Reinsuring the Future

Alea Alternative Risk gains a consolidated view of business by building an operational data store.

Multiple data sources and manual analysis left heads spinning at Alea Alternative Risk (AAR), a member of Alea Group Holdings (Bermuda; $1.4 million in assets). In 2003, Rocky Hill, Conn.-based AAR - a division of Alea North America Insurance Co. that provides primary, excess and reinsurance for alternative risk, and traditional insurance to small and midsize business clients - had six core systems that received 20 disparate data feeds from reinsurance clients, according to Kevin Costello, AAR's COO. As a result, workers spent an enormous amount of time collecting and reconciling data in Microsoft Excel spreadsheets, he relates.

Costello adds that it was imperative for AAR to gain a consolidated view of its business to enable executives to identify trends and opportunities more quickly. "It was becoming self-evident we were going to have to pull together all our sources of data if we wanted to grow as a business," he says. "There was just too much manpower involved in data gathering and data analysis."

"We needed a place to aggregate the transactional data and compile it in a meaningful way and in a timely manner," adds James Kilmartin, AAR's VP of IT, who notes that the insurer wanted to deploy a solution quickly. "We had to move fast and recognized that our legacy system was compatible with the Microsoft platform," he adds. "We could run any of the various transaction and reinsurance systems that we use - like Fiserv's Freedom Reinsurance - with Microsoft's SQL Server."

So, Kilmartin and Costello collaborated with Redmond, Wash.-based Microsoft Services' Quickstart business intelligence consulting group to identify a solution. Alea decided to build an operational data store based on SQL Server 2000 utilizing SQL Server Analysis Services software, which provides online analytical processing, and SQL Server Reporting Services software, which generates reports and delivers data in Excel.

"The SQL Server Analysis and Reporting Systems provide an integrated system that manages transactional data, compiles it in a meaningful way and in a timely manner for financial closings, and consolidates information and alleviates manual needs," explains Costello. The solution allows employees to query the operational data store, which automatically generates report dashboards that can be viewed online and creates an audit trail, he continues.

We Can Build It

But, with an IT staff of only five employees, Alea would be challenged to build the operational data store. So Microsoft Services recommended that AAR tap GRT, a Stamford, Conn.-based solutions provider of data integration and data warehousing. In a combined effort, GRT and AAR began implementing the $1.3 million solution in June 2004. AAR worked closely with GRT throughout implementation, reviewing the development and identifying concerns. "By making design changes early, we avoided problems later on," says Kilmartin.

"With AAR, we wanted to cut down the time spent for data collection by 70 percent," says GRT president Viktor Litvinov. The system was completed this past March.

"It's convenient for everyone," notes Kilmartin. "The data takes five to six hours to run overnight instead of five to six days of plugging data into Excel." This allows business lines to devote more time to settling claims and servicing customers, and the improved analysis allows management to move business forward more proactively, he adds.

"We have built a management information framework," says Costello. "We've taken a more holistic view to manage the business itself and built strong roots for the future."

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Case Study Profile

Company: Alea Alternative Risk (Rocky Hill, Conn.), a member of Alea Group Holdings (Bermuda; $1.4 million in assets).

Lines of Business: Reinsurance and alternative risk insurance.

Vendor/Technology: Microsoft Services (Redmond, Wash.) data store and SQL Server Analysis and Reporting Systems software; GRT (Stamford, Conn.) services.

Challenge: Streamline data collection in a centralized operational data store to improve data analysis.

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