Insurance Analytics

Actuarial Science • Actuarial analysis uses statistical models
to manage financial uncertainty by
making educated predictions about future
events. Insurance companies, banks,
government agencies and corporations
use actuarial analysis to design optimal
insurance policies, retirement plans and
pension plans.

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Applications Traditional Approach
of Predictive
Modeling in • Determine appropriate experience period
Insurance – • Gather data – exposure and deaths
Assumption • Select basis
Setting (1) • Calculate A / E

• By gender
• By age group etc
• Compare with industry tables / current assumptions
• Credibility of data important

Analytics Approach

• Determine the target variables
• Initial factor analysis
• Establish variable correlations
• Establish the model
• Validate the model
• Final calibration / smoothing
• Implementation

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Applications of Predictive Modeling in Insurance
– Assumption Setting (2)

• Pros and Cons
allows leveraging much more data
better insight into interaction of various factors
isolates the true effect of each factor
newer factors can be introduced
more refined assumptions – greater granularity
lack of expertise
misinterpretation of models
can the business build it in?

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Applications of Predictive Modeling in Insurance
– AI techniques in customer experience

AI Techniques Applications

• Sentiment Analysis of social • Understand / Anticipate
and online / offline customer needs
interaction data
• Understand customer
• Call center audio recording sentiments
• Text mining
• Predict propensity to churn
• Customer feedback / reason

for call / emotions of caller

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Applications of Predictive Modeling in
Insurance – Claims Fraud Detection (1)

Importance of identifying
fraudulent claims

• Increases cost for honest customers
• Additional time and scrutiny
• Scrutiny at the time of need – loss

of reputation and trust
• Increases policy turnover

Traditional • Area of claim agents
Approach • Rely on some facts and intuition
• Based on sampling methods
• Works in silos

Analytics • Data Collection and Integration
Approach • From underwriting, claims, industry

• Modeling
• Dynamic models – as fraud practices change frequently
• Relies on past behaviour and common traits

• Helps detect low incidence events
• Analyze textual data with the help of text analytics

Applications of Predictive Modeling in
Insurance – Claims Fraud Detection (2)

Applications of Predictive Modeling
in Insurance – Underwriting

Traditional Approach

• Heuristics based rather than statistics
based

• Based on
• Experience
• Market knowledge
• Intuition
• Oral history

AI Techniques

• Process mining techniques to automate & improve efficiencies
• Using home and industrial sensor data to operational intelligence on risk drivers that

feed into machine learning techniques

Applications

• Understanding the risk drivers
• Predictive models on risk assessment
• Automating of standardised underwriting in auto, home, commercial and life insurance
• Augmenting of large commercial underwriting & life underwriting by having AI systems

highlight key considerations for human decision makers

Applications of Predictive Modeling in
Insurance – Underwriting

Applications of Predictive Modeling in Insurance
– Underwriting

• Pros and Cons
quantifies risk level compared to similar account
alignment of price with risk quality in a more granular

way
leveraging price elasticity
allows harvesting of data in documents
may not identify finer dimensions of risk

• Current status of research
models are enablers rather than decision takers
models and underwriters must co-exist
few underwriting decisions are truly binary

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Predictive Modelling and AI in Insurance –
Emerging Risks

Accuracy of the Claim Data

Predictive modelling
effectiveness is based on the
data sources and the quality of
the claims data provided.

While predictive modelling may
enhance good claim handling, it
cannot rectify poor claim
handling.

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Predictive Modelling and AI in Insurance –
Emerging Risks

The Underwriters

Insights from experienced
underwriters not incorporated
into decision processes.

Need to balance risk quality,
emerging exposures, market
contexts and competitive
strategies.

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Predictive Modelling and AI in Insurance –
Emerging Risks

Looking Backwards

Backward looking and
therefore inadequate for
liability, which is long tail.

As environment changes,
variables that explain the
past may no longer
applicable to the future

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Predictive Modelling and AI in Insurance –
Emerging Risks

Privacy
Concerns/Regulatory
Hurdles

The insurance industry
will have to battle data
privacy concerns.

Keeping a close watch
on the regulatory
changes will be a big
challenge for insurers.

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Predictive Modelling and AI in Insurance –
Emerging Risks

The Glitch Risk

AI may also require a
significant investment of
human time to re-train or
re-configure before
resuming their work.

How many of us have
experienced a glitch in
excel/other models when
just a restart works.

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Claim Analytics

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Claims • Claims are ‘the moments of truth’. An example in
Analytics the insurance industry is: Many auto accidents
happen on late Friday nights. The drivers of these
cars are of a certain age group. Specific areas of the
city are more prone to such accidents.

• Mostly, these accidents happen on cloudy or rainy
nights when the visibility is low. Certain make and
models of cars have more damage than others in
such accidents. This is understood by looking at the
insurance data by drawing relations between
different variables such as day of the incident, time,
age group, and associating it with other external
information such as location, behavior patterns,
weather information, vehicle types, etc.

Claims • Gartner defines “claims analytics” as the use of
analytics business intelligence (BI), reporting solutions,
dashboards, data mining and predictive
modeling technologies to manage and analyze
claims data, which can result in improved
performance.

• Overall, three processes are supported in claims
analytics tools: claims analysis, reporting and
predictive modeling.

Uses of Allocation of Resources / Triage
analytics in Reserving / Settlement Values
claims Recognition of Potentially Fraudulent Claims
departments Early Identification of Potentially High Value Losses
Expense Management
Trend Analysis

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Key • Reduce claims cycle time
performance • Increase customer satisfaction
• Combat fraud
indicators • Optimize claims recovery
(KPI) in Claims • Reduce claim handling costs

Reduce claims • A cursory look at one of the insurer’s data revealed that
cycle time the claim cycle time is abnormally high in one of the
geographies. This data was compared to identify patterns
across insurance lines. Even though all the claim types
had a significantly higher cycle time in a particular
geography, a closer dissection of the data revealed that
the cycle time is almost twice for a particular type of
claim. This led to a few interesting questions.

• Is this pattern consistent across all claims or is there any
specific characteristic of a claim that increases the cycle
time?

• What are the sub-activities within the claim processing
that consume a longer cycle time for this particular type
of claim?

Optimize • Insurers have this dual role of keeping up with
claims the promises to pay and also ensure that
unwarranted payments are recovered
recovery appropriately. In auto insurance (both personal
and commercial), there are opportunities for
salvage, subrogation, and reinsurance to claim
back the payments partially or fully. The data on
the claims history, vendor data, and claim costs
have important information that helps insurers
to identify which vendors are cost-effective for
what kind of services.

Fraud • Fraud is a large and growing problem for the insurance
analytics industry. Most research estimates that about 10% of
insurance claims are fraudulent and cost the insurance
industry billions of dollars. To combat claims fraud,
insurance companies should implement a real-time or
near-real-time analytical engine that calculates the
propensity for fraud at each stage of the claims life cycle.

• The fraud analytical engine must use a combination of
techniques, including business rules, predictive modeling,
text mining, database searches and exception reporting.
In addition, insurers should consider network link analysis
technology, which analyzes all historical claims to quickly
discover organized fraud rings that might otherwise take
months or years to identify and prevent.

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Recovery • Recovery optimization scores claims at each
optimization stage in the claims lifecycle based on known
subrogation characteristics, identifying
unknown characteristics and optimizing
associated activities. By using text analytics,
insurers can analyze adjuster notes or other
unstructured data to find phrases that typically
indicate a subrogation case. Pinpointing likely
subrogation opportunities earlier, insurers
maximize loss recovery and ultimately reduce
loss expenses.

Settlement • Bringing consistency to the claims settlement
optimization process is an important objective — especially
for claims managers who are pressured to settle
faster, with transparent fairness, while using
fewer resources and reducing loss-adjustment
expenses.

Litigation • A significant portion of a company’s loss expense ratio
optimization goes to defending disputed claims. Every insurer can
relate to the typical horror story claim where the
passenger of an auto accident broke a finger and walked
away with a Rs 1,00,00,000 settlement. With litigation
optimization, insurers can use analytics to calculate a
litigation propensity score.

• Claims that involve an attorney often double the
settlement amount and significantly increase an insurer’s
expenses. Analytics can help determine which claims are
likely to result in litigation. Those claims can be assigned
to more senior adjusters who are likely to be able to
settle the claims sooner and for lower amounts.

Legal Analytics

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Legal Analytics • Legal Analytics provides data-driven insights
and trends in insurance case timing, resolutions,
damages, remedies, and findings.

• It includes cases alleging breach of an
insurance contract and cases seeking a
determination of the rights of the parties to an
insurance contract.

(Legal • With Legal Analytics, insurance lawyers can discover
Analytics what types of cases have actually been litigated,
strategy) Data- who represented the opposing parties, how long the
parties litigated, what findings the court or jury
Driven made, and what damages were awarded. It can also
Strategy provide a detailed litigation history of an opposing
party, allowing counsel to understand the party’s
strategies and litigation outcomes. Using that
information, both in-house counsel and their law
firms will be much better equipped to predict how
long a case may take, how much it will cost, what
damages might be expected, what strategy their
opponent might employ, what strategy is likely to be
successful, and many other important
considerations.

Finding the Litigation Trends

Legal data analytics provides unequaled With this data, insurers can better
insight into the behavior of courts, judges understand the litigation landscape and
and counsel in litigated insurance cases.
Analyzing data from actual cases allows develop successful legal strategie

insurers and their counsel improved 84
understanding of how insurance cases are
actually decided, how long cases last, and

what litigation strategies have been
successful.

Spotting • The use of class actions in litigation aggregates
trends hundreds or thousands of individual claims into
one lawsuit and can result in large expenses for
the insurer both in attorneys’ fees and
settlement costs for defendants. As a result,
understanding trends in class action filings in
insurance cases is crucial for accurate staffing
and budgeting.

Analyzing • From the class action automobile insurance
counsel filing data, we see that there is an increase in
the filing of these types of cases over the past
five year

Application of • The insurance industry has historically
Legal Analytics leveraged analytics to predict risk when writing
policies. Shouldn’t insurers and their counsel
apply those same kinds of analytics to
understand and predict their legal exposure
when claims become legal matters?

• With Legal Analytics, insurance lawyers can
discover what types of cases have actually been
litigated, who represented the opposing parties,
what findings the court or jury made, and what
damages were awarded.

Litigation • Selecting the lawyers
Management
• Approach the attorney selection process
systematically. Develop your own panel of
approved counsel, either on your own initiative
or with your insurer. If you are insured, seek
approval upfront at renewal or when coverage
is first placed for your ability to select counsel
or at least have a say in counsel selection

Litigation Analytics - Application

• Call the attorney to discuss.
• Write to the lawyer, itemizing your concerns.
• Request a written action plan for repairing the service lapses.
• Voice concerns to a managing partner.
• Hold a sit-down meeting with the lawyer(s).
• Reassign the case to another attorney within the firm.
• Reassign the case to another law firm.

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Reinsurance

Re-insurance • It is a process whereby one entity (the
reinsurer) takes on all or part of the risk covered
under a policy issued by an insurance company
in consideration of a premium payment. In
other words, it is a form of an insurance cover
for insurance companies.

Two types of • Treaty reinsurance agreements cover all or a
reinsurance portion of an insurer's risks, and they are
effective for a certain time period.

• Facultative coverage insures against a specific
risk factor. The underwriter would evaluate the
individual risk factor and write a policy
accordingly.

Re-insurance – • Protecting risk portfolios against natural
Application of disasters

Analytics • Integrating reinsurance solutions with existing
technology

• Complying with different countries regulations

• Scaling reinsurance solutions to meet future
needs

• Providing insurers with a viable alternative to
new risk management products

Data driven • Reinsurance Placement and Premium
Approach for Accounting
Reinsurance
• Data Entry, issue, placement and contract
binding, premium collection, accounting and
accounting reconciliation

• Claims Administration and Processing

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