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Nosy Data

By Steve Sullivan

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ACCORDING TO AN ASSOCIATED PRESS report last year, a man in Jefferson Parish, La., was arrested for insurance fraud related to Hurricane Katrina. Jose S., an absentee landlord in Florida, persuaded one of his Louisiana tenants to go up on the roof with a shovel and tear off the shingles, all the way down to the wood. Wind damage. Submit the claim. Collect the money.

He knew it would work because he’d done exactly the same thing back in 2001 and 2004. And the insurance company paid off both times. This time, however, somebody squealed, and cops showed up instead of the insurance adjustor. Who was probably very busy elsewhere, dealing with more legitimate claims.

What Jose did was clearly criminal. The insurance company shouldn’t have had to depend on a whistle-blower to catch him. The company should have had a database with a built-in red flag that said: What? Three times in five years? Same building? Same claim? Tilt!

But let’s say Jose’s claim was legitimate. Let’s say the wind really did rip off his roof, and he’s perfectly within his rights to file a claim and expect it to be paid. But wait a minute. Jose has a better idea. A couple of weeks ago, his brother-in-law dropped a ladder on the top of his car (idiot!) and really tore up that roof, too. Big dent. Broken glass. Scraped paint. A major eyesore and inconvenience, but the repair bill probably wouldn’t exceed his $1,000 deductible, so he let it go.

But now, he’s filling out this claim form for the wind damage on his home. Why not just add the car while he’s at it? Who’s to say the wind didn’t blow the ladder into it? Or something else? And it’ll all be covered under his homeowner’s policy. Besides, the insurance company can afford it.

Not criminal, maybe, though just as fraudulent. But there’s no anonymous tipster picking up the phone to bail out the insurance company this time. Probably because the anonymous tipster is filling out his own slightly off-color claim form. Because, you know, everybody does it.

It’s a situation addressed by two particularly timely articles in this issue. Kevin Bingham, John Lucker, and Mo Masud (“A Hard Look at Soft Fraud,” Page 28) look at how new data-mining techniques help insurers detect shady behavior that may stay just shy of larceny. If Jose tries the car-roof trick just once, chances are he’ll get away with it. But if behavior like that starts to become a habit—no way, Jose. Somebody is watching, even if it’s not your nosy neighbor.

In “New Catastrophe Models for Hard Times,” Page 32, Patricia Grossi and Howard Kunreuther describe the factors insurers use when modeling for natural disasters and acts of terrorism. One of those factors is moral hazard. Can a model used for pricing adequately account for the possibility of rampant fraud in the wake of an event like Hurricane Katrina? What about soft fraud?

Maybe they’ll need to wait until the next generation of models.

It all goes back to that delicate line insurance companies always try to walk but that is particularly tricky after a catastrophe like Katrina— the line between being a business and being a benefactor. Given the billions of dollars in legitimate claims insurers will be paying out after the 2005 hurricane season, they can hardly afford to be too cavalier about paying fraudulent ones. Yet questioning and investigating every claim would be a public-relations nightmare.

Somewhere in the data and the models lies the answer.

Thanks for your help.


Contingencies (ISSN 1048-9851) is published by the American Academy of Actuaries, 1100 17th St. NW, 7th floor, Washington, DC 20036. The basic annual subscription rate is included in Academy dues. The nonmember rate is $24. Periodicals postage paid at Washington, DC, and at additional mailing offices. BPA circulation audited.

This article may not be reproduced in whole or in part without written permission of the publisher. Opinions expressed in signed articles are those of the author and do not necessarily reflect official policy of the American Academy of Actuaries.

March/April 2006

A Hard Look at Soft Fraud

New Catastrophe Models for Hard Times

The Value of Human Life

In the Eye of the Beholder

Inside Track:
Nosy Data


Making Good Sausage

Up To Code:
Living With Precept 10

Policy Briefing:
Where Policy Meets Politics

Health-Related Quality of Life

The Future of Historic Studies

Statistical Miscellany:
2005 A Record Year for Casualty Claims

Peculiar Star Position

Second-Order Effects

2006 Directory of Actuarial Software

Past Issues

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