容資訊: 英文 61 Pages
簡介

本報告提供保險產業上巨量資料相關資料,影響大的領域及高ROI的可能性的領域分析。

第1章 摘要整理

第2章 簡介

第3章 保險的巨量資料、分析

  • 巨量資料、分析的機會
  • 保險企業的巨量資料的利益領域

第4章 有高ROI可能性的領域

  • 團體健康保險、傷殘保險
  • 汽車保險業者
  • 廣告、宣傳活動管理
  • 代理店分析
  • 呼叫細節記錄 (CDR)
  • 定做費用
  • 保險公司、損失建模

第5章 受巨量資料影響的領域

  • 風險評估、管理
  • 保險產業結構
  • 客戶分析
  • 申請管理
  • 法規遵守

第6章 保險的巨量資料趨勢

  • 組織、技術方面
  • 商務上遞送、資料優先順序
  • 使用了準確度精細資料的風險評估
  • 外部設備資料、車載資通系統的利用
  • 新的巨量資料、分析範例

第7章 結論、建議

圖表

目錄

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

Table of Contents

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

Figures

  • 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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