data analysis for insurance: providing greater depth and insight through technology

Data Analysis for InsuranceProviding greater depth and insight through technology保险行业的数据分析通过高科技洞察更深层次的行业信息

Insurance Industry Outlook:China marketplace continues to experience phenomenal growth, but not without pressure for bottom-line results

What are China life and property, casualty, and annuity companies up against in the challenging year and decade ahead? What hurdles must they clear to win the race against their competi-tion? What operational enhancements, distribu-tion solutions and tech upgrades should carriers consider to position their organizations so they have a better chance of succeeding over the long haul?

With the surge in the customer base, China insurers face fundamental, potentially game-changing developments threatening their long-term ability to achieve top- and bottom-line growth. The Chinese insurance market enjoyed excellent growth in recent years, but now insurers and regulators are interested in protecting their business into the future by enhancing business infrastructure and improving risk manage-ment. The good news is that insurers also have a number of strategic options and support tools available to implement the correct structure, and not just to survive, but to prosper profitably. Leading-edge carriers are responding proactively to these critical challenges in a variety of ways, employing new policies, practices and products to bolster their operational efficiency and effective-ness. Among the key areas of focus for insurers:

� Data Analysis for Insurance Providing greater depth and insight through technology

• Positioning for growth.An increase in customer counts can sometimes mask real issues preventing profitable growth: product pricing issues, lower investment returns, higher expenses, etc. This could prevent a company from achieving its full potential, making profitability elusive, particularly for carriers that merely try to maintain the status quo rather than experiment and innovate.

• Business optimization.Customers and business partners are looking for simpler ways to do business, and management at insurers are looking for ways to better understand the composition and nature of costs in order to identify, quantify and prioritize savings opportu-nities. Insurers are interested in improving their operations based on what they learn from the numbers they collect.

• Managing for risk.Beyond data management, the use of advanced analytics, predictive models, underwriting and claims fraud software, and other technology tools could make the difference between success and failure for many insurers.

在具有挑战性的�011年和未来十年中，中国的人

寿保险，财产保险，意外伤害保险和年金公司将

面临怎样的挑战？他们将如何突破重围去赢得和

同行企业的竞赛？从长远来看，他们应该怎样通

过改善运营方式、营销方案和科技技术去帮助企

业在更有效的道路上走向成功？

由于庞大和多层次的客户群体，中国的保险企业

正面临阻碍长期发展的潜在困难。尽管中国保险

市场近年来发展迅猛，但是保险公司及行业监管

机构仍致力于提高保险行业的基础商业建设及

风险管理的能力，以保护未来的中国保险产业。令

人可喜的是，中国保险企业具备一系列的战略措

施和工具去帮助企业建立一个正确的商业构造，

这使得保险企业不仅可以在激烈的竞争环境中

生存，并且不断的壮大。尖端保险企业也正在积

极的去迎接各种挑战，并采用新的保险法规和政

策、业务产品去提高自身企业的效率和效用。

保险行业的展望：中国市场继续呈现显著的增长，同时也面临达到增长底线的压力

以下为保险企业需关注的重要领域：

• 定位增长

客户量的高速增长可能会遮盖影响盈利增长的

真正原因，例如：产品定价问题、低投资回报率、

高费用支出等。这些问题都将影响企业的增长潜

力，令盈利遇阻，尤其对于没有创新的，刚刚能

维持现状的保险企业。

• 优化商业模式

客户和商业合伙人都在寻找一个简单的商业模

式，保险企业的管理层也在寻找一种途径去辨

别企业的成本组成及其性质，从而得以量化和

优先利用资金。保险企业会利用一些收集的数

据去提高他们的运营方式。

• 管理风险

除数据管理外，高端的分析方法、预测模型、承

保程序、检测索赔欺诈的软件和科技工具都可用

以管理保险企业的主要风险，从而让企业在面对

风险时游刃有余。

保险行业的数据分析通过高科技洞察更深层次的行业信息 �

Responding Proactively to Critical ChangesEmploying New Policies, Practices and Products

There is a middle class in China that has been historically underserved in their insurance needs, from basic property and casualty to life, health, and retirement. This underserved market has fed top-line growth in recent years, with China insurers pursuing aggressive growth strategies, but these strategies have not been focused on the bottom line; thus, profits have suffered. Moreover, multinational insurers have real competitive advantages over their Chinese coun-terparts, particularly in business management, underwriting, product development, claims, tech-nology, distribution and channel management, risk management, financial reporting, and customer service.

That said, insurers in China need to take action now by positioning for growth, optimizing business processes and managing risk aggressively. Some of the key levers in implementing this long-term view to business management include:

Positioning for GrowthIn China, gains for life insurers have come from sales of the simplest products, as consumers seek to cover their basic risks with the cheapest coverage available. Multinationals are adapting by offering more

hybrid products, such as including a long-term care option on life insurance policies. As consumers in China become more aware of options, interest in life insurance, and perhaps annuities as well, could be increased if carriers effectively call attention to the ongoing challenge of protecting one's family and adequately financing one's retirement. In order to better position for growth, carriers can focus on:

• Improving the "customer experience" and meeting new customer expectations;

• Developing new products to serve new customers and markets;

• Expanding distribution channels; and

• Differentiating the value propositions for agents/producers.

Business OptimizationThe recent focus of companies in China has been on strategic growth, often resulting in a reduced focus on processes and operational effectiveness;

Successful companies understand that financial strength is built from a cohesive and well managed operational platform. As organiza-tions face slowed growth and higher operating costs, these inefficiencies become major risks.

Driving out process inefficiencies improves costs and the company's position for future growth, and can work in the following areas:

• Improving the efficiency of claims processing

• Enabling "ease of use" for customers, agents/producers and underwriters

• Implementing more strategic sourcing, consolidation of suppliers, and improved purchase execution

• Implementing responsible, insightful financial reporting and performance measurement

Managing RiskPreventive controls and measures can be configured to address the dual business requirements of managing risk and maximizing profits. In order to efficiently address risks and better position for growth, carriers can focus on:

• Enhancing risk selection and pricing;

• Identifying high risk claims for potential fraud

• Improving the efficiency of claims processing


无论是基本的财产险、人身意外险，还是健康险

和退休保障金，中国中产阶级的保险需求在过去

都很难得到满足。在这个尚未得到满足的市场

中，中国保险企业采用激进的增长策略，推动中

国保险市场于近几年呈现高速增长的趋势；但这

些策略并未考虑到底线目标，并使得保险公司盈

利遇阻。因此，跨国合资保险公司的优势比中国

本土的保险企业更加明显，特别是在商业管理、

赔付、科技、分销和渠道管理、风险管理、财务报

表和客户服务等方面。

所以，中国的保险企业需要立即在增长定位、优

化商业程序和风险管理上做出改善。几个重要的

影响长期发展的因素如下：

定位增长在中国，保险企业的收益主要来源于单一产品的

销售，因为客户希望以最低的价格去抵御一些基

本风险。跨国合资的保险公司可通过提供一些混

合型产品来满足客户需求，例如：在人寿险上附

加长期护理险。随着中国客户的产品选择面越来

越广，如果中国的保险企业能提供保护一家人的

健康、财产并且能够保证其退休后的资金来源的

保险产品，相信会增加中国客户对人寿保险以及

年金的兴趣。为了达到这些增长目标，中国的保

险企业需要着重一下几点：

• 提高客户体验能力，达到新客户对产品的期望

• 研发新的产品满足市场和新客户的需求

• 扩大销售渠道

• 分散保险产品客户经理和保险产品提供商的

价值主张

对重要变化的积极响应施行新政策，新业务以及新产品

优化商业模式最近中国保险企业都着重于战略性增长却忽略了

商业模式的优化和运营效率的提高。

成功的企业都知道强大的财力是构建在良好管

理和有凝聚力的平台上。当企业面对慢速增长和

昂贵的运营成本时，低效率的运营就会变成很大

的风险

通过改变以下几个方面，就能减少低效率的运营

从而减少成本，把企业拉上成功的道路：

• 提高赔付程序的效率

• 简化客户，客户经理，经销商和承保的过程

• 改善渠道，集中供应商

• 提供真实的，深入的财务报告和运营现状

风险管理预防性的控制和评估可以帮助企业达到风险管

理和利润最大化的两个目标。为了更有效的管理

风险和定位增长，中国保险企业可以着重于以下

方面：

• 提高风险选择和定价风险的控制

• 识别高风险保单和可能的保险欺诈

• 提高赔付过程的效率


Deloitte Analytics: Benefits to InsurersOur Success Comes From the Combination of Deep Industry Knowledge and Technical Sophistication

How to Deploy New Policies, Practices and Products: Focused Insight

We believe the right business analytics create a competitive advantage that drives material benefits to the bottom line.

Three powerful trends are driving the adoption of business analytics:

• An unforgiving demand for consistent performance

• Dual wake-up calls around the need to proac-tively manage risk in an increasingly more stringent regulatory environment

• Exponentially increasing amounts of data to process, comprehend, and react to

The Use Of Analytics to Drive Competitive Advantage in Insurance is Expanding Across the Globe

Insurers are realizing that, to compete, they need the ability to:

• Use information to drive innovation and compet-itive advantage

• Access information tools that can help measure and improve enterprise-wide performance

• Identify insights from data that can drive more value from business and IT investments

• Leverage information to manage risk, improve compliance, and drive business outcomes

How We Help: Deep Industry Business Knowledge

Successful business analytics requires three powerful engines: deep sector knowledge, broad functional capabilities and a high degree of technical sophis-tication. Deloitte brings a big-picture approach, combining each of these strengths to provide unmatched services.

Advanced Analytics — Insurance Sector

Business Imperative Business Strategy Deloitte Solution

Advanced Analytics, including predictive analytics, provide

solutions to enable business strategies for success across the enterprise

• Reducedunderwritingcostsandincreasedefficiency

• Improvedunderwritingmanagement• Improvedpricingprecision• Enhancedriskselectioncapabilities

Underwriting Excellence

• Underwriting Modeltofocusonhigh-risk,complexapplicants

• Price Optimization Analyticstoidentifydemandelasticityofproductswithinsegments,andmodelcompetitiveandmarketplaceresponsetoactions

• Reducedtransactioncosts• Straightthroughprocessing• Improvedeaseofdoingbusiness

Operational Efficiency

• Algorithmic Solutionsstreamlinetheunder-writingprocessbyreducingthenumberofrequire-mentsneededandallowingunderwriterstofocusonhigh-risk,complexapplicants

• Targettherightrisksfornon-renewals• Improvedresultsbyfocusingretentionefforts

onbetterrisks• Deeperunderstandingoflifetimevalueof

customers• Increasedcross-sellopportunities• Recruitingofprofitableproducers

Marketing and Retention

• Retention Modeling to focus efforts on retaining/notretainingcustomerswhoaremostlikelytolapse

• Cross and Up Sell Modeling-candeterminelikelihoodanofferwillbeaccepted

• Customer Segmentation-Datadriven,granularinsightsforsmarterbusinessdecisions

• Identifyclaimsatriskforhigherseverity• Optimizeclaimsresourcedeploymentto

decreaseclaimsseverity• Improvedfrauddetection,whilespeedingup

paymentoflegitimateclaims

Effective Claims Management

• Claims Analytics/Improved Claims Managementtodeterminewhichclaimsqualifyforimmediateorfast-trackapproval,flagsuspiciousclaims,andfacilitateclaimhandlinginotherways.

• Increasethevalueofdatawithintheorganization

• Improvedatagovernance

Improved Decision Making

• Enterprise Data Management (EDM) whichassistsclientorganizationswithadatagovernancemodel,withafocusonimprovingdataqualitymanagement,dataretentionstandardsanddatasecurityandprivacywithinanorganization

• Developnewinnovativepriceconsciousproduct for select populations

• Analyzemortalityassumptionatpoint-of-sale

Informed Product

Development

• Customer Segmentation-Datadriven,granularinsightsforsmarterbusinessdecisions

• Underwriting Model to adjust pricing and options at point-of-sale


德勤分析：精于协助保险企业我们的成功经历来自于深刻的行业洞

察和技术优势

怎样应用新政策，新方法和新产品：深度观察我们相信准确的商业分析是增强竞争力并提高回

报的源泉。

三项使得商业分析被广泛地接受的趋势：

• 对于长期稳定增长的需求

• 对于在一个管理法规日益趋紧的环境中积极

主动管理风险的需求

• 对于处理、分析、理解呈几何级别增长的数据

的需求

在全球范围内，越来越多的保险企业正在利用数据分析增强其竞争力保险企业正在意识到，他们在竞争中需要以下几

点：

• 运用信息以驱动创新和增强竞争力

• 使用信息处理工具以衡量并提高企业效能

• 识别数据中蕴涵的深度信息以提升企业在经营

及IT方面投资的回报

• 利用信息以管理风险，促进融合并提升商业回

报

我们能做些什么：深入的行业知识成功的商业分析缘于三个强大的引擎：深入的知

识，广泛的实践和精湛的技术。德勤拥有宏观的视

角，辅以以上三点作为独有的优势，足以胜任任何

挑战。

高级分析方法——保险行业

企业的当务之急商业策略德勤的解决方法

高级分析

法，包括

预测分析

法能让企

业找到超

越同行企

业走向成

功的商业

战略

• 降低核保成本并提高效率• 提高承保的管理• 提高产品定价的准确性• 提升风险筛选的能力

优质的承保过程• 核保模型：着重于高风险，复杂的申请者• 优化定价分析方法：识别产品需求弹性和模拟竞

争激烈的市场

• 降低交易成本• 直达目标• 提高并简化商业运作模式

商务运作效率• 算法的解决方案：通过减少要求数量精简承保过

程，让承保人着重在高风险，复杂的申请者

• 针对非续保人员的风险• 努力提高续保概率并优化成效• 更进一步的了解客户寿命的价值• 增加交叉销售的机会• 招聘能产生利润的供应商

市场营销和续保

• 挽留模型：着重经历去挽留/不挽留流失客户• 交叉销售模型：能决定一个提议是否被接受的可

能性• 客户分段：通过数据，细化行业信息让企业能做更

好的决定

• 识别高风险的赔付• 合理的部署理赔资源，从而降低重大赔付的

机率• 提高对欺诈的监测，并提速有效理赔的付款

提高理赔的管理• 理赔分析法/提高理赔管理：判断有效的索赔并

立即进行赔偿，质疑有嫌疑的索赔并用不同的理赔方式

• 提高对数据的重视层度• 提高去数据的监管

提高决策力• 企业数据管理：可以在数据监管帮助企业，其侧重

点在提高企业的数据质量的管理，数据保留标准，数据安全以及私密性

• 帮助特定群体设计创新性价格的产品• 在销售时，分析生死率的假设

有把握的产品开发• 客户分段：通过数据，细化行业信息让企业能做更

好的决定• 承包模型：在销售时，调整定价和选择项


structure were in place and working on an ongoing basis.

Our procedures included the analysis of payments of more than US$1.�billion. Example findings:

• Approximately US$�00million worth of payments with no approver;

• Approximately US$100million worth of payments with no posting user or approver;

• Approximately US$�million worth of payments with the same user posting and approving.

Contract Risk and Claims Fraud Case Study:Large US-based Health InsurerOur client had been experiencing significant inefficiencies in its claims handling process. Deloitte began working with this client and identified certain areas contributing to the inef-ficiencies, including activities occurring at third party contracted facilities. Scope of the project included:

• Assessment of risk in relationships with other healthcare organizations (contract risk and contract compliance); and

• Creation of a more efficient claims handling process which addressed risk more effectively, contributing savings to the bottom-line.

Our activities included:

• The performance of a risk assessment of rela-tionships with other healthcare organizations, including an evaluation of existing terms within existing capitation agreements with contracted facilities, and compliance with those terms;

• The creation of an automated screening process for paid pharmaceutical claims to identify duplicate payments, invalid National Drug Code (NDC) numbering, inappropriate or unexpected National Association of Boards of Pharmacy (NABP) assignment, and an evalu-ation of the validity of data fields capturing days supplied, generic vs. brand, electronic vs. paper claim submissions, etc.;

• The review of claims processing of policy holders for data integrity from claim to system storage, identifying patterns of fraudulent claims or claims filed in error, and areas of improvement in claim reconciliation and business process controls; and

How We Have HelpedDeloitte Analytics has Assisted Clients in the Following Ways

Customer Insights and Analytics Case Study: Large InsurerTo differentiate themselves, our client needed to make a significant investment in order to improve their analytics capabilities. Deloitte had been working with the client's management team on a multi-year program designed to enhance identification, creation, and delivery of analytics capabilities.

The goals of the program was to:

• Increase effectiveness of sales activity;

• Improve efficiency of insurance operations;

• Support executive decision making;

• Improve knowledge and adoption of analytic capabilities;

• Increase retention as a result of improved customer satisfaction

The value Deloitte delivered included:

• Developed blueprint to transform existing analytics functions into a Center of Excellence that served entire enterprise;

• Identified duplicative analytical activity in the enterprise to improve quality, efficiency and consistency in analysis used to support business decision making;

• Improved policy retention by �00 basis points and increased acquisition rates on abandoned quotes by �00 basis points

• Improved adoption of actions identified by advanced analytic techniques, which improved sales and customer service functions.

Claims Fraud Analytics Case Study:Property and Casualty InsurerOur client had preventative controls over its claims processing function, but was continuing to suffer losses from fraud. Deloitte suggested a unique approach of using analytics combined with deep industry knowledge and experience to:

• Identify fraud patterns;

• Assist with root cause analysis of the fraud types;

• Install upgraded procedures to assist with fraud prevention and detection.

Continuous monitoring was deployed to assess that improvements to the internal control


合同风险和骗保的案例分析：大型美籍健康保险企业

我们的客户在处理索赔时效率低下。德勤开始和

这家企业合作后发现了效率低下的核心原因，包

括在履行和第三方的合约时。

这个案例的情况包括：

• 衡量在和其他健康保险企业合作时的风险（

合同风险和合同执行）

• 规划了一个更有效率的执行索赔的程序，降低

成本。

我们的活动包括：

• 检测已有的和其他健康保险企业合约的合同

风险

• 制作一个自动扫描程序以检测医药赔付中的

重复赔付等问题

• 重新检视索赔者的资料，检测骗保行为的规律

并提高企业程序性管控能力

• 改善管控程序和方式，使得我们的客户能够将

数据以风险大小分类进行选择，而非以往的随

机性选择

职员报酬案例：政府赞助的职员报酬项目

我们的客户帮助�0,000名雇主和�00,000名雇员

的管理政府的职员康复和报酬计划。

一个可行的，财力雄厚的职员报酬计划是每个人

的希望，我们客户的保费承诺规则是积极的追回

没有支付或少支付的保费-----让每个职员都享有

同样的，公平的保费系统。

德勤通过收集，分析和理解不符合保费规定的

员工的数据，利用这些数据建议参加这个计划的

雇主需要支付增加额外近百万的保费给我们的

客户。

德勤的解决方案包括开发和执行识别不符合保

费规定职员的模型，让我们的客户去追回此部分

欠款，以达到正确的保费资金。这个模型通过行

业分类识没有支付足额保费的雇主。

客户案例分析：大型保险企业

作为这样的企业，他们需要在分析能力上投入大

量资源来在竞争中突围而出。德勤和这些企业的

管理层紧密合作，制定了一个为期数年的计划以

提高其分析能力。

这个计划的目标在于：

• 提高销售环节的效率

• 提高保险运营环节的效率

• 协助公司决策的制定

• 改善数据分析能力

• 改善顾客满意度，提升续保率

德勤服务的价值：

• 为建立一个为全公司服务的“分析中心”绘制

路线图

• 识别公司内重复分析操作以提升分析的质量

并提升公司决策的有效性和连贯性

• 提升续保率达�00个基点

• 以先进的分析技术改善销售和客户服务质量

骗保行为数据分析的案例：财产保险公司和人身意外保险公司

我们的客户虽然拥有对于索赔的管控，但长期以

来仍然深受骗保行为之害。德勤运用其深刻的行

业知识和数据分析能力为企业提供了独特的解

决方法：

• 识别骗保的行为方式

• 协助分析每项骗保类型

• 实施完善后的流程以预防并识别骗保行为

这些内部控管措施需要长期连续性地实施。我们

的流程包括了分析一项超过1�亿美元的赔付。我

们发现：

• 约有�亿美元的赔付没有授权人

• 约有1亿美元的赔付没有对象或授权者

• 约有�亿美元的赔付有重复的对象或授权人

我们的业绩德勤分析已经在以下方面协助客户达成目标


• The improvements to processes and controls which enabled our client to conduct a more thorough review by relying on risk-based selection and claims handling as opposed to random sampling and balancing.

Worker's Compensation Case Study:Government Sponsored Worker's Compensation ProgramOur client manages a government workers' reha-bilitation and compensation scheme on behalf of approximately �0,000 employers and �00,000 employees.

A viable and financially sound workers' compen-sation scheme is in everyone's interest, and our client's premium compliance policy includes actively pursuing unpaid and underpaid premiums – with the aim of ensuring a fair and equitable premium system for all employers.

Deloitte was approached to gather, analyze and interpret data from a range of sources to identify non-compliant employers. We used these insights to recommend which employers to target, resulting in additional payments of millions of dollars for our client.

Deloitte's solution included the development and implementation of analytic models to identify non-compliant employers, which allowed our client to chase these in order to receive the correct premium. The models also recognized if employers were paying the incorrect premium based on their industry classification.

Our Deloitte Analytics team helped our client recover millions of dollars through this approach, and the models created could be used by the client for future auditing and checks into non-compliant employers. While we were working with the premium non-compliance audit team in this instance, the same approach can be applied to any targeted auditing or business problem where resources are limited, and efforts need to be focused on certain areas. Likewise, this approach can also be used to gain insights using historical data, or predict future trends.

Predictive Underwriting Model Case Study:General Liability and Commercial Property Insurer

For insurance companies, few innovations are more important than predictive modeling, espe-cially when it comes to underwriting and pricing. So when a major U.S. insurance carrier wanted to improve its underwriting and pricing discipline, it looked for a professional services provider that could not only develop algorithmic and predictive modeling capabilities but also deliver, integrate and deploy an end-to-end business solution across a range of product lines.

Deloitte was asked to develop predictive under-writing models and scoring engines for the client's Business Owner's Policy, Commercial Automobile and Commercial Package (General

Liability and Commercial Property) lines. The scoring engine that was developed was combi-nations of IT infrastructure and software that generates the predicted profitability score and lets the company monitor the effectiveness of business strategies derived from the models.

Once the predictive modeling solution was fully integrated into the insurer's technical and business infrastructure, Deloitte assisted in the business implementation of the models and the development and delivery of training content for regional underwriting offices.

Today, the client can effectively assess policies for risk quality, price adequacy, customer retention, agency management and underwriting decision compliance. It can also flag policies for follow-up attention in areas such as claims handling, agent training and customer service. The insurance carrier is able to measure the benefits of its predictive models and implemented methods, learning from past data and responding proac-tively to future needs.

10 Data Analysis for Insurance Providing greater depth and insight through technology

我们的德勤分析团队通过自己开发的模型帮助我

们的客户追回了近百万美金的欠款，而且该模型

还可为未来客户审计支持以及识别未足额交款的

雇主。我们的模型不仅仅能识别未足额交款的雇

主，也可以利用这同一思想在任何有目标的审计

工作和商业问题上。同样的，这样的模型思路也

可以利用过去的数据分析行业信息去预测未来

趋势。

预期承保模型案例：总负债和商产保险公司

对于保险公司来说，没有哪项创新比预期模型更

重要，特别是在承保和定价环节中。所以当一家

美国大型保险公司试图提高其承保和定价效能

时，它需要一家专业服务机构不仅能够开发数据

分析模型，而且能够综合运用，提供覆盖各条产

品线的一条龙商业服务。

德勤应邀开发了预期承保模型并为其企业家保

险，商业车保和商业保险（总负债和商产保险）制

定评分系统。此评分系统综合了IT硬件和软件，能

够提供盈利评分，使得公司能够实时监控商业决

策的有效性。

在此预期承保模型完成以后，德勤又协助制作了

此公司地区分部的教学材料，用于指导怎样运用

此项模型。

今天，这家保险公司客户能够有效地监控风险，

定价，续保率，销售经理的业绩及各项承保事宜。

系统还能够在接单，新员工培训，和顾客服务方

面出现问题时及时发现并提醒公司。现在，这家

保险公司得益于预期模型和执行方式的改变，使

得其能够从过往的各项数据中掌握重要信息并快

速主动地应对未来的挑战。

保险行业的数据分析通过高科技洞察更深层次的行业信息 11

ConclusionTurning Data Into Business Insight

With experienced industry specialists, Deloitte Analytics can help you identify which questions matter most and where to find the answers. Whether you need to look backward to evaluate past perform-ance or look forward to engage in scenario planning or predictive modeling, you need

proven strategies for turning your data into valuable insight. Deloitte Analytics can help. By delivering uncommon insights, we help you see beyond the data and provide you with what you need to know to improve your operation's performance and hone your competitive edge.

Scott Raso

China National Data Analytics LeaderDeloitte Touche Tohmatsu Direct: + �� 10 ��0 �[email protected]

Adrian Lee

Partner, Enterprise Risk ServicesDeloitte Touche Tohmatsu Direct: + �� 10 ��0 ��[email protected]

Contacts

To learn more, please contact:

1� Data Analysis for Insurance Providing greater depth and insight through technology

a

文本框

德勤的资深保险行业分析员能帮助保险企业识别风险的所在和控制风险的方案。不管是企业想了解

过去的企业的运营情况还是展望未来企业的蓝图和未来的预测模型，企业需要可靠的策略把行业数

据转化为行业洞察力，德勤的分析可以帮助这一切。我们能让企业看到数据背后蕴藏的行业信息并且

提供提高运营能力的建议，让企业能在激烈的竞争中脱颖而出。

结语把数据转化为行业的洞察力

斯高达

中国国家数据分析领导人

德勤华永会计师事务所有限公司北京分所直线：+ �� 10 ��0 �[email protected]

李嘉渊

企业风险管理服务合伙人

德勤华永会计师事务所有限公司北京分所直线：+ �� 10 ��0 ��[email protected]

联络人

了解更多信息，请联系：

保险行业的数据分析通过高科技洞察更深层次的行业信息 1�

a

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保险行业的数据分析通过高科技洞察更深层次的行业信息 1�

Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private company limited by guarantee, and its network of member firms, each of which is a legally separate and independent entity. Please see www.deloitte.com/cn/en/about for a detailed description of the legal structure of Deloitte Touche Tohmatsu Limited and its member firms.

Deloitte provides audit, tax, consulting, and financial advisory services to public and private clients spanning multiple industries. With a globally connected network of member firms in more than 1�0 countries, Deloitte brings world-class capabilities and deep local expertise to help clients succeed wherever they operate. Deloitte's approximately 1�0,000 professionals are committed to becoming the standard of excellence.

In China, services are provided by Deloitte Touche Tohmatsu and Deloitte Touche Tohmatsu CPA Limited and their subsidiaries and affiliates. Deloitte Touche Tohmatsu and Deloitte Touche Tohmatsu CPA Limited are, together, a member firm of Deloitte Touche Tohmatsu Limited.

Deloitte China is one of the leading professional services providers in the Chinese Mainland, Hong Kong SAR and Macau SAR. We have over �,000 people in 1� offices in Beijing, Chongqing, Dalian, Guangzhou, Hangzhou, Hong Kong, Jinan, Macau, Nanjing, Shanghai, Shenzhen, Suzhou, Tianjin, Wuhan and Xiamen.

As early as 1�1�, we opened an office in Shanghai. Backed by our global network, we deliver a full range of audit, tax, consulting and financial advisory services to national, multinational and growth enterprise clients in China.

We have considerable experience in China and have been a significant contributor to the development of China's accounting standards, taxation system and local professional accountants. We also provide services to around one-third of all companies listed on the Stock Exchange of Hong Kong.

This publication contains general information only, and none of Deloitte Touche Tohmatsu Limited, its member firms, or their related entities (collectively the “Deloitte Network”) is by means of this publication, rendering professional advice or services. Before making any decision or taking any action that may affect your finances or your business, you should consult a qualified professional adviser. No entity in the Deloitte Network shall be responsible for any loss whatsoever sustained by any person who relies on this publication.

Deloitte （“德勤”）泛指德勤有限公司（一家根据英国法律组成的私人担保有限公司，以下称 “德勤有限公司”) ，以及其一家或多家成员所。每一个成员所均为具有独立法律地位的法律实体。请参阅 www.deloitte.com/cn/about 中有关德勤有限公司及其成员所法律结构的详细描述。

德勤为各行各业的上市及非上市客户提供审计、税务、企业管理咨询及财务咨询服务。德勤成员所网络遍及全球逾1�0个国家，凭借其世界一流的专业服务能力及对本地市场渊博的知识，协助客户在全球各地取得商业成功。德勤约1�0,000 名专业人士致力于追求卓越，树立典范。

在中国，我们通过德勤•关黄陈方会计师行和德勤华永会计师事务所有限公司，以及其下属机构和关联机构提供服务。德勤•关黄陈方会计师行及德勤华永会计师事务所有限公司共同为德勤有限公司的成员所。

德勤中国是中国大陆及港澳地区居领导地位的专业服务机构之一，共拥有逾�,000名员工分布于包括北京、重庆、大连、广州、杭州、香港、济南、澳门、南京、上海、深圳、苏州、天津、武汉和厦门在内的1�个城市。

早在1�1�年，我们于上海成立了办事处。我们以全球网络为支持，为国内企业、跨国公司以及高成长的企业提供全面的审计、税务、企业管理咨询和财务咨询服务。

我们在中国拥有丰富的经验，并一直为中国会计准则、税制以及本土专业会计师的发展作出重大的贡献。在香港，我们更为大约三分之一在香港联合交易所上市的公司提供服务。

本文件中所含数据乃一般性信息，故此，并不构成任何德勤有限公司、其成员所或相关机构(统称为 "德勤网络")提供任何专业建议或服务。在做出任何可能影响自身财务或业务的决策或采取任何相关行动前，请咨询合资格的专业顾问。任何德勤网络内的机构不对任何方因使用本文件而导致的任何损失承担责任。

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