Services

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

Fraud Detection: Health and Dental

What does predictive modeling offer for fraud detection?

Predictive models are particularly strong in fraud detection in their ability to expose emerging fraudulent practices. Because they can, with lightning speed, examine hundreds of thousands, or even millions, of submitted claims, they identify atypicality in an objective, open-minded fashion, unbound by past events.

How does our open-ended approach differ from rules-based?

By comparing each claim to every other claim, and each practitioner to every other practitioner, our pattern-detection technology goes beyond traditional rules-based approaches to fraud detection. This open-ended approach identifies virtually any type of atypical activity, rather than only those defined by a pre-determined set of rules.

What makes our fraud detection service unique?

We look at the bigger picture.

Our competition tends to focus on the individual claim level. We don’t. Why not? Because a focus on the individual claim level cannot take into account who the claimant or practitioner is, and the nature of their claim history.

We use individual claims as building blocks to grow a bigger, clearer picture – a picture of activity at the level of individual practitioners and individual claimants. We don’t consider each claim in isolation – we take into account the entire history of work for each practitioner, and for each patient, allowing our model to find and quantify more types of irregularities.

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."]