Claim Analytics, Predictive Modeling, Predictive, Model, predictive model, Insurance, Benchmarking, Scoring, Claims, Data Mining, Patter Detection, Acturial, Actuary, Long Term Disability, disability, claims data model sample, fraud detection, probablility
Greater Reserving Accuracy
Claimant-specific termination rates deliver appropriate disabled life reserves for each claim. This ends the ‘averages of averages’ problem of traditional methodologies.
Traditional methodologies are constrained by their inability to manage more than a very limited set of claim differentiators: at best age, gender, elim period and one or two others.
The Claim Analytics approach is to apply advanced predictive modeling techniques to determine precise, claimant-specific termination rates. These rates are based on key drivers such as:
- elimination period
- primary and secondary diagnoses (not just diagnostic category)
- benefit level
- change in definition date
- geographic region and
- number of previous claims by the same claimant.
This methodology results in reserves for each claimant that reflect that claimant’s true probability of terminating. Our approach provides:
- an improved measure of profitability
- less earnings volatility
- better information for pricing and experience rating.
Evaluating IF Blocks of DI