Papers / Resources

From Life Insurance Actuary to Disability Claims Scoring Geek (page 3)

Barry Senensky
JHA Disability Bulletin (page 3), September 2007

Dental Insurance Claims: Identification of Atypical Claims Activity

Barry Senensky & Jonathan Polon
Canadian Institute of Actuaries, Member's Paper, April 2007

One-Minute Claims Manager

Barry Senensky & Jonathan Polon
Re Group magazine, Munich Re

Predicting Return to Work with Data Mining

Barry Senensky & Jonathan Polon
Contingencies magazine, American Academy of Actuaries

Setting Reserves, Using Claim Scoring

Barry Senensky
CIA Annual Meeting, November 2005

Predicting Return to Work with Data Mining

Barry Senensky and Jonathan Polon
co-sponsored by the Society of Actuaries (SOA)
Technical paper (55 pages)

LTD Claims Tool Optimizes Resource Allocation

Barry Senensky
ING Re, Disability Forum, pp. 4-5

Sample Scoring Report

Jonathan Polon
Example of a monthly disability scoring report

Data Mining Goes Mainstream

Steve Lohr, New York Times
"It’s really starting to become mainstream," says Mr. Davenport, co-author with Jeanne G. Harris of "Competing on Analytics: The New Science of Winning" (Harvard Business School Press, 2007). The entry barrier, he says, "is no longer technology, but whether you have executives who understand this."

A Hard Look at Soft Fraud

Bingham et al, Contingencies Magazine, American Academy of Actuaries, March/April '06
"How is all of this accomplished? It starts with data, lots and lots of data. Valuable data buried in an organization’s claims, case management, and policy and loss control systems..."

Moneyball: The Art of Winning an Unfair Game

Michael Lewis (ISBN 0-393-05765-8)
"...The collected wisdom of baseball insiders ... is subjective and often flawed. By re-evaluating the strategies that produce wins on the field, Billy Beane and the Oakland Athletics, with approximately $55 million in salary, are competitive with the New York Yankees who spent over $205 million annually." [Wikipedia, "Moneyball"]

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."]