Data (Chap 4 & 5 ) Flashcards
What are the four main ways to aggregate claim data?
Calendar Year, Accident Year, Policy (Underwriting) Year, and Report Year Aggregation.
What is the primary advantage of calendar year claim data?
It is readily available from financial systems and aligns with financial reporting requirements.
Why is accident year claim data commonly used for actuarial analysis?
It groups claims based on the date of the accident, making it useful for estimating unpaid claims and conducting ratemaking analyses.
What is a key disadvantage of using policy year claim data?
It has a significant time lag since claims may develop over multiple years before the dataset is complete.
How does report year aggregation differ from accident year aggregation?
Report year groups claims by when they were reported, whereas accident year groups claims by when the loss event occurred.
What are the two main types of loss adjustment expenses (LAE)?
- Allocated Loss Adjustment Expenses (ALAE)—directly tied to a specific claim
- Unallocated Loss Adjustment Expenses (ULAE)—general claim-handling costs not tied to specific claims.
What is salvage in the context of insurance claims?
The amount an insurer recovers by selling damaged property after paying a claim.
What is subrogation, and how does it benefit insurers?
It is the insurer’s legal right to seek reimbursement from a third party responsible for the loss, helping reduce overall claim costs.
Why do actuaries analyze claim data at alternative limits?
To remove distortions caused by large claims and ensure a more stable and credible analysis of claim trends.
What is the formula for calculating net claims?
Net Claims = Direct Claims + Assumed Claims - Ceded Claims.
What is the formula for calculating Gross claims?
Gross claims = Direct Claims + Assumed claims
What are the definitions of Direct claims, assumed claims and ceded claims when you are an insurer?
Direct claims = Claims coming from your policyholders
Assumed claims = Claims are coming from another insurer. You decided to be the reinsurer of this insurer and assume theirs claims based on an agreement
Ceded claims = Claims transferred to the reinsurer you have very larges losses over a certain threshold.
What is one major drawback of report year data aggregation?
It does not capture incurred but not reported (IBNR) claims, requiring additional analysis to estimate ultimate claims.
What are the 8 key factors actuaries must consider when using external data?
- Claim count definitions 📊 (e.g., one insurer may count ALAE-only claims, another may not)
- Claim management practices 🏢 (e.g., aggressive vs. conservative case reserves)
- Lines of business and policy coverage 📜 (e.g., Ontario auto insurance vs. Quebec’s system)
- Underwriting strategies 🎯 (e.g., targeting high-risk vs. preferred customers)
- Geographic mix 🗺️ (e.g., urban vs. rural claims frequency)
- Claims coding systems 💾 (e.g., insurers using different software affecting data categorization)
- Deductibles and policy limits 💰 (e.g., one insurer specializes in high-deductible policies, another in low-deductible ones)
- Legal precedents and regulatory environments ⚖️ (e.g., tort reform in one jurisdiction affecting claims severity trends while another jurisdiction remains unchanged)
How does a policy limit impact claim severity trends?
Lower policy limits cap the severity of individual claims, making annual severity trends less volatile.
What are the characteristics of a good exposure base?
- Proportional to expected claims – The base should reflect the risk exposure accurately.
- Easy to measure & already recorded – Should be readily available and not require extra tracking.
- Not subject to manipulation – Should be objective and verifiable.
- Accurately reflects differences in risk – Should distinguish between low- and high-risk policyholders.
- Industry consistency (Werner & Modlin) – Should align with standard industry exposure bases.
What is the difference between written, in-force, and earned exposure?
Written Exposure 📜
Total exposure units for all policies issued during a time period (e.g., a calendar year).
Example: A one-year policy written in CY1 is fully counted in CY1, even if it extends into CY2.
In-Force Exposure ⏳
Total exposure units currently active at a given date.
Example: On July 1, a six-month policy issued on April 1 is still in force, but one issued on January 1 is not.
Earned Exposure ✅
Portion of written exposures that has been exposed to risk during a given time period.
Example: A one-year policy covering 500 vehicle-years has 250 earned exposures at the 6-month mark.
🔹 Key Difference:
Written exposure refers to what was sold, In-force exposure refers to what is active, Earned exposure refers to what was used for coverage.
What is the trade-off between homogeneity and credibility in actuarial data analysis?
Homogeneity 🎯
Data should be grouped so that claims share similar characteristics (e.g., claim frequency, severity, development). More homogeneous data leads to better predictive patterns. Example: Separating auto liability BI and PD claims because they develop differently.
Credibility 📊
Data must have sufficient volume to produce statistically reliable results. More data increases predictive stability but may reduce homogeneity. Example: Small datasets may need to be combined to improve credibility, even if it introduces some heterogeneity.
What are 3 key aspects of data quality review for actuaries?
- Reconciliation: Tying actuarial data to financial statements.
2. Validation: Checking for missing, duplicate, or unreasonable values.
3. Documentation: Recording sources, assumptions, and modifications.
“Garbage in = Garbage out” – poor-quality data leads to inaccurate actuarial work.
What are the two major categories of expense data?
- General expenses: Rent, utilities, senior management salaries.
- Underwriting expenses: Commissions, marketing, underwriting staff salaries, premium taxes.
How do investment strategies affect actuarial reserving and pricing?
Investment returns determine discount rates for liabilities, influencing reserve adequacy and pricing models in compliance with actuarial standards.
Why is it important for actuaries to review an insurer’s management, ownership, and business plans?
Changes in management, ownership, or business strategy can impact claims handling, reserving philosophy, underwriting priorities, and expense structures, affecting actuarial projections.
How can changes in IT systems impact actuarial projections?
IT changes can alter claim reporting and settlement speeds, affecting ultimate claims projections, reserving assumptions, and pricing accuracy.
Why do actuaries need to understand an insurer’s accounting practices?
Accounting methods impact claims data aggregation, regulatory reporting, and financial assessments, influencing reserve calculations and pricing decisions.
How do claim handling practices affect actuarial analysis?
Differences in case reserving, claims settlement speed, and claims philosophy influence future claims development and reserve adequacy.
What underwriting factors should actuaries monitor for potential impacts on claim trends?
Shifts in exposure types, deductible levels, policy limits, and risk classifications can impact loss trends and required reserves.
Why must actuaries understand catastrophe exposure?
Catastrophe claims significantly impact pricing models, reserving needs, and reinsurance strategies, requiring actuarial adjustments.
How does historical rate change information assist actuaries?
Historical rate changes help adjust premium data, assess rate adequacy, and refine claims projections for accurate pricing.
Why must actuaries analyze both historical and future reinsurance programs?
Reinsurance impacts net claim liabilities, reserve requirements, and pricing strategies, affecting the insurer’s risk exposure.
What is a residual market mechanism, and why is it important in insurance? (not in textbook)
A residual market mechanism provides insurance coverage to individuals or businesses unable to obtain it in the voluntary market. These mechanisms are often mandatory in certain jurisdictions, and insurers share profits and losses from these pools.
How does participation in a residual market mechanism impact actuarial work? (not in textbook)
Residual market mechanisms affect pricing, reserving, and financial projections because:
- Loss-sharing: Insurers must absorb a portion of residual market losses, impacting profitability.
- Uncertainty in claims: High-risk policies can lead to volatile loss experience, requiring actuarial adjustments.
3. Regulatory requirements: Actuaries must ensure compliance with jurisdictional rules for participation and rate setting.
- Impact on private market pricing: Higher assessments from residual market losses may lead insurers to adjust voluntary market rates.
2 examples of residual market mechanisms in Canada, and how do they impact insurers?
Facility Association (FA):
Provides auto insurance to high-risk drivers who cannot obtain coverage in the voluntary market. All private auto insurers share the profits and losses from policies assigned to the FA. Actuaries must consider the impact of FA assessments on insurers’ pricing and reserving.
Risk Sharing Pools (RSPs):
Used by insurers to transfer high-risk policies while still retaining the customer relationship. Common in Ontario, Alberta, New Brunswick, Nova Scotia, and Prince Edward Island. Helps smooth risk, but actuaries must adjust pricing models to account for ceded risks and expected loss-sharing.
What are some common data issues that data reconciliation can identify?
- Missing or duplicate records
- Incorrect or missing values
- Incorrectly formatted data
- Broken relationships across tables or systems
What are some examples of actuarial data reconciliation checks?
- Premiums, claims, and expenses should match accounting records.
- Total reported claims = Paid claims + Case estimates.
- Reserving data should reconcile with pricing data, with differences documented
What should an actuarial data reconciliation report include?
- Data validation steps, adjustments, and modifications
- Any data deficiencies and their impact on actuarial work
- The intended audience for actuarial findings
