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  1. 4 phases approach for reserving
    • 1. Exploratory analysis of the data
    • 2. Apply appropriate techniques for estimating unpaid claims
    • 3. Evaluate conflicting results of the various methods
    • 4. Monitor projections of clm dvpmt over subsequent calendar periods
  2. Sources of data
    • Large insr often solely rely on internal data (may use ext for new lob/terr)
    • Small insr have small volume w. less credibility
    • Examples of external sources: ISO, NCCI, RAA, Best
  3. Uses of external data
    • Tail devpmt factors
    • Trend rates
    • ECR
    • Evaluating and reconciling results of various methods
  4. Some potential problems with external data may be due to differences relating to:
    List 3 (out of 6)
    • Insurance products
    • Case O/S & Settlement practices
    • Insurers' operations
    • Coding
    • Geographical areas
    • Mix of business & product types
  5. Key characteristics when evaluating credibility of data in each group
    • Consistency of coverage triggered by claims
    • Volume of claim counts
    • Ability to develop appropriate Case O/S estimate
    • Settlement or payment patterns
    • Likelihood of claim reopening
    • Severity
  6. What is the acutary's goal when creating groups in data?
    Divide data into sufficiently homogeneous groups without compromising credibility of data

    * Increased homogeneity or increasing volume of data tends to increase credibility

    Conflicting goals: increasing homogenity decreases volume and credibility
  7. Determination of size criteria for large clms (the kind that can distort your data)
    • Nbr of claims over treshold
    • Size of claim relative to policy limits
    • Size of claim relative to reinsurance limits
    • Credibility of internal data regarding large claims
    • Availability of relative external data
  8. Verification of data to ensure reliable and sufficient
    • Consistency w/ financial statement data
    • Consistency w/ prior data
    • Data reasonableness
    • Data definitions
  9. Aggregation by CY
    • Transactional data
    • CY rpd = pd + beg - end
    • Primary used for aggregation of exposures
    • Adv: no dvpmt, readiliy available
    • Dis: inability to address issue of dvpmt
  10. Aggregation by AY
    • CY exposures often used w. AY claims
    • Adv: grouping easy to achieve and understand; shorter time frame than PY; track economic or regulatory chg
    • Dis: mismatch btwn clms and exposures; can mask chg in retention
  11. Aggregation by PY
    • Clms extend over a 24 mths period
    • Adv: true match btwn clms and exposures; track uw or pricing chg
    • Dis: extended time frame; difficult to isolate and understand affect of a single large event
  12. Aggregation by RY
    • Claims-made coverages is dependent on rpt date
    • Used to estimate value of know clms
    • Adv: nbr clms is fixed
    • Dis: only measures dvpmt on know clms and NOT IBNR
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