Chicago-based SPSS, a provider of predictive analytics software, has unveiled a new solution that will reportedly reduce insurance fraud, significantly improve the claims process and cut costs.
SPSS’ PredictiveClaims, an application based on real-time predictive analytic technology, integrates with existing claims-management systems to instantly determine which claims qualify for immediate approval and which are potentially fraudulent. “Fast-track claims handling” reportedly improves the efficiency of claim handling and customer service.
PredictiveClaims automatically analyzes all claims entering the system – from any channel – against risk profiles and external fraud databases. PredictiveClaims will either approve a claim for processing or flag it for investigation. The application can also generate “smart” questions that prompt a claim handler to ask customers for critical new information that can confirm the likelihood of fraud.
PredictiveClaims enables property and casualty insurers to:
— Approve legitimate insurance claims quickly to satisfy valuable customers and minimize loss adjustment expenses and claim handling costs;
— Identify potential fraud at an early stage with a high degree of accuracy – even with large claim volumes;
— Understand why certain claims are flagged as suspicious, so insurance Special Investigation Units (SIUs) know where to focus their investigations;
— Combine and analyze data from multiple internal and external sources, including federal and insurance industry databases;
— Integrate with existing claims management systems without extensive customization or lengthy implementation periods;
— Analyze textual claim data, such as accident descriptions, for other indicators of fraudulent behavior.
For more information about PredictiveClaims and other SPSS predictive analytic applications, visit www.spss.com.
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