September 02, 2008, 3:38 PM — Rather unpredictably, hail pounds U.S. crops and property to the tune of about US$1 billion in damage each year, according to the National Weather Service. These icy agents of chaos account for just one of many catastrophic scenarios that Guy Carpenter & Co. must simulate and model, if the company is to protect its policyholders and stay one step ahead of disasters.
As a major reinsurance firm, New York-based Guy Carpenter sells policies to other insurance companies. Most of Guy Carpenter's clients are looking for added protection from those disasters that are either difficult to predict or tend to be the most damaging.
It's risky business, indeed, and the company's strength depends on its ability to calculate not only its own risks, but also those facing its many clients. To best serve the array of parties involved and to make the best projections, Guy Carpenter has blended business intelligence and Web 2.0 technologies and layered the resulting application with advanced mapping capabilities.
At a Glance
A risk and reinsurance specialist with 50 offices worldwide, this firm has business expertise in areas such as aviation; construction and engineering; life, accident and health; marine; political risk and trade credit; professional liability; property; terrorism; and workers' compensation.
-- Project champion: Ryan Ogaard, global leader
-- Project cost and payback: Because the company provides the i-aXs service as a value-add for clients, it doesn't license it or quantify its return on investment.
-- Size of the IT group: About 25 people
"As a global reinsurance broker, our transactions must include services such as catastrophe modeling, portfolio management and exposure management. All of these services generate a voluminous amount of data that has geographic context," says Shajy Mathai, managing director.
To meet these challenges, Guy Carpenter has put in place a system dubbed i-aXs, which is infused with location intelligence and online risk management capabilities, among others. The system relies on MicroStrategy Inc.'s MicroStrategy 8 BI software. Mathai recalls trying many combinations before settling on the i-aXs approach. "We investigated products until we found a combination of a database, BI, mapping, and [extract, transform and load] capabilities that had sufficient compatibility from which we could start building a product," he says.
Building a collaboration framework that can handle this heavy load and a range of users costs Guy Carpenter more than $5 million a year in system investments. However, these resources are well spent, Mathai says, because they enable the company to prepare itself and its clients for potentially devastating losses, particularly from bad weather. "Our clients' data is intersected with weather data, so we can identify which policies may have been affected by a hail event within minutes of the event," he says.
When deciding how to structure a system that makes use of BI and Web 2.0 tools, it's important to consider the data and try different methods of organizing access, says Wayne Eckerson, director of research at The Data Warehousing Institute in Renton, Wash.
"This is kind of like the old 'Do we data-warehouse or do we federate?' argument that arises every few years, except this time, people are calling this choice Web 2.0," he says.
"The argument always comes down to this: If you use the data intensively, and there is a lot of it, or you need to apply complex transformations to integrate or clean it and need high performance, then warehouse it," Eckerson advises. "If you only need data around the edges of your application and there isn't much of it, and what's there is of good quality and is easy to link to and integrate, then federate."
In fact, Guy Carpenter gives its customers independent access to i-aXs via a home page set up for each of them. From this page, the customers can use the company's DataMinerix, a query and reporting system customized to specific end users. This module contains the mapping component and is layered with other capabilities, such as a policy-ranking tool that allows insurance agencies to identify the riskiest policies in their portfolios.
"Since we have different consumers of this voluminous data -- including clients, brokers, rating agencies, investors and markets -- we needed to develop a platform on which Web-based collaboration is straightforward and easy," Mathai says.
