This white paper written by SAS examines how claims data can be used to improve combined ratios and gain competitive advantage.
For years insurance companies have concentrated on improving their operational systems, such as policy, claims and billing, often neglecting the vast amount of information that is held within these applications. Insurance is a data-rich industry, and the claims process is no exception, with a huge amount of information collected on each claim. Analytics can help insurance companies mine this data to improve their combined ratios and gain competitive advantage.
Claims is by far the biggest expense within a property and casualty (P&C) insurance company. Claims payouts and loss-adjustment expenses can account for up to 80 percent of an insurance company’s revenue. Thus the way an
insurance company manages the claims process is fundamental to its profits and long-term sustainability. Equally important is the role claims processing plays in customer satisfaction, renewal and retention. Unfortunately, the claims process is typically time-consuming and labor-intensive, involving multiple systems, outdated technology and distributed business units. The resulting inconsistent processes slow turnaround times and sap resources, leading to negative customer experiences.
Predictive insurance claims processing, or claims analytics, is the process to analyze the structured and unstructured data at all stages in the claims cycle to make the right decision, at the right time, for the right party. Rather than analyzing one case at a time – based on only the current information available - analytics gives you added perspective by allowing you to view this one claim in context – by comparing it with previous claims settlements in your database.