Chartis, a world leading property-casualty and general insurance organisation, recently turned to analytics from SAS, the leader in business analytics software and services, to estimate the risk of future losses and help underwriters assess insurance risk.
Chartis has also used SAS analytics to reconcile claim payments and estimate the need for bad-debt reserve funds related to premium receivables.
John Savage, VP of Chartis' Strategic Risk Analysis Group, says, "Our use of 16 underwriting and finance predictive models has helped prevent millions of dollars of potential future losses on an insurance and reinsurance portfolio of approximately $13-billion."
Chartis' Strategic Risk Analysis Group, under the direction of Savage and assistant VP, David Lee, has focused its use of SAS analytics on three specific areas: executive liability insurance, catastrophe modelling and financial accounting.
"In an 18-month period, we used a web-based modelling tool available from SAS to target $14-million in new executive liability business, representing 100% growth in that segment. In addition, the modelling tool enabled us to avoid a potential loss of $75-million from certain executive liability accounts over the course of a year," says Lee.
The Strategic Risk Analysis Group also used the analytics to tackle the difficult task of catastrophe planning. When a calamity does strike, Chartis must quickly respond to the needs of affected clients.
To ensure that funds are accurately accounted for in the company general ledger, the analytics team built an automated reconciliation tool using SAS. As a result, the group was able to reduce the amount of reserve funds needed to cover any discrepancies related to un-reconciled funds.
Finally, in a financial accounting project, the Strategic Risk Analysis Group used SAS analytics to build probability-based exposure models to estimate bad-debt reserve for uncollected premium receivables, based on open balances across multiple lines of business. The methodology and algorithms comply with audit requirements, and provide stable exposure estimates each quarter.
"Modelling with SAS is highly scalable," says Savage.