How does the Claim Analytics LTD claim reserving methodology differ from others?

Current reserving methodologies determine termination rates using simple statistics and only a limited set of claimant characteristics. The Claim Analytic’s approach to calculating LTD claim reserves is to determine claimant-specific termination rates.

The Claim Analytics approach is to apply advanced predictive modeling techniques to determine very precise, claimant-specific, termination rates based on all the key drivers of termination, such as: age, gender, elimination period, primary and secondary diagnoses (not just diagnostic category), occupation, industry, income, benefit level, change in definition date, geographic region and number of previous claims by the same claimant.

What are the advantages of the Claim Analytics LTD claim reserving methodology?

The Claim Analytics reserving methodology results in:

• More accurate reserves, that reflect each claimant’s true probability of termination
• An improved ability to measure profitability
• Less earnings volatility
• Better information for pricing and experience rating.

Fraud Detection Questions

How does the Claim Analytics fraud detection approach differ from current methodologies?

The most common approach to fraud detection is to apply rules-based technology. Claims are screened for procedures that match known types of fraud.

This approach has two weaknesses.

1. A rules-based approach cannot screen for new, unknown, types of fraudulent behavior.
2. A rules-based approach does not recognize that each practitioner/claimant has a history of claims and that some practitioners/claimants have demonstrated a greater or lesser degree of atypical activity.

The Claim Analytics approach to screening for fraud is to analyze all claims paid by the insurer over the past 12 or more months. We use advanced pattern-detection technology to:

1. Categorize each claim as typical or atypical,
2. Roll this information back up to the practitioner level, and thereby
3. Identify practitioners with atypical claims patterns.

Our approach allows the insurer to identify and concentrate on those practitioners with the most suspicious claims activities regardless of whether or not their behaviors match already known fraud schemes.

What are the data requirements to work with Claim Analytics?

Claim Analytics requires an extract of recent claims paid, typically 6 to 24 months of data. The primary data fields we require are: claim #, date service performed, procedure/product code, practitioner ID code, claimant ID#, age of claimant, type of work performed, total amount billed and total amount paid.