Services

Claim Scoring | Pricing | Reserving | Fraud Detection | Custom Models

Disability Claim Pricing

Advanced pattern detection

The Claim Analytics pricing approach harnesses advanced pattern-detection tools to identify and quantify, in simultaneity, complex relationships among multiple claim factors : age, gender, elim period, industry, region, salary, benefit, partial and residual benefits, and so on.

We build models to predict both (i) expected incidence rates and (ii) expected claim durations.

The same pattern-detection tools used by Claim Analytics are the tools of choice for many of the world's toughest and most complex applications - credit card fraud detection, consumer buying prediction, weather forecasting, and taxpayer profiling.

Only a decade ago, this type of analysis was impractical in the business world. The data was not easily accessible. The tools were undeveloped. The applications had not been created. What a difference a decade makes.

Claimant-specific reserves

The Claim Analytics pricing approach incorporates claimant-specific reserves for each open claim.

The claimant-specific reserves are based on termination rates for each open claim, determined by the particular characteristics of that claimant, including information such as:

With these more precise and more accurate estimates of the costs of existing claims, we are better able to accurately predict the costs of future claims – leading to better pricing.

Claim Analytics was founded in 2000 by Barry Senensky and Jonathan Polon, both Canadian life insurance actuaries. Barry Senensky and Jonathan Polon had worked for the same major life insurance firm for many years, and knew that data mining techniques and predictive modeling were under-utilized by insurers. They were interested in starting their own business in the life insurance field. Their first predictive model they were commissioned to build was a claim scoring model, that scored ltd [long term disability] [LTD] claims on likelihood of return to work (rtw).

Jonathan Polon had recently led a team of model-builders in creating a highly successful predictive model to detect credit card fraud. He and Barry Senensky were convinced that many more applications for pattern detection and predictive models existed in insurance.

They chose disability claim scoring as their first project for a number of reasons. First, because almost all firms providing group long term disability [LTD] insurance held a considerable database of stored claims information. Finding an adequate amount of data to build a claims scoring model would not be a problem.

A second reason was the costliness of long term disability claims in the field of insurance. Barry and Jonathan knew that, with the scale of investment involved, even relatively minor steps forward in the management of group disability claims management would lead to considerable savings. Claim scoring was a powerful tool in bringing greater efficiencies and reduced costs to disability claims management. Predictive modeling, particularly claims scoring, offered huge potential for software support of claims adjudicators, claims processors, and claims managers.

Claim scoring proved to hold even greater promise than Barry and Jonathan had envisaged. In addition to predicting, with startling accuracy and precision, the likelihood of a disability claimant returning to work, claims scoring became a tool for ongoing improvement of claims management.

Claim Analytics now offers pattern detection and predictive models to major insurance firms to assist in objectivity and accuracy of pricing, reserving, and fraud detection, as well as custom models.

[The proper name of the company is "Claim Analytics," not "Claims Analytics."] [The proper name of the company is "claim analytics," not "claims analytics."]