- TIA EXAM 5 - WERNER CH 03

• 1. Analyze adequacy of existing rates
• Generally use internal or industry historical data to project future costs
• Company should collect and maintain relevant and consistent historical data
• 2. Pricing new products

• Must be aware of the impact on the analysis
• Should examine how sensitive the results are to various assumptions
• Select data that minimizes distortions in results

• 1. Appropriateness for the intended purpose of the analysis
• 2. Reasonableness and comprehensiveness of the data elements

Use Policy database and Claims database

Need to link policy exposure and premium with corresponding claims and losses

• 1. May not even be specific to one line of business
• 2. Expenses that fall into this category
• *Underwriting expenses - incurred in acquisition and servicing of policies
• *ULAE

• 1. Accurately match losses and premium for the policy
• 2. Use the most recent data available
• 3. Minimize data collection and retrieval costs

• 1.Type: Transactional data
• CY = PAID + END CASE - BEG CASE

• 2.Primary use:
• Aggregation of exposures
• May be used for LOBs or coverages in which losses are reported and settled quickly

• 3.Advantages:
• No future development - the value remains fixed and doesn't change over timeReadily available - most financial reporting on a calendar year basis

• 4. Disadvantages:
• Mismatch in timing between premium & losses
• *Earned premium comes from policies in force during the year
• *Losses may come from payments or reserve changes on policies from previous years

• 1. Type: Losses grouped according to date of occurrence, regardless of when pol written or claim reported.
• AY = PAID + ENDING CASE

Will develop over successive CYs with more information and as new claims are reported

Most common grouping of claims data for the actuarial analysis of unpaid claims

2. Advantages:Better match of premium and losses than calendar year

• 3. Disadvantages:
• Must estimate future development on claims
• May select valuation date several months after end of year to improve estimate because allows some time for loss emergence

• 1. Type: Group premiums and losses by year in which policy was written
• Losses arising from a PY can extend over a 24-month calendar period

2. Advantages: True match between claims and exposures

3. Disadvantages: Extended time frame - data takes longer to develop

• 1. Type: Group claims according to date of report to the insurer
• Claims-made coverage is dependent on the report date

2. Advantages: Number of claims is fixed at close of the year

• Overall analysis
• Reviewing the adequacy of the overall rate level
• Data can be highly summarized

• Classication analysis
• Data must be at a more detailed level

• Must work with available data and use actuarial judgment to deal with data deficiencies
• E.g., if missing earned premium by territory, may use in force premium by territory to estimate

• Statistical Plans
• Other Aggregated Industry Data
• Competitor Rate Filings/Manuals
• Other Third-Party Data

Regulators often require insurers to file statistical data, usually in a summary form

• In order to comply with various state requirements, industry service organizations formed to:
• 1.Aggregate & Collect data
• 2.Analyze data and make available to participating companies
• 3.Collect data at transactional level

Many insurers voluntarily report data to various organizations

To be used by insurance industry, regulators, public policy makers or general public

• e.g. Fast Track Monitoring System
• * Used by insurers and regulators to analyze loss trends
• Highway Loss Data Institute
• * Loss information by type of car to member companies and public policy makers

Rate filings available to the public when required to be filled with regulators

• Must take great care when using competitive information:
• * each company has different types of:
• insureds
• goals
• expenses
• operating procedures

May not be useful info if differences are material

• 1. Economic Data
• a. used for trends in expenses, premiums, losses
• b. Consumer Price Index for medical cost and construction cost indices

• 2. Geo-demographic data
• a. Census data - population density to predict freq
• b. Weather indices, avg miles driven

• 3. Other third-party data
• a. Credit score, DMV records, fire station distance