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NIIT Technologies White Paper

Analytics in Insurance Value ChainAnalytics in Insurance Value Chain

Surekha SugandhiInsurance Practice - Solution Architect

CONTENTS

Insurance Industry Overview and Major Trends 3

Business Intelligence and Insurance Value Chain 3

Insurance and Analytics: Current state of the art 4

How can analytics help build insurance value chain? 4

Best Practices for leveraging business analytics in insurance sector 5

Conclusion 6

industry have exponentially increased the importance and

complexity of an effective business intelligence environment.

Growing Consolidation: Consolidation is a major force altering

the structure of the insurance industry, as insurers seek to create

economies of scale and broaden their product portfolios. The

aggregated value of mergers and acquisitions was $75.7 billion in

2010, up from $ 41.7 billion in 1999 and a mere $8.5 billion in 1993.

Convergence of Financial Services: Mergers and acquisitions

involving insurance companies and financial service providers,

such as banks have led to the emergence of integrated financial

services companies.

New Distribution Channels: New distribution channels are fast

catching up with traditional insurance agents. These channels,

though, not a major threat, are rapidly changing the way insurers

and clients interact with each other.

Focus on Customer Relationship Management: The only

viable strategy for insurers, today, is to focus on customer needs

and serve them better. Clients have extremely differentiated needs

with different profitability. Hence, an effective CRM strategy is the

most vital component of an insurer's overall business strategy.

Insurance Industry Overview and Major Trends Insurance industry is totally dependent on the ability to convert raw

data into intelligence - about clients, markets, competitors, and

business environment. Over the years, data processing technology

has progressed phenomenally and tools such as data

warehousing, OLAP and data mining that constitute the

cornerstone of an effective Business Intelligence (BI) environment

are today widely accepted across industries. However, insurance

companies have been relatively slow in adopting these tools,

primarily because of protective regulations. Now they can no

longer afford to be complacent as the Internet, deregulation,

consolidation, and convergence of insurance with other financial

services are fast changing the basics.

The insurance industry is quite diverse in terms of product portfolio

offered by different companies. These can be broadly classified

into two product lines: Property and Casualty (P&C) and Life

Insurance. Life insurance product line can be further sub-divided

into life insurance, health insurance and annuity products.

Growing consolidation and changes in regulatory framework have

forced insurers to add new products to their portfolio. These

changes have presented its own unique challenge of leveraging its

greatest asset - data. A number of other trends in the insurance

Business Intelligence and Insurance Value ChainIn the last three decades, insurance companies have acquired

significant product development capabilities. However, they

failed to truly understand clients’ needs and demands. This led

most firms to rather develop products that they could manage

than, those their clients required. Moreover, during the last few

years, deregulation and growing competition have forced

insurance companies to move from traditional product-centric

operations to customer-centric operations.

3

Analytics is a two-sided coin. While on one side, it uses

descriptive and predictive models to gain valuable knowledge

from data, i.e. data analysis, on the other side, it provides

insight to recommend action or guide decision making, i.e.

communication. Thus, analytics is not much about individual

analyses or analysis steps as it is about the entire methodology.

Today, there is a pronounced tendency to use the term analytics

in business settings e.g. text analytics vs. generic text mining to

emphasize this broader perspective.

4

Insurance and Analytics: Current state of the artIntroduction of business intelligence software resulted in the

evolution of computing in the insurance industry from a tactical

and transaction focus to a strategic and business planning focus.

This does not mean that transaction processing has faded from

the scene or diminished in importance. Rather, it means insurers

still process billions of transactions every day in sales, service,

and claims arena. They perform basic data processing and

appear competitive only when they efficiently handle large

transaction volumes.

However, for insurers, efficiency is only one aspect of the winning

equation. To compete successfully and profitably, insurers must

identify and act on emerging trends, new customer insights, and

improve understanding of natural and man-made hazards. In

addition, insurers need the ability to spot operational issues and

opportunities in real-time to respond proactively. Fortunately, this is

possible with two new classes of software known as business

intelligence and advanced analytics. Currently, insurers use any of

the two software’s with the ability to create dashboards and

scorecards, conduct what-if analyses, leverage scenario planning,

employ advanced statistical analyses, harness data/text mining, as

well as uncover new opportunities from predictive models.

These technologies, combined with human experience and insights,

are already giving leading insurers advantage in the marketplace.

How can analytics help build insurance value chain?Leading organizations use analytics to drive important decisions

and progressively build their analytics capability. Assessing the

maturity of skills, insurance companies design and technology

capability against current and future needs will guide your

priorities and planning process.

Choose your strategy carefully

• Grow client profitability by looking at your own information from

a client perspective. Use digital and social media to identify high

potential clients, their behaviors and preferences.

• Use this information to define client’s experience strategies and

implement initiatives that will delight your priority clients and

attract new high potential clients.

• Continuously monitor and re-evaluate clients’ potential, risk

attributes, situation and environment to test on-going validity of

segmentation. As circumstances change, this information will

help companies in retaining a realistic view of client profitability

and risk.

So, many additional opportunities exist for insurers to further

capitalize on today’s business intelligence and advanced

analytics solutions.

Figure 1: Policy and Claim Life Cycle*Source: Celent, Forrester, Innovation Group

CLAIMSPOLICY

Core Solutions Core Solutions

Sales

Quotations

Segmentation

Lifecycle

Workloads

Cancellation

Renewals

Recoveries

Settled Claims

Fraud

Lifecycle

Supply Chain

Repairs

FNOL

• Mine data in a risky environment to understand how market and

credit events are related and use it for funding plan and for

reducing emergency funding at punitive rates.

Develop highly relevant and attractive products and service offerings

• Use client insight to develop highly relevant and attractive

products and product bundles for specific customer segments

or individual customers. Along with these products,

organizations must develop an effective pricing strategy to

maximize delicate risk reward balance.

• Harness more sophisticated, risk-based pricing to introduce

products that otherwise would have been too risky to develop at

the right price.

Generate quality leads

• Embed intelligence about your clients in your distribution

strategy to generate quality leads. This should be performed for

clients that have a high propensity to buy and determine the

most effective distribution channel that cost effectively captures

their business.

• Improve your risk culture by profiling employees for mismatches

in risk profile required by the role.

• Identify and monitor leading risk and profitability indicators

across distribution network to detect poor selling practices. It will

help to refine your distribution strategy.

Track client behavior

• Build digital records about your clients, their behaviors and

preferences to develop effective loyalty programs and retention

strategies. These digital records will make it difficult for other

insurers to attract your highly valued clients

5

Best Practices for leveraging business analytics in insurance sector Profitable growth is an elusive goal in today’s increasingly

competitive insurance industry. Rapid development and

deployment of new products and its features, balancing broader

distribution channel opportunities, managing risks across

organization, responding to regulatory and reporting agency

demands, and providing precise pricing levels require effective

decisions to be made with greater accuracy, efficiency and

transparency. Personal experience is often insufficient in making

consistent, accurate and effective decisions in line with rapidly

changing marketplace.

Leading organizations are increasingly turning to business

analytics for survival. Business analytics solutions are used by

insurers to reduce the time required to react to competitive

pressure, respond efficiently to market changes, increase

effectiveness of business managers in improving financial results

and driving value for organization, to more effectively managing

risks an enterprise face to improve precision and efficiency of

operational decisions. The primary forms of business analytics

used by the industry leaders include:

Ad Hoc Management Reporting and Dashboards: This

business analytics solution use analysis and reporting tools to provide

automatic feedback on achievement of key performance criteria.

• Analyze customer interactions and channel choices to improve

customer service and deliver new service to sale opportunities.

This data will also reveal opportunities to reduce cost by

eliminating services your clients do not value.

They are also used to create ad hoc reports using data from a

variety of data sources in order to improve management’s ability to

make better and faster decisions. Common examples include

claim reporting and settlement lag time, call center response times,

and achievement of service standards, etc.

Profiling and Segmentation: These business analytics solutions

involve data mining to determine historic behaviour of a group, or

performance of a group of people, risks or transaction types.

Common examples include clients by profitability, claim types by

severity or frequency, and clients by product preference, etc.

Forecasting: This business analytics solution allows an insurer to

attempt and determine a time series estimate of what will happen

in future based on statistical evaluation of current and historic

aggregate data.

6

ConclusionInsurance industry is divided in its adoption of business intelligence

environment based on technologies such as data warehousing,

OLAP and data mining. Quite a few insurance companies are in

advanced stages of their business intelligence initiative; yet there

are many oblivious of its benefits. Some insurers have gone for

non-scalable temporary solutions, which often fail to leverage the

ever-increasing volumes of data.

Predictive Analytics: This business analytics solution attempts to

predict future behavior or performance based on analysis of historic

transactional data, third party data (like loss history, motor vehicle, geo

demographic data, credit data, etc.) or derived data often calculated

from one or more data elements. The analysis often results in a score

or recommended action assigned during the processing of a

transaction. Examples include determining the loss ratio relativity of a

risk being underwritten, pricing adequacy based on anticipated loss

experience, propensity of fraud on a reported claim, etc.

Optimization: This business analytics solution focuses on

optimization of business decisions usually based on multiple

scenarios or multiple predictive analytics models. For insurance,

optimization is always constrained optimization. Example includes

maximizing response to a direct response campaign constrained

by marketing budget.

Data

Insi

ght R

equi

red

Business Value Derived

AD HOCReporting

Dashboards

Profiling and Segmentation

Forecasting

Predictive Analytics & Scorecards

Optimization

Figure 2 : Business Value Derived at each stageSource: www.sas.com

By combining analytics expertise with business knowledge,

insurance companies can uncover the real cause of toughest

problems, and anticipate and identify future opportunities to

differentiate and grow business. However, it is not enough to

capture, integrate and analyse data. Enterprises must also act on

what they find. This requires a culture that is ready to embrace

novel and counter-intuitive ideas. Unless leadership sets tone by

expecting data-driven decisions and encouraging ‘test and learn’

experimentation, analytics will remain a much talked about subject,

rather than a core strategic capability.

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